Not in the top 1,000 by GitHub stars
Jakeschincariol/hunter-skill
11 Claude skills that land you the job: listings, resume, research, LinkedIn and warm intros to the hiring manager.
History
Review weekly changes to SKILL.md before updating. Dates mark the weeks in which we observed each version; checks apply to the recorded commits.
View weekly changes
skills/hunter-apply/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → f40d6a94b612
No earlier version was observed for comparison.
skills/hunter-connector/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → f40d6a94b612
No earlier version was observed for comparison.
skills/hunter-cover/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → f40d6a94b612
No earlier version was observed for comparison.
skills/hunter-followup/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → f40d6a94b612
No earlier version was observed for comparison.
skills/hunter-interview/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → f40d6a94b612
No earlier version was observed for comparison.
skills/hunter-linkedin/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → f40d6a94b612
No earlier version was observed for comparison.
skills/hunter-research/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → f40d6a94b612
No earlier version was observed for comparison.
skills/hunter-resume/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → f40d6a94b612
No earlier version was observed for comparison.
skills/hunter-salary/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → f40d6a94b612
No earlier version was observed for comparison.
skills/hunter-scout/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → f40d6a94b612
No earlier version was observed for comparison.
skills/hunter-tracker/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → f40d6a94b612
No earlier version was observed for comparison.
View on GitHubAdd to my skills
Verified commit f40d6a94b61283785b20e47a237608d5b1976b1c
git clone --filter=blob:none --no-checkout https://github.com/Jakeschincariol/hunter-skill.git .hotskills/jakeschincariol-hunter-skill && git -C .hotskills/jakeschincariol-hunter-skill checkout --detach f40d6a94b61283785b20e47a237608d5b1976b1c
# Copy each selected skill directory from .hotskills/jakeschincariol-hunter-skill into .claude/skills/<skill-name>/81GitHub stars
38stars in 7 days
11skills in this repository
1commit in 30 days
Heat
7.2- Star growth, last 7 daysNot available
- Star growth, last 24 hoursNot available
- Rank movement, last 7 daysNot available
- Commits, last 30 daysNot available
- Accounts asking about it through MCP, last 7 daysNot available
- Hot votes this weekNot available
Heat never changes the order by stars, and sponsorship never touches it. How heat works
Stars over time
30-day change not yet available: not enough history.
Individual skills in this repository
- hunter-apply
skills/hunter-apply - hunter-connector
skills/hunter-connector - hunter-cover
skills/hunter-cover - hunter-followup
skills/hunter-followup - hunter-interview
skills/hunter-interview - hunter-linkedin
skills/hunter-linkedin - hunter-research
skills/hunter-research - hunter-resume
skills/hunter-resume - hunter-salary
skills/hunter-salary - hunter-scout
skills/hunter-scout - hunter-tracker
skills/hunter-tracker
How it works
hunter-apply
When your agent uses it
The Application Assistant. Fills the user's job applications from their own saved answers and leaves every one as a draft for them to review and submit: maps each form question to hunter/profile.json (name, links, work authorization, sponsorship, start date, relocation and more), flags what only the user can answer (salary, voluntary self-identification, anything not in the profile), runs a per-application checklist (tailored resume, cover letter, research, warm intro), fills the form in the user's browser with Claude in Chrome and stops before Submit. Use when the user says "apply to this job", "fill out the application", "fill the Greenhouse form", "prep my application", "what's left before I apply", or "save it as a draft".
What it loads into context
Frontmatter
193 tokensAlways available to the agent
SKILL.md
1,551 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What it reads and writes
- The rules
- Steps
- Example: the reel
- What it never does
- Consent
Scripts executed separately
answers.py
Files
../../.claude-plugin/marketplace.jsonasset732 bytes../../.claude-plugin/plugin.jsonasset563 bytesSKILL.mdcore6,379 bytesSKILL.mdcore6,379 bytesSKILL.mdcore6,379 bytesSKILL.mdcore6,379 bytesSKILL.mdcore6,379 bytesSKILL.mdcore6,379 bytesSKILL.mdcore6,379 bytesanswers.pyruns20,872 bytesprofile.example.jsonasset2,339 bytes
Read SKILL.md
# hunter-apply
Most of an application is the same twenty questions. This skill answers
those from the user's own saved answers, asks about the rest, fills the form
in their browser, and stops with the Submit button untouched. The user reads
it, fixes anything, and submits it themselves.
What the user typed after `/hunter-apply`: `$ARGUMENTS`
Tool in this folder (standard library Python; it never submits anything):
```bash
python3 "${CLAUDE_SKILL_DIR}/answers.py" --help
```hunter-connector
When your agent uses it
The Connector. Finds the hiring manager for a role (from the posting, the team page and a LinkedIn search the user runs), researches them from public professional information only, works out their likely work email with a confidence, drafts a personalized email and a LinkedIn note, and keeps the conversation going with polite, rule-bound follow-ups until a coffee chat is booked on the user's calendar. Nothing goes out without the user's yes for that exact message, every message is sent from the user's own accounts, and the stop rules end a thread at the first no. Use when the user says "find the hiring manager", "reach out to the team", "get me a warm intro", "email the manager", "set up a coffee chat", "who is hiring for this role", or "follow up with Maya".
What it loads into context
Frontmatter
205 tokensAlways available to the agent
SKILL.md
2,423 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What it reads and writes
- Step 1: find the hiring manager
- Step 2: research them, professionally
- Step 3: the email address
- Step 4: draft, then the user's yes
- Step 5: it goes out from the user's own accounts
- Step 6: follow up, within the stop rules
- Step 7: book the coffee chat
- Example: the reel
- What it never does
- Consent
Scripts executed separately
emails.pyoutreach.py
Files
SKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesSKILL.mdcore9,900 bytesemails.pyruns10,529 bytesoutreach.pyruns29,495 bytes
Read SKILL.md
# hunter-connector
The fastest way into a company is a conversation with the person hiring.
This skill finds that person, learns what they have said and built in
public, writes to them like a peer, and keeps the thread alive just long
enough to land a 20 minute coffee chat, so the user has an in before the
application is even sent. It never spams, never guesses at anyone's private
life, and never sends a word the user has not approved.
What the user typed after `/hunter-connector`: `$ARGUMENTS`
Tools in this folder (standard library Python; neither one sends anything):
```bashhunter-cover
When your agent uses it
Cover Letter Writer. Writes a specific cover letter for every application: addressed to the hiring manager or the team, opening with something real about the company or the team from the research brief, one paragraph of proof from the tailored resume (a result with a number), why this team, and a clear ask, in 150 to 400 words. Then checks it with cover.py: company, role and team named, at least one concrete proof point, no banned cliches, no leftover placeholders, no other company's name pasted in, every number traceable to the resume, reading level. Use when the user says "write a cover letter", "cover letter for Google", "do I need a cover letter", "make this letter better", "check my cover letter", or after /hunter-resume.
What it loads into context
Frontmatter
194 tokensAlways available to the agent
SKILL.md
1,727 tokensWhen the skill is activated
Reference documents
529 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What it reads and writes
- The shape (150 to 400 words)
- Steps
- The checks (exit 0 when nothing FAILs)
- Example: Jordan Reyes to Maya C. at Google
- What it never does
- Consent and truth
Scripts executed separately
cover.py
Files
SKILL.mdcore7,059 bytesSKILL.mdcore7,059 bytesSKILL.mdcore7,059 bytesSKILL.mdcore7,059 bytesSKILL.mdcore7,059 bytescover.pyruns12,245 bytesexample-cover.mddoc1,276 bytes../hunter-resume/examples/jordan-after.mddoc1,202 bytes
Read SKILL.md
# hunter-cover
The Cover Letter Writer writes one letter per application, never a template with
the company name swapped. A recruiter should be able to tell in two lines that it
was written for this team, by someone who did the work on the resume.
`${CLAUDE_SKILL_DIR}` below is this skill's folder (where this SKILL.md lives).
```bash
python3 "${CLAUDE_SKILL_DIR}/cover.py" check hunter/covers/<role-id>.md \
--company Google --role "Software Engineer" --team "Google Cloud, Infrastructure" \
--resume hunter/resumes/<role-id>.md --pipeline hunter/pipeline.json
python3 "${CLAUDE_SKILL_DIR}/cover.py" cliches
```hunter-followup
When your agent uses it
Follow-up Manager. Sends polite follow-ups so no thread goes cold, and stops before anyone feels chased. Every morning it reads hunter/pipeline.json and lists what is due today: follow-ups on unanswered outreach, replies waiting on you, drafts waiting for your yes, applications with no news after 10 days, thank-you notes after interviews and coffee chats, and anyone the stop rules have closed (first "no", 2 follow-ups, 21 days of silence, a stop request honoured forever). Drafts each message from a polite template; you approve, it goes out from your account, and it is logged. Use when the user says "follow up", "who do I need to follow up with", "nudge", "they haven't replied", "check on my application", "thank-you note", or every morning.
What it loads into context
Frontmatter
205 tokensAlways available to the agent
SKILL.md
1,948 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The stop rules (the same as /hunter-connector's)
- The cadence
- Steps, every morning
- Example: Maya C. in the reel
- What it never does
- Consent and truth
Scripts executed separately
followup.py
Files
SKILL.mdcore7,563 bytesSKILL.mdcore7,563 bytesSKILL.mdcore7,563 bytesSKILL.mdcore7,563 bytesSKILL.mdcore7,563 bytesSKILL.mdcore7,563 bytesSKILL.mdcore7,563 bytesfollowup.pyruns30,456 bytestemplates.jsonasset3,028 bytes../hunter-tracker/pipeline.example.jsonasset4,527 bytes
Read SKILL.md
# hunter-followup
The Follow-up Manager keeps every conversation warm without pestering anyone. It
reads the pipeline, says what is due, drafts it, and waits for the user's yes. It
never sends anything by itself.
`${CLAUDE_SKILL_DIR}` below is this skill's folder (where this SKILL.md lives).
```bash
python3 "${CLAUDE_SKILL_DIR}/followup.py" due --today 2026-10-15
python3 "${CLAUDE_SKILL_DIR}/followup.py" draft --contact maya-c-google --template nudge-1 \
--hook "One new, true line." --today 2026-10-15
python3 "${CLAUDE_SKILL_DIR}/followup.py" record --contact maya-c-google --channel email \
--text-file /tmp/sent.txt --approval "yes" --today 2026-10-15hunter-interview
When your agent uses it
Interview Coach. Runs mock interviews with the questions this company actually asks, from a question bank that cites each company's own published interview-prep pages (Google, Meta, NVIDIA, Netflix, Amazon) and marks everything else as community-reported. Shows the company's interview loop, asks one question at a time, checks every answer for Situation, Task, Action, Result and a number, coaches the weak part, logs the session and tells you what to practice next. Use when the user says "mock interview", "practice interview", "prep me for Google", "what does Amazon ask", "Leadership Principles", "behavioral questions", "STAR", "system design practice", "interview tomorrow", or when a role moves to screen or interview.
What it loads into context
Frontmatter
184 tokensAlways available to the agent
SKILL.md
2,047 tokensWhen the skill is activated
Reference documents
156 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The question bank (questions.json)
- Steps for a mock interview
- The STAR check (one point each, out of 5)
- Example: Jordan Reyes, Google behavioral round
- What it never does
- Consent and truth
Scripts executed separately
star.py
Files
SKILL.mdcore8,390 bytesSKILL.mdcore8,390 bytesSKILL.mdcore8,390 bytesSKILL.mdcore8,390 bytesSKILL.mdcore8,390 bytesSKILL.mdcore8,390 bytesSKILL.mdcore8,390 bytesSKILL.mdcore8,390 bytesSKILL.mdcore8,390 bytesexample-answer.mddoc707 bytesquestions.jsonasset33,401 bytesstar.pyruns17,568 bytes
Read SKILL.md
# hunter-interview
The Interview Coach prepares the user before the interview. It never helps during
one: Google and NVIDIA both say on their own hiring pages that AI tools are not
allowed in their interviews, and every other company should be assumed the same.
`${CLAUDE_SKILL_DIR}` below is this skill's folder (where this SKILL.md lives).
```bash
python3 "${CLAUDE_SKILL_DIR}/star.py" loop --company google
python3 "${CLAUDE_SKILL_DIR}/star.py" ask --company google --round behavioral --count 3 \
--session hunter/interviews/google.json --today 2026-10-20
python3 "${CLAUDE_SKILL_DIR}/star.py" check --text "At Brightline Labs last year..."
python3 "${CLAUDE_SKILL_DIR}/star.py" log --session hunter/interviews/google.json \hunter-linkedin
When your agent uses it
The LinkedIn Optimizer. Rewrites the user's LinkedIn headline, About, skills and experience for the job they want, using only what is true, then checks the draft against LinkedIn's field limits (headline, About, skills count, position title and description) and against the keywords of the target job description, with a before/after report: coverage, required terms, what was gained and what is still missing. The user pastes the final text into LinkedIn themselves. Use when the user says "optimize my LinkedIn", "rewrite my headline", "fix my About section", "LinkedIn for this job", "what skills should I list", or "make recruiters find me".
What it loads into context
Frontmatter
176 tokensAlways available to the agent
SKILL.md
1,354 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What it reads and writes
- LinkedIn's limits
- The rules
- Steps
- Example: the reel
- What it never does
Scripts executed separately
profile.py
Files
SKILL.mdcore5,571 bytesSKILL.mdcore5,571 bytesSKILL.mdcore5,571 bytesSKILL.mdcore5,571 bytesSKILL.mdcore5,571 byteslimits.jsonasset1,656 byteslinkedin.example.jsonasset2,167 bytesprofile.pyruns17,458 bytes
Read SKILL.md
# hunter-linkedin
Recruiters search LinkedIn by keyword, and the hiring manager will open the
profile before the coffee chat. This skill makes the profile say what the
user really does in the words the job description uses, inside LinkedIn's
limits, and shows the before and after.
What the user typed after `/hunter-linkedin`: `$ARGUMENTS`
Tool in this folder (standard library Python, never touches LinkedIn):
```bash
python3 "${CLAUDE_SKILL_DIR}/profile.py" --help
```hunter-research
When your agent uses it
The Company Researcher. Researches each company the user is applying to and writes a one-page brief in hunter/companies/: the business from its own SEC filings (latest 10-K, 10-Q, recent 8-Ks with what each one is about), news from the company's own blogs, the culture in the company's own words, the team the user would join, and the interview loop with a source on every line (official prep pages for Google, Meta, NVIDIA, Netflix and Amazon, everything else marked community-reported). Use when the user says "research Google", "tell me about this company", "company brief", "prep me for Meta", "what's their interview process", "what does this team do", or before a coffee chat or interview.
What it loads into context
Frontmatter
194 tokensAlways available to the agent
SKILL.md
1,368 tokensWhen the skill is activated
Reference documents
227 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What it reads and writes
- The rules
- Steps
- Example: the reel
- What it never does
Scripts executed separately
brief.py
Files
SKILL.mdcore5,541 bytesSKILL.mdcore5,541 bytesSKILL.mdcore5,541 bytesSKILL.mdcore5,541 bytesSKILL.mdcore5,541 bytesbrief.pyruns23,524 bytesbrief.template.mddoc974 bytescompanies.jsonasset2,598 bytesloops.jsonasset11,161 bytes
Read SKILL.md
# hunter-research
Walking into a coffee chat or an interview knowing the business, the latest
news and how the loop works is the cheapest edge there is. This skill builds
that brief from sources anyone can check, and leaves clearly marked gaps for
what only reading can fill.
What the user typed after `/hunter-research`: `$ARGUMENTS`
Tool in this folder (standard library Python):
```bash
python3 "${CLAUDE_SKILL_DIR}/brief.py" --help
```hunter-resume
When your agent uses it
Resume Writer. Rewrites your resume for each role so it reads like the job description, using only what is true: it mirrors the posting's own words for skills you really have, moves buried results into strong bullets (a number, a strong verb, one line), reorders for the role, and asks you about anything the posting wants that the resume does not show. Scores the match from 0 to 100 with a published formula (minimum qualifications, preferred qualifications, bullet strength, sections) and runs a truth check that fails if the rewrite adds a number or a skill that was not in your original. Use when the user says "rewrite my resume", "tailor my resume", "ATS score", "beat the ATS", "match my resume to this job", "resume for Google", "improve my bullets", or after /hunter-scout picks a role.
What it loads into context
Frontmatter
217 tokensAlways available to the agent
SKILL.md
2,137 tokensWhen the skill is activated
Reference documents
901 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What it reads and writes
- The score (ats.py, 0 to 100)
- Steps
- Example: Jordan Reyes and the Google posting
- What it never does
- Consent and truth
Scripts executed separately
ats.py
Files
SKILL.mdcore8,476 bytesSKILL.mdcore8,476 bytesSKILL.mdcore8,476 bytesSKILL.mdcore8,476 bytesSKILL.mdcore8,476 bytesSKILL.mdcore8,476 bytesSKILL.mdcore8,476 bytesats.pyruns29,604 bytesexamples/google-swe-jd.mddoc1,904 bytesexamples/jordan-after.mddoc1,202 bytesexamples/jordan-before.mddoc1,173 bytesterms.jsonasset11,949 bytes
Read SKILL.md
# hunter-resume
The Resume Writer makes one resume per role. The reel's numbers come from the
example in this folder: Jordan Reyes against a Google Software Engineer posting
goes from 54 to 96, and the truth check passes because the rewrite only
reorders and rewords what Jordan's original already said.
`${CLAUDE_SKILL_DIR}` below is this skill's folder (where this SKILL.md lives).
```bash
python3 "${CLAUDE_SKILL_DIR}/ats.py" terms --jd hunter/resumes/<role-id>.jd.md
python3 "${CLAUDE_SKILL_DIR}/ats.py" score hunter/resumes/base.md --jd hunter/resumes/<role-id>.jd.md
python3 "${CLAUDE_SKILL_DIR}/ats.py" compare hunter/resumes/base.md hunter/resumes/<role-id>.md \
--jd hunter/resumes/<role-id>.jd.md --facts hunter/resumes/facts.mdhunter-salary
When your agent uses it
Salary Negotiator. Benchmarks the role and scripts your counter-offer. Turns an offer into total compensation year by year (base, bonus target, equity with its real vesting schedule including back-loaded ones, sign-on), compares offers side by side, pulls wage percentiles from the U.S. Bureau of Labor Statistics (OEWS) for your occupation and area, adds pay ranges printed in job postings with their URL, and writes a counter range and a polite script that cites every number. Never invents a market number. Use when the user says "I got an offer", "negotiate my salary", "is this offer good", "compare offers", "counter offer", "what should I ask for", "total compensation", "RSU vesting", or when a role reaches the offer stage.
What it loads into context
Frontmatter
200 tokensAlways available to the agent
SKILL.md
2,219 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What it reads and writes
- Step 1: the offer, exactly as written
- Step 2: total compensation
- Step 3: benchmarks, cited or not at all
- Step 4: the counter
- Example: Jordan's Google offer (fictional numbers)
- What it never does
- Consent and truth
Scripts executed separately
offer.py
Files
SKILL.mdcore8,479 bytesSKILL.mdcore8,479 bytesSKILL.mdcore8,479 bytesSKILL.mdcore8,479 bytesSKILL.mdcore8,479 bytesSKILL.mdcore8,479 bytesSKILL.mdcore8,479 bytesbench.example.jsonasset2,328 bytescompeting.example.jsonasset480 bytesoffer.example.jsonasset512 bytesoffer.pyruns28,653 bytes
Read SKILL.md
# hunter-salary
The Salary Negotiator puts numbers on the table that the user can defend: every
benchmark comes with its source and URL, and the counter is built from those
numbers by a rule the user can read. It drafts the message; the user sends it.
`${CLAUDE_SKILL_DIR}` below is this skill's folder (where this SKILL.md lives).
```bash
python3 "${CLAUDE_SKILL_DIR}/offer.py" total hunter/offers/google.json
python3 "${CLAUDE_SKILL_DIR}/offer.py" compare hunter/offers/google.json hunter/offers/amazon.json
python3 "${CLAUDE_SKILL_DIR}/offer.py" bls --soc 15-1252 --area msa:41940 --out hunter/offers/bench.json
python3 "${CLAUDE_SKILL_DIR}/offer.py" bench hunter/offers/google.json --bench hunter/offers/bench.json
python3 "${CLAUDE_SKILL_DIR}/offer.py" counter hunter/offers/google.json --bench hunter/offers/bench.json \hunter-scout
When your agent uses it
The Job Scout. Finds the best open roles for the user's profile and ranks them by fit: pulls live listings from public job boards that need no login (Greenhouse, Lever, Ashby, NVIDIA's Workday board, Netflix's careers search), filters by title, place, remote and how recent, scores every listing 0-100 against hunter/profile.json with the reasons (title, skills, location, seniority, pay range, freshness), writes hunter/jobs.json and adds the user's picks to the pipeline. Gives the manual route for Google, Meta and Amazon, whose careers sites cannot be read by a script. Use when the user says "find me jobs", "best listings for me", "scout roles", "what's open at Google", "search Software Engineer jobs", "rank these jobs", or "where should I apply".
What it loads into context
Frontmatter
208 tokensAlways available to the agent
SKILL.md
1,787 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What it reads and writes
- Which careers sites it can and cannot read
- The fit score
- Steps
- Example: the reel
- What it never does
Scripts executed separately
jobs.py
Files
SKILL.mdcore7,073 bytesSKILL.mdcore7,073 bytesSKILL.mdcore7,073 bytesSKILL.mdcore7,073 bytesSKILL.mdcore7,073 bytesSKILL.mdcore7,073 bytesboards.jsonasset7,105 bytesjobs.pyruns38,821 bytes
Read SKILL.md
# hunter-scout
Instead of sending thousands of resumes, send a few to the roles that fit. The
scout reads the job boards companies publish for exactly this purpose, keeps
the listings that match the titles and places you want, and ranks them with a
score you can check line by line.
What the user typed after `/hunter-scout`: `$ARGUMENTS`
Tool in this folder (standard library Python, no keys, no login):
```bash
python3 "${CLAUDE_SKILL_DIR}/jobs.py" --help
```hunter-tracker
When your agent uses it
Pipeline Tracker. Tracks every role, contact and next step in one pipeline: hunter/pipeline.json, the single file every hunter skill reads and writes. Adds and updates roles and contacts, moves roles through the stages (saved, applied, screen, interview, onsite, offer, rejected, withdrawn), shows a summary by stage, what is coming up this week, what has gone stale, and exports CSV for a spreadsheet. Owns the pipeline schema and validates it after every change. Use when the user says "show my pipeline", "where am I with each application", "add this job", "I applied", "I got an interview", "I got rejected", "what's next", "what's stale", "export my applications", or "track this".
What it loads into context
Frontmatter
191 tokensAlways available to the agent
SKILL.md
2,218 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The schema (this skill owns it)
- Who writes what
- Steps
- Example: Jordan's pipeline on 2026-10-16
- What it never does
- Consent and truth
Scripts executed separately
pipeline.py
Files
SKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytesSKILL.mdcore8,559 bytespipeline.example.jsonasset4,527 bytespipeline.pyruns34,055 bytes
Read SKILL.md
# hunter-tracker
The Pipeline Tracker is the memory of the whole search. Every other hunter skill
reads `hunter/pipeline.json` and writes its part back; this skill owns the file's
shape and checks it.
`${CLAUDE_SKILL_DIR}` below is this skill's folder (where this SKILL.md lives).
```bash
python3 "${CLAUDE_SKILL_DIR}/pipeline.py" init
python3 "${CLAUDE_SKILL_DIR}/pipeline.py" add-role --company Google --title "Software Engineer" --url <posting URL>
python3 "${CLAUDE_SKILL_DIR}/pipeline.py" move google-software-engineer applied --today 2026-10-23
python3 "${CLAUDE_SKILL_DIR}/pipeline.py" update-role google-software-engineer --next-step "Recruiter screen" --next-date 2026-10-30
python3 "${CLAUDE_SKILL_DIR}/pipeline.py" add-contact --name "Maya C." --company Google --role "Engineering Manager"Skills included
- hunter-apply
- hunter-connector
- hunter-cover
- hunter-followup
- hunter-interview
- hunter-linkedin
- hunter-research
- hunter-resume
- hunter-salary
- hunter-scout
- hunter-tracker
Repository facts
- License
- MIT
- Created
- Oct 15, 2026
- Last pushed
- Oct 15, 2026
- Forks
- 16
- Open issues
- 1
- Project activity
- Active
Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 6, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 73 notes
skills/hunter-apply/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-apply/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-apply/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-apply/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-apply/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-apply/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-connector/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-cover/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/hunter-cover/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- No dangerous shell commands
- 299 network referencesin 17 files: hunter-research/loops.json:8 hunter-research/companies.json:8 hunter-interview/questions.json:5 and 14 more
- No hidden or obfuscated text
- No instruction overrides
- 12 executable scriptsin 12 files: hunter-apply/answers.py:1 hunter-connector/emails.py:1 hunter-connector/outreach.py:1 and 9 more
- 3 references to secretsin 3 files: hunter-research/brief.py:86 hunter-salary/SKILL.md:68 hunter-salary/offer.py:533
- No requests to vote in Hot
Show all 314 signals by file
.claude-plugin/marketplace.json
- Network accessline 1
{ "$schema": "https://anthropic.com/claude-code/marketplace.schema.json", - Network accessline 2
"$schema": "https://anthropic.com/claude-code/marketplace.schema.json", - Network accessline 5
"owner": { "name": "Jake Schincariol", "url": "https://opusjake.ai" - Network accessline 6
"name": "Jake Schincariol", "url": "https://opusjake.ai" }, - Network accessline 7
"url": "https://opusjake.ai" - Network accessline 13
"source": "./", "category": "productivity", "homepage": "https://github.com/Jakeschincariol/hunter-skill" - Network accessline 14
"category": "productivity", "homepage": "https://github.com/Jakeschincariol/hunter-skill" } - Network accessline 15
"homepage": "https://github.com/Jakeschincariol/hunter-skill"
.claude-plugin/plugin.json
- Network accessline 5
"author": { "name": "Jake Schincariol", "url": "https://opusjake.ai" - Network accessline 6
"name": "Jake Schincariol", "url": "https://opusjake.ai" }, - Network accessline 7
"url": "https://opusjake.ai" - Network accessline 8
}, "homepage": "https://github.com/Jakeschincariol/hunter-skill", "repository": "https://github.com/Jakeschincariol/hunter-skill", - Network accessline 9
"homepage": "https://github.com/Jakeschincariol/hunter-skill", - Network accessline 10
"repository": "https://github.com/Jakeschincariol/hunter-skill",
skills/hunter-apply/answers.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 11
python3 answers.py fill --questions form.json --out hunter/applications/<role-id>.json python3 answers.py checklist --role <role-id> python3 answers.py draft --role <role-id> --form-url https://... --answers hunter/applications/<rol - Network accessline 12
python3 answers.py checklist --role <role-id> python3 answers.py draft --role <role-id> --form-url https://... --answers hunter/applications/<role-id>.json python3 answers.py submitted --role <role-id> --confirm "I submitted it" - Network accessline 13
python3 answers.py draft --role <role-id> --form-url https://... --answers hunter/applications/<role-id>.json
skills/hunter-apply/profile.example.json
- Network accessline 16
"dream_companies": ["Google", "Meta", "NVIDIA", "Netflix", "Amazon"], "links": { "linkedin": "https://www.linkedin.com/in/your-handle", - Network accessline 17
"links": { "linkedin": "https://www.linkedin.com/in/your-handle", "github": "https://github.com/your-handle", - Network accessline 18
"linkedin": "https://www.linkedin.com/in/your-handle", - Network accessline 19
"github": "https://github.com/your-handle",
skills/hunter-connector/SKILL.md
- Network accessline 60
python3 "${CLAUDE_SKILL_DIR}/outreach.py" contact add --name "Maya C." --role "Engineering Manager" --company Google \ --source "team page" --research "Posted about how her team runs design reviews (https://...)" - Network accessline 61
--source "team page" --research "Posted about how her team runs design reviews (https://...)"
skills/hunter-connector/emails.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/hunter-connector/outreach.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/hunter-cover/cover.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/hunter-followup/followup.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/hunter-interview/SKILL.md
- Network accessline 103
1. [google-behavioral-01] Tell me about a time you had to make a decision without the information you wanted. ... (behavioral, ambiguity; official: Google says behavioral rounds cover this (https://www.google.com/about/careers/applicatio - Network accessline 104
(behavioral, ambiguity; official: Google says behavioral rounds cover this (https://www.google.com/about/careers/applications/interview-tips/))
skills/hunter-interview/questions.json
- Network accessline 5
"google-how-we-hire": { "publisher": "Google", "url": "https://www.google.com/about/careers/applications/how-we-hire/", - Network accessline 6
"publisher": "Google", "url": "https://www.google.com/about/careers/applications/how-we-hire/", "checked": "2026-10-15", - Network accessline 7
"url": "https://www.google.com/about/careers/applications/how-we-hire/", - Network accessline 11
"google-interview-tips": { "publisher": "Google", "url": "https://www.google.com/about/careers/applications/interview-tips/", - Network accessline 12
"publisher": "Google", "url": "https://www.google.com/about/careers/applications/interview-tips/", "checked": "2026-10-15", - Network accessline 13
"url": "https://www.google.com/about/careers/applications/interview-tips/", - Network accessline 17
"meta-techscreen": { "publisher": "Meta", "url": "https://www.metacareers.com/swe-prep-techscreen/", - Network accessline 18
"publisher": "Meta", "url": "https://www.metacareers.com/swe-prep-techscreen/", "checked": "2026-10-15", - Network accessline 19
"url": "https://www.metacareers.com/swe-prep-techscreen/", - Network accessline 23
"meta-onsite": { "publisher": "Meta", "url": "https://www.metacareers.com/swe-prep-onsite/", - Network accessline 24
"publisher": "Meta", "url": "https://www.metacareers.com/swe-prep-onsite/", "checked": "2026-10-15", - Network accessline 25
"url": "https://www.metacareers.com/swe-prep-onsite/", - Network accessline 29
"nvidia-how-we-hire": { "publisher": "NVIDIA", "url": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/", - Network accessline 30
"publisher": "NVIDIA", "url": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/", "checked": "2026-10-15", - Network accessline 31
"url": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/", - Network accessline 35
"netflix-culture": { "publisher": "Netflix", "url": "https://jobs.netflix.com/culture", - Network accessline 36
"publisher": "Netflix", "url": "https://jobs.netflix.com/culture", "checked": "2026-10-15", - Network accessline 37
"url": "https://jobs.netflix.com/culture", - Network accessline 41
"amazon-sde2-prep": { "publisher": "Amazon", "url": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep", - Network accessline 42
"publisher": "Amazon", "url": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep", "checked": "2026-10-15", - Network accessline 43
"url": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep", - Network accessline 47
"amazon-interview-loop": { "publisher": "Amazon", "url": "https://www.amazon.jobs/content/en/how-we-hire/interview-loop", - Network accessline 48
"publisher": "Amazon", "url": "https://www.amazon.jobs/content/en/how-we-hire/interview-loop", "checked": "2026-10-15", - Network accessline 49
"url": "https://www.amazon.jobs/content/en/how-we-hire/interview-loop", - Network accessline 53
"amazon-leadership-principles": { "publisher": "Amazon", "url": "https://www.amazon.jobs/content/en/our-workplace/leadership-principles", - Network accessline 54
"publisher": "Amazon", "url": "https://www.amazon.jobs/content/en/our-workplace/leadership-principles", "checked": "2026-10-15", - Network accessline 55
"url": "https://www.amazon.jobs/content/en/our-workplace/leadership-principles",
skills/hunter-interview/star.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/hunter-linkedin/limits.json
- Network accessline 17
"unit": "skills", "status": "linkedin_help", "source": "https://www.linkedin.com/help/linkedin/answer/35265", - Network accessline 18
"status": "linkedin_help", "source": "https://www.linkedin.com/help/linkedin/answer/35265", "quote": "You can add a maximum of 100 skills to your profile." - Network accessline 19
"source": "https://www.linkedin.com/help/linkedin/answer/35265", - Network accessline 34
"unit": "characters", "status": "linkedin_help", "source": "https://www.linkedin.com/help/linkedin/answer/46662", - Network accessline 35
"status": "linkedin_help", "source": "https://www.linkedin.com/help/linkedin/answer/46662", "quote": "Each message can be up to 200 characters.", - Network accessline 36
"source": "https://www.linkedin.com/help/linkedin/answer/46662", - Network accessline 42
"automation": { "status": "linkedin_help", "source": "https://www.linkedin.com/help/linkedin/answer/a1341387", - Network accessline 43
"status": "linkedin_help", "source": "https://www.linkedin.com/help/linkedin/answer/a1341387", "rule": "LinkedIn's User Agreement (section 8.2) prohibits bots and other unauthorized automated methods to access the Services, add or downl - Network accessline 44
"source": "https://www.linkedin.com/help/linkedin/answer/a1341387", "rule": "LinkedIn's User Agreement (section 8.2) prohibits bots and other unauthorized automated methods to access the Services, add or download contacts, or send or redi
skills/hunter-linkedin/profile.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/hunter-research/SKILL.md
- Network accessline 72
python3 "${CLAUDE_SKILL_DIR}/brief.py" build --company Google --role "Software Engineer" --team "Google Cloud, Infrastructure" python3 "${CLAUDE_SKILL_DIR}/brief.py" build --company "Acme Corp" --ticker ACME --feed https://acme.example/b - Network accessline 73
python3 "${CLAUDE_SKILL_DIR}/brief.py" build --company "Acme Corp" --ticker ACME --feed https://acme.example/blog/rss
skills/hunter-research/brief.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 10
python3 brief.py build --company Google --role "Software Engineer" --team "Google Cloud, Infrastructure" python3 brief.py build --company Stripe --feed https://stripe.com/blog/feed.rss # any company - Network accessline 11
python3 brief.py build --company Stripe --feed https://stripe.com/blog/feed.rss # any company - Network accessline 16
Sources: SEC EDGAR submissions https://data.sec.gov/submissions/CIK##########.json - Network accessline 17
SEC EDGAR submissions https://data.sec.gov/submissions/CIK##########.json - Network accessline 18
SEC ticker list https://www.sec.gov/files/company_tickers.json - Network accessline 42
LOOPS = os.path.join(HERE, "loops.json") TEMPLATE = os.path.join(HERE, "brief.template.md") SUBMISSIONS = "https://data.sec.gov/submissions/CIK%s.json" - Network accessline 43
TEMPLATE = os.path.join(HERE, "brief.template.md") SUBMISSIONS = "https://data.sec.gov/submissions/CIK%s.json" TICKERS = "https://www.sec.gov/files/company_tickers.json" - Network accessline 44
SUBMISSIONS = "https://data.sec.gov/submissions/CIK%s.json" - Network accessline 45
TICKERS = "https://www.sec.gov/files/company_tickers.json" - Network accessline 46
ARCHIVE = "https://www.sec.gov/Archives/edgar/data/%d/%s/%s" - Network accessline 47
EDGAR_PAGE = "https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=%s" - Network accessline 48
FEED_UA = "hunter-skill/1.0 (company research; +https://github.com/Jakeschincariol/hunter-skill)" - Access to secretsline 86
[Credential-related source omitted] - Network accessline 114
time.sleep(self.pause - gap) self._last = time.time() req = urllib.request.Request(url, headers={"User-Agent": self.sec_ua if sec else FEED_UA, - Network accessline 115
self._last = time.time() req = urllib.request.Request(url, headers={"User-Agent": self.sec_ua if sec else FEED_UA, "Accept-Encoding": "gzip"}) - Network accessline 116
req = urllib.request.Request(url, headers={"User-Agent": self.sec_ua if sec else FEED_UA, - Network accessline 117
"Accept-Encoding": "gzip"}) try: with urllib.request.urlopen(req, timeout=30) as resp: - Network accessline 118
try: with urllib.request.urlopen(req, timeout=30) as resp: data = resp.read() - Network accessline 119
with urllib.request.urlopen(req, timeout=30) as resp:
skills/hunter-research/companies.json
- Network accessline 8
"cik": "0001652044", "domain": "google.com", "careers": "https://www.google.com/about/careers/applications/", - Network accessline 9
"domain": "google.com", "careers": "https://www.google.com/about/careers/applications/", "investors": "https://abc.xyz/investor/", - Network accessline 10
"careers": "https://www.google.com/about/careers/applications/", - Network accessline 11
"investors": "https://abc.xyz/investor/", - Network accessline 14
"id": "google-blog", "name": "News from Google", "url": "https://blog.google/rss/" - Network accessline 15
"name": "News from Google", "url": "https://blog.google/rss/" }, - Network accessline 16
"url": "https://blog.google/rss/" - Network accessline 19
"id": "google-cloud-blog", "name": "Google Cloud Blog", "url": "https://cloudblog.withgoogle.com/rss/" - Network accessline 20
"name": "Google Cloud Blog", "url": "https://cloudblog.withgoogle.com/rss/" }, - Network accessline 21
"url": "https://cloudblog.withgoogle.com/rss/" - Network accessline 24
"id": "google-developers", "name": "Google Developers Blog", "url": "https://developers.googleblog.com/feeds/posts/default" - Network accessline 25
"name": "Google Developers Blog", "url": "https://developers.googleblog.com/feeds/posts/default" } - Network accessline 26
"url": "https://developers.googleblog.com/feeds/posts/default" - Network accessline 33
"cik": "0001326801", "domain": "meta.com", "careers": "https://www.metacareers.com/", - Network accessline 34
"domain": "meta.com", "careers": "https://www.metacareers.com/", "feeds": [ - Network accessline 35
"careers": "https://www.metacareers.com/", - Network accessline 38
"id": "meta-engineering", "name": "Engineering at Meta", "url": "https://engineering.fb.com/feed/" - Network accessline 39
"name": "Engineering at Meta", "url": "https://engineering.fb.com/feed/" } - Network accessline 40
"url": "https://engineering.fb.com/feed/" - Network accessline 47
"cik": "0001045810", "domain": "nvidia.com", "careers": "https://www.nvidia.com/en-us/about-nvidia/careers/", - Network accessline 48
"domain": "nvidia.com", "careers": "https://www.nvidia.com/en-us/about-nvidia/careers/", "feeds": [ - Network accessline 49
"careers": "https://www.nvidia.com/en-us/about-nvidia/careers/", - Network accessline 52
"id": "nvidia-dev", "name": "NVIDIA Technical Blog", "url": "https://developer.nvidia.com/blog/feed/" - Network accessline 53
"name": "NVIDIA Technical Blog", "url": "https://developer.nvidia.com/blog/feed/" }, - Network accessline 54
"url": "https://developer.nvidia.com/blog/feed/" - Network accessline 57
"id": "nvidia-blog", "name": "NVIDIA Blog", "url": "https://blogs.nvidia.com/feed/" - Network accessline 58
"name": "NVIDIA Blog", "url": "https://blogs.nvidia.com/feed/" } - Network accessline 59
"url": "https://blogs.nvidia.com/feed/" - Network accessline 66
"cik": "0001065280", "domain": "netflix.com", "careers": "https://jobs.netflix.com/", - Network accessline 67
"domain": "netflix.com", "careers": "https://jobs.netflix.com/", "feeds": [ - Network accessline 68
"careers": "https://jobs.netflix.com/", - Network accessline 71
"id": "netflix-tech", "name": "Netflix TechBlog", "url": "https://netflixtechblog.com/feed" - Network accessline 72
"name": "Netflix TechBlog", "url": "https://netflixtechblog.com/feed" } - Network accessline 73
"url": "https://netflixtechblog.com/feed" - Network accessline 80
"cik": "0001018724", "domain": "amazon.com", "careers": "https://www.amazon.jobs/", - Network accessline 81
"domain": "amazon.com", "careers": "https://www.amazon.jobs/", "feeds": [ - Network accessline 82
"careers": "https://www.amazon.jobs/", - Network accessline 85
"id": "amazon-news", "name": "Amazon News", "url": "https://www.aboutamazon.com/news/rss" - Network accessline 86
"name": "Amazon News", "url": "https://www.aboutamazon.com/news/rss" }, - Network accessline 87
"url": "https://www.aboutamazon.com/news/rss" - Network accessline 90
"id": "amazon-science", "name": "Amazon Science", "url": "https://www.amazon.science/index.rss" - Network accessline 91
"name": "Amazon Science", "url": "https://www.amazon.science/index.rss" }, - Network accessline 92
"url": "https://www.amazon.science/index.rss" - Network accessline 95
"id": "aws-news", "name": "AWS News Blog", "url": "https://aws.amazon.com/blogs/aws/feed/" - Network accessline 96
"name": "AWS News Blog", "url": "https://aws.amazon.com/blogs/aws/feed/" } - Network accessline 97
"url": "https://aws.amazon.com/blogs/aws/feed/"
skills/hunter-research/loops.json
- Network accessline 8
{ "title": "Our hiring process", "url": "https://www.google.com/about/careers/applications/how-we-hire/" - Network accessline 9
"title": "Our hiring process", "url": "https://www.google.com/about/careers/applications/how-we-hire/" }, - Network accessline 10
"url": "https://www.google.com/about/careers/applications/how-we-hire/" - Network accessline 12
{ "title": "Interviewing at Google: best practices, advice and tips", "url": "https://www.google.com/about/careers/applications/interview-tips/" - Network accessline 13
"title": "Interviewing at Google: best practices, advice and tips", "url": "https://www.google.com/about/careers/applications/interview-tips/" }, - Network accessline 14
"url": "https://www.google.com/about/careers/applications/interview-tips/" - Network accessline 16
{ "title": "Build your future: technical learning resources (techdevguide.withgoogle.com redirects here)", "url": "https://www.google.com/about/careers/applications/buildyourfuture/resources/" - Network accessline 17
"title": "Build your future: technical learning resources (techdevguide.withgoogle.com redirects here)", "url": "https://www.google.com/about/careers/applications/buildyourfuture/resources/" } - Network accessline 18
"url": "https://www.google.com/about/careers/applications/buildyourfuture/resources/" - Network accessline 23
"text": "Steps: apply online; you may take the Google Hiring Assessment (workstyle) and role-related assessments such as a coding exercise; one or two shorter recruiter conversations by phone or video; sometimes a small project; then a pane - Network accessline 24
"kind": "official", "source": "https://www.google.com/about/careers/applications/how-we-hire/" }, - Network accessline 25
"source": "https://www.google.com/about/careers/applications/how-we-hire/" - Network accessline 28
"text": "Structured interviewing: every candidate for a role is scored on the same clear rubrics, with role-related, open-ended questions and no brainteasers.", "kind": "official", "source": "https://www.google.com/about/careers/a - Network accessline 29
"kind": "official", "source": "https://www.google.com/about/careers/applications/how-we-hire/" }, - Network accessline 30
"source": "https://www.google.com/about/careers/applications/how-we-hire/" - Network accessline 33
"text": "For software engineering, Google's hiring page points to its own prep videos on coding interviews and systems design interviews.", "kind": "official", "source": "https://www.google.com/about/careers/applications/how-we-hi - Network accessline 34
"kind": "official", "source": "https://www.google.com/about/careers/applications/how-we-hire/" }, - Network accessline 35
"source": "https://www.google.com/about/careers/applications/how-we-hire/" - Network accessline 38
"text": "Behavioral answers should be specific examples; Google suggests the STAR approach (Situation, Task, Activity, Result) and a broad library of examples.", "kind": "official", "source": "https://www.google.com/about/careers/ - Network accessline 39
"kind": "official", "source": "https://www.google.com/about/careers/applications/interview-tips/" }, - Network accessline 40
"source": "https://www.google.com/about/careers/applications/interview-tips/" - Network accessline 43
"text": "AI tools are not permitted during Google interviews.", "kind": "official", "source": "https://www.google.com/about/careers/applications/how-we-hire/" - Network accessline 44
"kind": "official", "source": "https://www.google.com/about/careers/applications/how-we-hire/" }, - Network accessline 45
"source": "https://www.google.com/about/careers/applications/how-we-hire/" - Network accessline 48
"text": "In-person interviews are back for some roles; initial interviews are often virtual. Outcomes can take several weeks.", "kind": "official", "source": "https://www.google.com/about/careers/applications/interview-tips/" - Network accessline 49
"kind": "official", "source": "https://www.google.com/about/careers/applications/interview-tips/" }, - Network accessline 50
"source": "https://www.google.com/about/careers/applications/interview-tips/" - Network accessline 62
{ "title": "Preparing for your technical screen (Software Engineering)", "url": "https://www.metacareers.com/swe-prep-techscreen/" - Network accessline 63
"title": "Preparing for your technical screen (Software Engineering)", "url": "https://www.metacareers.com/swe-prep-techscreen/" }, - Network accessline 64
"url": "https://www.metacareers.com/swe-prep-techscreen/" - Network accessline 66
{ "title": "Preparing for your full loop interview (links the Full Loop Interview Guide PDF)", "url": "https://www.metacareers.com/swe-prep-onsite/" - Network accessline 67
"title": "Preparing for your full loop interview (links the Full Loop Interview Guide PDF)", "url": "https://www.metacareers.com/swe-prep-onsite/" } - Network accessline 68
"url": "https://www.metacareers.com/swe-prep-onsite/" - Network accessline 73
"text": "Technical screen: a 45-minute conversation with an engineer.", "kind": "official", "source": "https://www.metacareers.com/swe-prep-techscreen/" - Network accessline 74
"kind": "official", "source": "https://www.metacareers.com/swe-prep-techscreen/" }, - Network accessline 75
"source": "https://www.metacareers.com/swe-prep-techscreen/" - Network accessline 78
"text": "Full loop: 4 to 6 interviews of 45 minutes each, covering coding, design and behavioral.", "kind": "official", "source": "https://www.metacareers.com/swe-prep-onsite/" - Network accessline 79
"kind": "official", "source": "https://www.metacareers.com/swe-prep-onsite/" }, - Network accessline 80
"source": "https://www.metacareers.com/swe-prep-onsite/" - Network accessline 83
"text": "Coding: expect about two problems in about 40 minutes, written without a compiler; scored on communication, problem solving, coding and verification. The guide says Meta does not ask dynamic programming questions.", "kind": "o - Network accessline 84
"kind": "official", "source": "https://www.metacareers.com/swe-prep-onsite/" }, - Network accessline 85
"source": "https://www.metacareers.com/swe-prep-onsite/" - Network accessline 88
"text": "Design: 45 minutes, either Systems Design (distributed systems, scalability, performance) or Product Architectural Design (API design, usability), depending on the role.", "kind": "official", "source": "https://www.metaca - Network accessline 89
"kind": "official", "source": "https://www.metacareers.com/swe-prep-onsite/" }, - Network accessline 90
"source": "https://www.metacareers.com/swe-prep-onsite/" - Network accessline 93
"text": "Behavioral: 45 minutes on resolving conflict, growing continuously, embracing ambiguity, driving results and communicating effectively; the guide suggests the STAR method.", "kind": "official", "source": "https://www.meta - Network accessline 94
"kind": "official", "source": "https://www.metacareers.com/swe-prep-onsite/" }, - Network accessline 95
"source": "https://www.metacareers.com/swe-prep-onsite/" - Network accessline 98
"text": "No unauthorized outside assistance during interviews, including AI tools like ChatGPT; you may be asked to share your screen over Zoom.", "kind": "official", "source": "https://www.metacareers.com/swe-prep-onsite/" - Network accessline 99
"kind": "official", "source": "https://www.metacareers.com/swe-prep-onsite/" }, - Network accessline 100
"source": "https://www.metacareers.com/swe-prep-onsite/" - Network accessline 112
{ "title": "How we hire", "url": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" - Network accessline 113
"title": "How we hire", "url": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" } - Network accessline 114
"url": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" - Network accessline 119
"text": "NVIDIA suggests applying to the three to five roles that best fit your background rather than many.", "kind": "official", "source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" - Network accessline 120
"kind": "official", "source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" }, - Network accessline 121
"source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" - Network accessline 124
"text": "Interviews are one-on-one, small group or panel, usually 30 to 60 minutes each, by phone, video or in person; an onsite, in-person interview is required before an offer can be considered.", "kind": "official", "source": " - Network accessline 125
"kind": "official", "source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" }, - Network accessline 126
"source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" - Network accessline 129
"text": "Technical candidates may complete a coding exercise, usually on HackerRank, on a whiteboard or an NVIDIA-provided laptop.", "kind": "official", "source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" - Network accessline 130
"kind": "official", "source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" }, - Network accessline 131
"source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" - Network accessline 134
"text": "Using unapproved outside tools such as ChatGPT during interviews disqualifies the candidacy.", "kind": "official", "source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" - Network accessline 135
"kind": "official", "source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" }, - Network accessline 136
"source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" - Network accessline 139
"text": "If you interview with several teams, feedback may be shared across them. A referral comes from an NVIDIA employee through the internal job board, before you apply.", "kind": "official", "source": "https://www.nvidia.com/e - Network accessline 140
"kind": "official", "source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" }, - Network accessline 141
"source": "https://www.nvidia.com/en-us/about-nvidia/careers/how-we-hire/" - Network accessline 153
{ "title": "Netflix culture memo", "url": "https://jobs.netflix.com/culture" - Network accessline 154
"title": "Netflix culture memo", "url": "https://jobs.netflix.com/culture" }, - Network accessline 155
"url": "https://jobs.netflix.com/culture" - Network accessline 157
{ "title": "Engineering at Netflix (careers)", "url": "https://jobs.netflix.com/careers/engineering" - Network accessline 158
"title": "Engineering at Netflix (careers)", "url": "https://jobs.netflix.com/careers/engineering" } - Network accessline 159
"url": "https://jobs.netflix.com/careers/engineering" - Network accessline 164
"text": "Netflix does not publish a step-by-step interview guide. Its culture memo is the official reference for the culture conversations: a dream team of high performers, people over process, freedom and responsibility, context not contro - Network accessline 165
"kind": "official", "source": "https://jobs.netflix.com/culture" }, - Network accessline 166
"source": "https://jobs.netflix.com/culture" - Network accessline 169
"text": "The memo says Netflix pays top of market for each role and location to recruit and retain stunning colleagues.", "kind": "official", "source": "https://jobs.netflix.com/culture" - Network accessline 170
"kind": "official", "source": "https://jobs.netflix.com/culture" }, - Network accessline 171
"source": "https://jobs.netflix.com/culture" - Network accessline 183
{ "title": "How we hire", "url": "https://www.amazon.jobs/content/en/how-we-hire" - Network accessline 184
"title": "How we hire", "url": "https://www.amazon.jobs/content/en/how-we-hire" }, - Network accessline 185
"url": "https://www.amazon.jobs/content/en/how-we-hire" - Network accessline 187
{ "title": "The interview loop", "url": "https://www.amazon.jobs/content/en/how-we-hire/interview-loop" - Network accessline 188
"title": "The interview loop", "url": "https://www.amazon.jobs/content/en/how-we-hire/interview-loop" }, - Network accessline 189
"url": "https://www.amazon.jobs/content/en/how-we-hire/interview-loop" - Network accessline 191
{ "title": "SDE II interview prep", "url": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep" - Network accessline 192
"title": "SDE II interview prep", "url": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep" }, - Network accessline 193
"url": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep" - Network accessline 195
{ "title": "Leadership Principles", "url": "https://www.amazon.jobs/content/en/our-workplace/leadership-principles" - Network accessline 196
"title": "Leadership Principles", "url": "https://www.amazon.jobs/content/en/our-workplace/leadership-principles" } - Network accessline 197
"url": "https://www.amazon.jobs/content/en/our-workplace/leadership-principles" - Network accessline 202
"text": "SDE II steps: application, an online assessment (90 minutes for two coding questions, 20 minutes of system design scenarios, an 8-minute Work Style Survey), then the interview loop; the outcome usually comes within 5 business days. - Network accessline 203
"kind": "official", "source": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep" }, - Network accessline 204
"source": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep" - Network accessline 207
"text": "Loop: four 55-minute interviews with members of the software development community; each interviewer assesses different Leadership Principles and usually asks two or three behavioral questions.", "kind": "official", "sour - Network accessline 208
"kind": "official", "source": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep" }, - Network accessline 209
"source": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep" - Network accessline 212
"text": "Coding: syntactically correct code, no pseudocode; scalable, robust and well tested, with edge cases. Expect at least one system design question.", "kind": "official", "source": "https://www.amazon.jobs/content/en/how-we- - Network accessline 213
"kind": "official", "source": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep" }, - Network accessline 214
"source": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep" - Network accessline 217
"text": "Behavioral answers in STAR format with metrics where you have them; prepare examples of risk taking, success, failure and growth.", "kind": "official", "source": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-inte - Network accessline 218
"kind": "official", "source": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep" }, - Network accessline 219
"source": "https://www.amazon.jobs/content/en/how-we-hire/sde-ii-interview-prep" - Network accessline 222
"text": "Amazon lists 16 Leadership Principles, from Customer Obsession and Ownership to Success and Scale Bring Broad Responsibility.", "kind": "official", "source": "https://www.amazon.jobs/content/en/our-workplace/leadership-pr - Network accessline 223
"kind": "official", "source": "https://www.amazon.jobs/content/en/our-workplace/leadership-principles" }, - Network accessline 224
"source": "https://www.amazon.jobs/content/en/our-workplace/leadership-principles" - Network accessline 227
"text": "Amazon's hiring pages name Bar Raisers as part of the process.", "kind": "official", "source": "https://www.amazon.jobs/content/en/how-we-hire" - Network accessline 228
"kind": "official", "source": "https://www.amazon.jobs/content/en/how-we-hire" }, - Network accessline 229
"source": "https://www.amazon.jobs/content/en/how-we-hire"
skills/hunter-resume/ats.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/hunter-resume/examples/google-swe-jd.md
- Network accessline 4
<!-- Example job description for hunter-skill. Written for this pack in the format of Google's public job postings (Minimum qualifications, Preferred qualifications, About the job, Responsibilities). The format was checked on 2026-10-15 aga
skills/hunter-salary/SKILL.md
- Access to secretsline 68
[Credential-related source omitted]
skills/hunter-salary/bench.example.json
- Network accessline 5
"kind": "base", "source": "U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics (OEWS), May 2025 estimates, BLS Public Data API", "url": "https://api.bls.gov/publicAPI/v2/timeseries/data/OEUM0041940000000 - Network accessline 6
"source": "U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics (OEWS), May 2025 estimates, BLS Public Data API", "url": "https://api.bls.gov/publicAPI/v2/timeseries/data/OEUM004194000000015125213", "as_o - Network accessline 7
"url": "https://api.bls.gov/publicAPI/v2/timeseries/data/OEUM004194000000015125213", - Network accessline 30
"kind": "base", "source": "U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics (OEWS), May 2025 estimates, BLS Public Data API", "url": "https://api.bls.gov/publicAPI/v2/timeseries/data/OEUN0000000000000 - Network accessline 31
"source": "U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics (OEWS), May 2025 estimates, BLS Public Data API", "url": "https://api.bls.gov/publicAPI/v2/timeseries/data/OEUN000000000000015125213", "as_o - Network accessline 32
"url": "https://api.bls.gov/publicAPI/v2/timeseries/data/OEUN000000000000015125213",
skills/hunter-salary/offer.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 48
BLS_API = "https://api.bls.gov/publicAPI/v2/timeseries/data/" - Network accessline 49
UA = "hunter-skill/1.0 (+https://github.com/Jakeschincariol/hunter-skill)" - Network accessline 299
if key: body["registrationkey"] = key req = urllib.request.Request(BLS_API, data=json.dumps(body).encode("utf-8"), - Network accessline 300
body["registrationkey"] = key req = urllib.request.Request(BLS_API, data=json.dumps(body).encode("utf-8"), headers={"Content-Type": "application/json", "User-Agent": UA}) - Network accessline 301
req = urllib.request.Request(BLS_API, data=json.dumps(body).encode("utf-8"), - Network accessline 302
headers={"Content-Type": "application/json", "User-Agent": UA}) with urllib.request.urlopen(req, timeout=30) as r: return json.load(r) - Network accessline 303
with urllib.request.urlopen(req, timeout=30) as r: - Access to secretsline 533
[Credential-related source omitted]
skills/hunter-scout/boards.json
- Network accessline 3
"verified": "2026-10-15", "endpoints": { "greenhouse": "https://boards-api.greenhouse.io/v1/boards/<token>/jobs (detail: /jobs/<id>)", - Network accessline 4
"endpoints": { "greenhouse": "https://boards-api.greenhouse.io/v1/boards/<token>/jobs (detail: /jobs/<id>)", "lever": "https://api.lever.co/v0/postings/<token>?mode=json", - Network accessline 5
"greenhouse": "https://boards-api.greenhouse.io/v1/boards/<token>/jobs (detail: /jobs/<id>)", - Network accessline 6
"lever": "https://api.lever.co/v0/postings/<token>?mode=json", - Network accessline 7
"ashby": "https://api.ashbyhq.com/posting-api/job-board/<token>?includeCompensation=true", - Network accessline 13
"company": "Google", "type": "manual", "careers": "https://www.google.com/about/careers/applications/jobs/results", - Network accessline 14
"type": "manual", "careers": "https://www.google.com/about/careers/applications/jobs/results", "why_manual": "No public JSON feed, and google.com/robots.txt disallows crawling paged job results (/about/careers/applications/jobs/result - Network accessline 15
"careers": "https://www.google.com/about/careers/applications/jobs/results", - Network accessline 20
"company": "Meta", "type": "manual", "careers": "https://www.metacareers.com/jobs", - Network accessline 21
"type": "manual", "careers": "https://www.metacareers.com/jobs", "why_manual": "metacareers.com/robots.txt points to Meta's automated data collection terms, which require written permission for automated collection.", - Network accessline 22
"careers": "https://www.metacareers.com/jobs", - Network accessline 32
"site": "NVIDIAExternalCareerSite", "jobs_seen": 1731, "careers": "https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite", - Network accessline 33
"jobs_seen": 1731, "careers": "https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite", "terms": "robots.txt on nvidia.wd5.myworkdayjobs.com allows /NVIDIAExternalCareerSite/ and does not disallow the /wday/cxs/ job search the - Network accessline 34
"careers": "https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite", - Network accessline 42
"domain": "netflix.com", "jobs_seen": 154, "careers": "https://explore.jobs.netflix.net/careers", - Network accessline 43
"jobs_seen": 154, "careers": "https://explore.jobs.netflix.net/careers", "terms": "explore.jobs.netflix.net/robots.txt explicitly allows /api/apply, the job search this tool reads." - Network accessline 44
"careers": "https://explore.jobs.netflix.net/careers", - Network accessline 48
"company": "Amazon", "type": "manual", "careers": "https://www.amazon.jobs/en/search?base_query=software+engineer", - Network accessline 49
"type": "manual", "careers": "https://www.amazon.jobs/en/search?base_query=software+engineer", "why_manual": "amazon.jobs links Amazon's Conditions of Use, whose license excludes data mining, robots and similar data gathering tools, s - Network accessline 50
"careers": "https://www.amazon.jobs/en/search?base_query=software+engineer",
skills/hunter-scout/jobs.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 15
Sources (public JSON, one request per board plus a few job details): Greenhouse https://boards-api.greenhouse.io/v1/boards/<token>/jobs - Network accessline 16
Greenhouse https://boards-api.greenhouse.io/v1/boards/<token>/jobs - Network accessline 17
Lever https://api.lever.co/v0/postings/<token>?mode=json - Network accessline 18
Ashby https://api.ashbyhq.com/posting-api/job-board/<token>?includeCompensation=true - Network accessline 42
HERE = os.path.dirname(os.path.abspath(__file__)) BOARDS = os.path.join(HERE, "boards.json") UA = "hunter-skill/1.0 (job scout; +https://github.com/Jakeschincariol/hunter-skill)" - Network accessline 43
BOARDS = os.path.join(HERE, "boards.json") UA = "hunter-skill/1.0 (job scout; +https://github.com/Jakeschincariol/hunter-skill)" AUTO = ("greenhouse", "lever", "ashby", "workday", "eightfold") - Network accessline 44
UA = "hunter-skill/1.0 (job scout; +https://github.com/Jakeschincariol/hunter-skill)" - Network accessline 48
GH_LIST = "https://boards-api.greenhouse.io/v1/boards/%s/jobs" - Network accessline 49
GH_JOB = "https://boards-api.greenhouse.io/v1/boards/%s/jobs/%s" - Network accessline 50
GH_BOARD = "https://boards-api.greenhouse.io/v1/boards/%s" - Network accessline 51
LEVER = "https://api.lever.co/v0/postings/%s?mode=json" - Network accessline 52
ASHBY = "https://api.ashbyhq.com/posting-api/job-board/%s?includeCompensation=true" - Network accessline 53
WORKDAY = "https://%s/wday/cxs/%s/%s/jobs" - Network accessline 54
EIGHTFOLD = "https://%s/api/apply/v2/jobs?domain=%s&query=%s&start=%d&num=%d" - Network accessline 73
"pennsylvania tennessee texas utah vermont virginia washington wisconsin wyoming").split() US_CODES = ("AL AK AZ AR CA CO CT DE FL GA HI ID IL IN IA KS KY LA ME MD MA MI MN MS MO MT NE NV NH NJ NM " "NY NC ND OH OK OR PA RI SC S - Network accessline 74
US_CODES = ("AL AK AZ AR CA CO CT DE FL GA HI ID IL IN IA KS KY LA ME MD MA MI MN MS MO MT NE NV NH NJ NM " "NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY DC").split() NON_US = ("europe", "emea", "apac", "united kingd - Network accessline 75
"NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY DC").split() - Network accessline 226
data = json.dumps(body).encode("utf-8") headers["Content-Type"] = "application/json" req = urllib.request.Request(url, data=data, headers=headers) - Network accessline 227
headers["Content-Type"] = "application/json" req = urllib.request.Request(url, data=data, headers=headers) try: - Network accessline 228
req = urllib.request.Request(url, data=data, headers=headers) - Network accessline 229
try: with urllib.request.urlopen(req, timeout=self.timeout) as resp: raw = resp.read() - Network accessline 230
with urllib.request.urlopen(req, timeout=self.timeout) as resp: - Network accessline 336
def from_workday(board, data, today): out = [] base = "https://%s/%s" % (board["host"], board["site"]) - Network accessline 337
out = [] base = "https://%s/%s" % (board["host"], board["site"]) for p in data.get("jobPostings", []): - Network accessline 338
base = "https://%s/%s" % (board["host"], board["site"]) - Network accessline 358
remote = str(p.get("work_location_option") or "") == "remote" or "remote" in loc.lower() url = p.get("canonicalPositionUrl") or "https://%s/careers/job/%s" % (board["host"], p.get("id")) - Network accessline 359
url = p.get("canonicalPositionUrl") or "https://%s/careers/job/%s" % (board["host"], p.get("id"))
skills/hunter-tracker/pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Declared and executed: 7 facts
- Contacts data.sec.gov, which SKILL.md, the README and the plugin manifest do not mentionhunter-research/brief.py:44High confidence
- Contacts www.sec.gov, which SKILL.md, the README and the plugin manifest do not mentionhunter-research/brief.py:45High confidence
- Contacts github.com, which SKILL.md, the README and the plugin manifest do not mentionhunter-research/brief.py:48High confidence
- Contacts github.com, which SKILL.md, the README and the plugin manifest do not mentionhunter-salary/offer.py:49High confidence
- Contacts github.com, which SKILL.md, the README and the plugin manifest do not mentionhunter-scout/jobs.py:44High confidence
- Contacts api.lever.co, which SKILL.md, the README and the plugin manifest do not mentionhunter-scout/jobs.py:51High confidence
- Contacts api.ashbyhq.com, which SKILL.md, the README and the plugin manifest do not mentionhunter-scout/jobs.py:52High confidence
Signal confidence: 77 facts
- Executable script in code of a file that runshunter-apply/answers.py:1High confidence
- Network access in prose of a file that runshunter-apply/answers.py:11Low confidence
- Network access in prose of a file that runshunter-apply/answers.py:12Low confidence
- Network access in prose of a file that runshunter-apply/answers.py:13Low confidence
- Network access in code of the SKILL.mdhunter-connector/SKILL.md:60High confidence
- Network access in code of the SKILL.mdhunter-connector/SKILL.md:61High confidence
- Executable script in code of a file that runshunter-connector/emails.py:1High confidence
- Executable script in code of a file that runshunter-connector/outreach.py:1High confidence
- Executable script in code of a file that runshunter-cover/cover.py:1High confidence
- Executable script in code of a file that runshunter-followup/followup.py:1High confidence
- Network access in code of the SKILL.mdhunter-interview/SKILL.md:103High confidence
- Network access in code of the SKILL.mdhunter-interview/SKILL.md:104High confidence
- Executable script in code of a file that runshunter-interview/star.py:1High confidence
- Executable script in code of a file that runshunter-linkedin/profile.py:1High confidence
- Network access in code of the SKILL.mdhunter-research/SKILL.md:72High confidence
- Network access in code of the SKILL.mdhunter-research/SKILL.md:73High confidence
- Executable script in code of a file that runshunter-research/brief.py:1High confidence
- Network access in prose of a file that runshunter-research/brief.py:10Low confidence
- Network access in prose of a file that runshunter-research/brief.py:11Low confidence
- Network access in prose of a file that runshunter-research/brief.py:16Low confidence
- Network access in prose of a file that runshunter-research/brief.py:17Low confidence
- Network access in prose of a file that runshunter-research/brief.py:18Low confidence
- Network access in code of a file that runshunter-research/brief.py:42High confidence
- Network access in code of a file that runshunter-research/brief.py:43High confidence
- Network access in code of a file that runshunter-research/brief.py:44High confidence
- Network access in code of a file that runshunter-research/brief.py:45High confidence
- Network access in code of a file that runshunter-research/brief.py:46High confidence
- Network access in code of a file that runshunter-research/brief.py:47High confidence
- Network access in code of a file that runshunter-research/brief.py:48High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuehunter-research/brief.py:86Low confidence
- Network access in code of a file that runshunter-research/brief.py:114High confidence
- Network access in code of a file that runshunter-research/brief.py:115High confidence
- Network access in code of a file that runshunter-research/brief.py:116High confidence
- Network access in code of a file that runshunter-research/brief.py:117High confidence
- Network access in code of a file that runshunter-research/brief.py:118High confidence
- Network access in code of a file that runshunter-research/brief.py:119High confidence
- Executable script in code of a file that runshunter-resume/ats.py:1High confidence
- Network access in prose of documentationhunter-resume/examples/google-swe-jd.md:4Low confidence
- Access to secrets in prose of the SKILL.mdhunter-salary/SKILL.md:68Low confidence
- Executable script in code of a file that runshunter-salary/offer.py:1High confidence
- Network access in code of a file that runshunter-salary/offer.py:48High confidence
- Network access in code of a file that runshunter-salary/offer.py:49High confidence
- Network access in code of a file that runshunter-salary/offer.py:299High confidence
- Network access in code of a file that runshunter-salary/offer.py:300High confidence
- Network access in code of a file that runshunter-salary/offer.py:301High confidence
- Network access in code of a file that runshunter-salary/offer.py:302High confidence
- Network access in code of a file that runshunter-salary/offer.py:303High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuehunter-salary/offer.py:533Low confidence
- Executable script in code of a file that runshunter-scout/jobs.py:1High confidence
- Network access in prose of a file that runshunter-scout/jobs.py:15Low confidence
and 27 more
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Native | Manifest .claude-plugin/marketplace.json | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md skills/hunter-apply/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/hunter-apply/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/hunter-apply/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/hunter-apply/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/hunter-apply/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.
