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extruct-ai/gtm-skills
History
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Verified commit 34406cbf36eaf7441af277ce9b33d9ab913572f6
git clone --filter=blob:none --no-checkout https://github.com/extruct-ai/gtm-skills.git .hotskills/extruct-ai-gtm-skills && git -C .hotskills/extruct-ai-gtm-skills checkout --detach 34406cbf36eaf7441af277ce9b33d9ab913572f6
# Copy each selected skill directory from .hotskills/extruct-ai-gtm-skills into .claude/skills/<skill-name>/Heat
1.8- 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
- account-research
skills/account-research - atomic-message
skills/atomic-message - campaign-sending
skills/campaign-sending - competitor-monitoring
skills/competitor-monitoring - context-building
skills/context-building - email-generation
skills/email-generation - email-prompt-building
skills/email-prompt-building - email-response-simulation
skills/email-response-simulation - email-search
skills/email-search - email-verification
skills/email-verification - enrichment-design
skills/enrichment-design - extruct-api
skills/extruct-api - hypothesis-building
skills/hypothesis-building - inbox-reply
skills/inbox-reply - list-building
skills/list-building - list-enrichment
skills/list-enrichment - list-segmentation
skills/list-segmentation - market-research
skills/market-research - people-search
skills/people-search - post-engagers
skills/post-engagers - table-creation
skills/table-creation
How it works
account-research
When your agent uses it
Deep-research a single target account into a decision-ready dossier: the entity tree, the buying units and decision-makers, live signals (open/closed roles, leadership moves, news, tech stack), and the entry angle. Powered by Extruct Deep Research — ONE cited report per account, no table/column setup. Optionally enrich buyer emails via email-search. Use to prep for a sales conversation, build a buyer map, find who to sell to at a company, or surface why-now signals for ONE account (not a list). Triggers on: "research this account", "account research", "buyer map", "who do we sell to at", "prep for the meeting with", "deep dive on", "map the org at", "find decision makers at", "why now at", "research [company] before the call".
What it loads into context
Frontmatter
190 tokensAlways available to the agent
SKILL.md
1,520 tokensWhen the skill is activated
Reference documents
1,980 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Related Skills
- Engine
- Inputs
- Workflow
- Ground Rules
- Reference
Files
../../.claude-plugin/marketplace.jsonasset362 bytes../../.claude-plugin/plugin.jsonasset233 bytesSKILL.mdcore6,549 bytesSKILL.mdcore6,549 bytesSKILL.mdcore6,549 bytesSKILL.mdcore6,549 bytesSKILL.mdcore6,549 bytesreferences/deep-research-engine.mddoc4,889 bytesreferences/methodology.mddoc2,689 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubatomic-message
When your agent uses it
Draft ONE cold-outreach message from a signal + persona + channel: a single cold email, a LinkedIn note, or a follow-up. Generates one primary draft plus 1-2 variants, then runs a self-check lint. Reads voice, sender identity, positioning, proof, and banned words from the company context file. This is the single-message craft engine; it does NOT build sequences, cadences, or multi-touch arcs (email-prompt-building owns sequencing). Triggers on: "write a message", "draft a message", "atomic message", "write a single email", "one-off email", "write a linkedin message", "linkedin note", "linkedin dm", "write a follow-up", "follow-up message", "draft outreach", "rewrite this message", "write a cold email" (one message, not a sequence).
What it loads into context
Frontmatter
196 tokensAlways available to the agent
SKILL.md
887 tokensWhen the skill is activated
Reference documents
1,497 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Inputs
- Workflow
- Reference
Files
SKILL.mdcore3,808 bytesreferences/craft.mddoc6,440 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcampaign-sending
When your agent uses it
Upload finalized emails for sequencing and sending. Maps fields to lead schema, creates or finds campaigns, uploads leads with dedup, and provides a pre-send verification checklist. Triggers on: "upload to instantly", "run instantly", "send emails", "instantly campaign", "push to instantly", "start campaign", "load into instantly".
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
1,012 tokensWhen the skill is activated
Reference documents
1,463 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Environment
- Workflow
- After Sending: Feedback Loop
- API Reference
Files
SKILL.mdcore4,673 bytesreferences/instantly-api.mddoc5,170 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcompetitor-monitoring
When your agent uses it
Set up and run competitive intelligence monitoring. Discover competitors via user input, lookalike search, web research, or G2/review sites, then create an Extruct company table with research columns for blog, social media, news, key people, and business model tracking. Re-run columns to refresh monitoring data. Triggers on: "competitor monitoring", "track competitors", "competitive intelligence", "monitor competitors", "competitor analysis", "competitive landscape", "who are my competitors", "competitor tracking".
What it loads into context
Frontmatter
122 tokensAlways available to the agent
SKILL.md
2,668 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
- Related Skills
- Extruct API Operations
- Inputs
- Workflow
- Output
- Next Steps After Setup
Files
SKILL.mdcore11,603 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcontext-building
When your agent uses it
Build and maintain a global company context file that all other GTM skills read from. Captures product info, voice rules, ICP, win cases, proof library, campaign history, hypotheses, and DNC lists. Supports four modes: create (new context), update (append to existing), call recording capture (extract signals from transcripts), and feedback loop (import campaign results). Triggers on: "company context", "update context", "build context", "ICP", "win cases", "campaign history", "call recording", "feedback loop", "DNC list".
What it loads into context
Frontmatter
135 tokensAlways available to the agent
SKILL.md
1,338 tokensWhen the skill is activated
Reference documents
1,117 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Context File Location
- Modes
- Cross-Skill References
- Reference
Files
SKILL.mdcore5,935 bytesSKILL.mdcore5,935 bytesreferences/context-schema.mddoc4,447 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubemail-generation
When your agent uses it
Render a cold-outreach email sequence from a fixed template set and a contact CSV whose input fields are already filled. Deterministic — variant routing + placeholder substitution, no per-row LLM call. The render contract (newline model, per-target escaping, operation order) is the core of this skill. Triggers on: "generate emails", "email generation", "run emails", "render emails", "create emails", "email pipeline", "generate outreach".
What it loads into context
Frontmatter
111 tokensAlways available to the agent
SKILL.md
1,580 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
- Architectural Principle
- Inputs Required
- The Render Contract
- Workflow
- Feedback Loop
- Suppression
- No template yet?
Scripts executed separately
references/render_emails.py
Files
SKILL.mdcore6,695 bytesSKILL.mdcore6,695 bytesreferences/render_emails.pyruns5,346 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubemail-prompt-building
When your agent uses it
Design a cold-outreach email sequence as a FIXED TEMPLATE SET — real, human-approved copy with {{input_field}} placeholders and variant-routing rules. Reads the company context file and campaign research, explores angles, and commits to copy at design time. The output is rendered later by the email-generation skill via deterministic substitution — no per-row LLM. Triggers on: "cold email", "outreach prompt", "email campaign", "new vertical email", "draft email prompt", "email sequence", "email template".
What it loads into context
Frontmatter
126 tokensAlways available to the agent
SKILL.md
2,227 tokensWhen the skill is activated
Reference documents
3,432 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Architectural Principle
- What This Skill Reads (inputs)
- What This Skill Produces (output)
- Building a Campaign Template Set
- Anti-patterns — what NOT to write into any touch
- Sequence shape (defaults)
- References
Files
SKILL.mdcore10,289 bytesSKILL.mdcore10,289 bytesSKILL.mdcore10,289 bytesSKILL.mdcore10,289 bytesSKILL.mdcore10,289 bytesSKILL.mdcore10,289 bytesreferences/email-structures.mddoc6,194 bytesreferences/example-sequence.mddoc2,480 bytesreferences/prompt-patterns.mddoc6,075 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubemail-response-simulation
When your agent uses it
Deep persona simulation and skeptical buyer review for cold emails. Builds a full prospect "world" from LinkedIn + company data, defines their professional reality (KPIs, pain points, inbox behavior), then runs a skeptical buyer roast — emotional reaction first, business evaluation second. One prospect at a time, Tier 1 only. Triggers on: "review email", "copy feedback", "email feedback", "would they reply", "persona review", "check this email", "review this draft", "roast this email", "skeptical buyer".
What it loads into context
Frontmatter
132 tokensAlways available to the agent
SKILL.md
2,568 tokensWhen the skill is activated
Reference documents
1,702 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Related Skills
- Scraping Tell Detection
- When to Use
- Environment
- Inputs
- Workflow
- Running One by One vs. Template Training
- Guidelines
- Reference
Files
SKILL.mdcore11,281 bytesreferences/persona-review-template.mddoc6,981 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubemail-search
When your agent uses it
Get verified emails and phones for contacts found by people-search. Takes LinkedIn profiles from the Extruct people table and enriches them via contact enrichment providers like Prospeo or Fullenrich. Supports single-provider and waterfall modes. Outputs a contact CSV ready for email-generation. Triggers on: "get emails", "find emails", "enrich contacts", "email finder", "get phone numbers", "enrich people", "contact enrichment", "verify emails", "email enrichment".
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
1,183 tokensWhen the skill is activated
Reference documents
4,393 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Related Skills
- Extruct API Operations
- Inputs
- Choosing a Provider
- Workflow
- API References
Files
SKILL.mdcore5,143 bytesreferences/fullenrich-api.mddoc8,966 bytesreferences/prospeo-api.mddoc6,998 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubemail-verification
When your agent uses it
Validate email addresses before campaign sending. Takes a contact CSV, validates each email via a verification provider, removes invalid/do_not_mail/abuse/catch-all/unknown addresses, and optionally cleans them from sequencer campaigns. Outputs a verified CSV ready for campaign-sending. Fits between email-generation and campaign-sending in the pipeline. Triggers on: "verify emails", "validate emails", "email verification", "clean emails", "check emails before sending", "remove bad emails", "email hygiene".
What it loads into context
Frontmatter
124 tokensAlways available to the agent
SKILL.md
1,012 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
- Environment
- Inputs
- Workflow
- Output
- API References
Files
SKILL.mdcore4,513 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubenrichment-design
When your agent uses it
Design enrichment columns that bridge research hypotheses to list enrichment. Two modes: segmentation (columns that score hypothesis fit per company) and personalization (columns for company-specific hooks). Interactive column design with the user. Outputs ready-to-run column_configs for list-enrichment. Triggers on: "data points", "enrichment columns", "column design", "what to research", "data point builder", "build columns", "segmentation columns", "personalization columns".
What it loads into context
Frontmatter
111 tokensAlways available to the agent
SKILL.md
1,201 tokensWhen the skill is activated
Reference documents
1,623 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Two Modes
- Interactive Column Design
- Column Design Guidelines
- Reference Library
- Output Handoff
Files
SKILL.mdcore5,081 bytesreferences/data-point-library.mddoc6,357 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubextruct-api
When your agent uses it
Run explicit Extruct API tasks through the bundled Extruct CLI. Covers Deep Search, semantic search, lookalike search, company and people tables, column operations, enrichment, and contact finding.
What it loads into context
Frontmatter
47 tokensAlways available to the agent
SKILL.md
5,687 tokensWhen the skill is activated
Reference documents
10,002 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Source Of Truth
- Resolve The Bundled CLI First
- Core Workflow
- Resolve Extruct Identifiers
- Choose The Right Company-Finding Path
- Operate Existing Tables
- Research Companies With Tables
- Find People At Companies
- Research People
- Troubleshooting And Recovery
- Global Flags
- References
Files
SKILL.mdcore23,548 bytesSKILL.mdcore23,548 bytesSKILL.mdcore23,548 bytesSKILL.mdcore23,548 bytesSKILL.mdcore23,548 bytesagents/openai.yamlasset350 bytesassets/logo.svgasset684 bytesreferences/column-guide.mddoc28,737 bytesreferences/finding-companies.mddoc3,373 bytesreferences/finding-people-at-companies.mddoc2,993 bytesreferences/researching-companies.mddoc2,970 bytesreferences/researching-people.mddoc3,684 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhypothesis-building
When your agent uses it
Generate testable pain hypotheses from the company context file (ICP, win cases, product knowledge) and user input. Fast, no API keys needed — pure reasoning. Outputs a hypothesis set with search angles that directly guide list-building queries. Sits between context-building and list-building. Triggers on: "build hypotheses", "hypothesis set", "pain hypotheses", "define hypotheses", "what pain points", "campaign angles", "search angles", "refine hypotheses".
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
1,384 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
- When to Use
- Inputs
- Output
- Workflow
- Output Format
- Refine Mode
- Key Difference from market-research
- Output Consumers
Files
SKILL.mdcore6,248 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubinbox-reply
When your agent uses it
Manage and reply to lead responses in Instantly unibox. Fetches unread conversations, classifies reply intent, drafts contextual responses, and sends replies via Instantly API. Fits after campaign-sending in the GTM pipeline. Triggers on: "reply to leads", "inbox replies", "instantly inbox", "unibox", "respond to replies", "manage replies", "instantly replies", "check inbox", "lead replies", "answer leads".
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
1,126 tokensWhen the skill is activated
Reference documents
1,074 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Environment
- Inputs
- Workflow
- Reference
Files
SKILL.mdcore4,919 bytesreferences/instantly-inbox-api.mddoc3,856 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHublist-building
When your agent uses it
Build targeted company lists for outbound campaigns using Extruct. Use when the user wants to: (1) find companies matching an ICP, (2) build a prospect or outbound list, (3) search for companies by description, (4) discover companies in a market segment, (5) create a target account list, (6) research a competitive landscape, (7) find lookalike companies from a seed list. Triggers on: "find companies", "build a list", "company search", "prospect list", "target accounts", "outbound list", "discover companies", "ICP search", "lookalike search", "seed company".
What it loads into context
Frontmatter
159 tokensAlways available to the agent
SKILL.md
1,244 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
- Extruct API Operations
- Decision Tree
- Before You Start
- Method 1: Lookalike Search
- Method 2: Semantic Search — Fast, Broad
- Method 3: Deep Search — Deep, Qualified
- Upload to Table
- Re-run After Enrichment
- Result Size Guidance
- Workflow
Files
SKILL.mdcore5,348 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHublist-enrichment
When your agent uses it
Add research-powered enrichment columns to Extruct company tables. Use when the user wants to add enrichment columns (e.g. funding, verticals, tech stack) to an existing Extruct table, run column configs from enrichment-design, or monitor enrichment progress. Triggers on: "enrich", "add column", "add data point", "research column", "enrich table", "enrichment", "add a field", "run enrichment", "monitor enrichment".
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
903 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
- Extruct API Operations
- Workflow
Files
SKILL.mdcore3,732 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHublist-segmentation
When your agent uses it
Take an enriched Extruct table and a hypothesis set, then segment companies by hypothesis fit and assign tiers based on data richness and signal strength. Outputs a tiered, segmented list ready for email generation. Triggers on: "segment", "tier", "segment companies", "tier companies", "prioritize list", "segment and tier", "tiering", "which companies first".
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,167 tokensWhen the skill is activated
Reference documents
679 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Inputs
- Extruct API Operations
- Workflow
- Reference
Files
SKILL.mdcore4,832 bytesreferences/tiering-framework.mddoc2,537 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmarket-research
When your agent uses it
Research a target vertical's pain points using deep research APIs and distill findings into a numbered hypothesis set. Pure industry education tool — decoupled from email generation. Use when the user wants to understand a market before outreach, form hypotheses about a vertical, or build an industry knowledge base. Triggers on: "deep research", "hypothesis set", "research pain points", "research vertical", "sourcing research", "pain mapping", "industry problems", "market research", "educate me on".
What it loads into context
Frontmatter
123 tokensAlways available to the agent
SKILL.md
1,490 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
- Environment
- Workflow
- Output Consumers
- Relationship to hypothesis-building
- When NOT to Use This Skill
Files
SKILL.mdcore6,744 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpeople-search
When your agent uses it
Find LinkedIn profiles of decision makers at target companies using Extruct's company_people_finder. Takes a company table, adds a people finder column, and produces a linked child people table with names, roles, and LinkedIn URLs. No external API credits — uses Extruct's index only. Triggers on: "find linkedin", "find people", "find contacts", "find decision makers", "people search", "linkedin search", "who to contact", "find profiles".
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
1,251 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
- Related Skills
- Extruct API Operations
- Inputs
- Workflow
- Output
- Key Table IDs to Pass Forward
Files
SKILL.mdcore5,299 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpost-engagers
When your agent uses it
Extract people who engage (comment, react, repost) on a LinkedIn post, enrich their profile / email / company data, and land them in a local SQLite CRM (content.db → crm.db) as the source of truth. Extruct people-table upload is optional. Triggers on: "post engagers", "linkedin engagers", "who commented on", "who liked", "who reacted", "linkedin post engagers", "scrape post", "extract engagers", "post commenters".
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
2,016 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
- Assumed repo layout
- Related skills
- Inputs
- Workflow
- Verification checklist
- Tips
- Output
Files
SKILL.mdcore7,917 bytesSKILL.mdcore7,917 bytesSKILL.mdcore7,917 bytesSKILL.mdcore7,917 bytesSKILL.mdcore7,917 bytesSKILL.mdcore7,917 bytesSKILL.mdcore7,917 bytesSKILL.mdcore7,917 bytesSKILL.mdcore7,917 bytesSKILL.mdcore7,917 bytesSKILL.mdcore7,917 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtable-creation
When your agent uses it
Create an Extruct company table from user-provided data, upload rows, and optionally add enrichment columns. Handles the full flow: parse input (CSV, pasted list, or structured data), create or reuse a table, upload domains in batches, add agent columns, and trigger enrichment. Triggers on: "create table", "upload companies", "add to extruct", "new extruct table", "import companies", "upload list to extruct".
What it loads into context
Frontmatter
109 tokensAlways available to the agent
SKILL.md
976 tokensWhen the skill is activated
Reference documents
903 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Extruct API Operations
- Workflow
- Input Parsing Patterns
- Common Pitfalls
Files
../list-enrichment/SKILL.mddoc3,732 bytesSKILL.mdcore4,045 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubSkills included
- account-research
- atomic-message
- campaign-sending
- competitor-monitoring
- context-building
- email-generation
- email-prompt-building
- email-response-simulation
- email-search
- email-verification
- enrichment-design
- extruct-api
- hypothesis-building
- inbox-reply
- list-building
- list-enrichment
- list-segmentation
- market-research
- people-search
- post-engagers
- table-creation
Repository facts
- License
- No license detected
- Created
- Mar 1, 2026
- Last pushed
- Sep 28, 2026
- Forks
- 25
- Open issues
- 0
- Project activity
- Active
Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 5, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 25 notes
skills/account-research/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/account-research/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/account-research/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/account-research/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/context-building/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/email-generation/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/email-prompt-building/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/email-prompt-building/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/email-prompt-building/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/email-prompt-building/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/email-prompt-building/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/extruct-api/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/extruct-api/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/extruct-api/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/extruct-api/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/post-engagers/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/post-engagers/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/post-engagers/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/post-engagers/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/post-engagers/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- No dangerous shell commands
- 77 network referencesin 13 files: email-search/references/fullenrich-api.md:5 email-search/references/prospeo-api.md:5 extruct-api/SKILL.md:18 and 10 more
- No hidden or obfuscated text
- No instruction overrides
- 1 executable scriptin 1 file: email-generation/references/render_emails.py:1
- 9 references to secretsin 6 files: account-research/references/deep-research-engine.md:17 email-search/references/prospeo-api.md:6 campaign-sending/references/instantly-api.md:4 and 3 more
- No requests to vote in Hot
Show all 87 signals by file
skills/account-research/references/deep-research-engine.md
- Network accessline 16
Deep Research bills **1 credit per executed agent** (max 50 at `xhigh`); failed tasks are refunded. The token lives in the repo `.env` as `EXTRUCT_API_TOKEN` (the `~/.ext` wrapper reads it but only execs the CLI, so for raw curl read it you - Access to secretsline 17
[Credential-related source omitted] - Network accessline 18
CLI, so for raw curl read it yourself). - Network accessline 20
## 1. Endpoints, auth, params - **Create:** `POST https://api.extruct.ai/v1/deep_research_tasks` - Network accessline 21
- **Create:** `POST https://api.extruct.ai/v1/deep_research_tasks` - Network accessline 22
- **Poll/Get:** `GET https://api.extruct.ai/v1/deep_research_tasks/{id}` - Network accessline 23
- **List:** `GET https://api.extruct.ai/v1/deep_research_tasks` - Access to secretsline 24
[Credential-related source omitted] - Network accessline 41
[Credential-related source omitted] - Access to secretsline 41
[Credential-related source omitted] - Network accessline 42
[Credential-related source omitted] - Network accessline 43
curl -s -X POST https://api.extruct.ai/v1/deep_research_tasks \ - Network accessline 52
[Credential-related source omitted] - Network accessline 53
[Credential-related source omitted] - Network accessline 54
[Credential-related source omitted]
skills/campaign-sending/references/instantly-api.md
skills/competitor-monitoring/SKILL.md
- Network accessline 249
| Output | Format | Location | |--------|--------|----------| | Competitor monitoring table | Extruct company table | `https://app.extruct.ai/tables/{table_id}` | - Network accessline 251
| Competitor monitoring table | Extruct company table | `https://app.extruct.ai/tables/{table_id}` |
skills/email-generation/references/render_emails.py
- Executable scriptline 1
Executable file
skills/email-search/references/fullenrich-api.md
- Network accessline 5
**Base URL:** `https://app.fullenrich.com/api/v2` - Network accessline 6
[Credential-related source omitted] - Access to secretsline 6
[Credential-related source omitted] - Network accessline 7
**API Key:** https://app.fullenrich.com/app/api - Network accessline 8
**Docs:** https://docs.fullenrich.com/api/v2 (index: https://docs.fullenrich.com/llms.txt) - Network accessline 57
"domain": "acme.com", "company_name": "Acme Corp", "linkedin_url": "https://www.linkedin.com/in/janedoe/", - Network accessline 58
"company_name": "Acme Corp", "linkedin_url": "https://www.linkedin.com/in/janedoe/", "enrich_fields": ["contact.work_emails"], - Network accessline 59
"linkedin_url": "https://www.linkedin.com/in/janedoe/", - Network accessline 104
"company_domain": "acme.com", "company_name": "Acme Corp", "professional_network_url": "https://www.linkedin.com/in/janedoe/" - Network accessline 105
"company_name": "Acme Corp", "professional_network_url": "https://www.linkedin.com/in/janedoe/" }, - Network accessline 106
"professional_network_url": "https://www.linkedin.com/in/janedoe/" - Network accessline 119
"headline": "VP of Sales at Acme Corp", "location": { "country": "United States", "country_code": "US", "city": "San Francisco" }, "social_profiles": { "professional_network": { "url": "https://www.linkedin.com/in/janedoe", - Network accessline 120
"location": { "country": "United States", "country_code": "US", "city": "San Francisco" }, "social_profiles": { "professional_network": { "url": "https://www.linkedin.com/in/janedoe", "handle": "janedoe" } }, "employment": { - Network accessline 121
"social_profiles": { "professional_network": { "url": "https://www.linkedin.com/in/janedoe", "handle": "janedoe" } }, - Network accessline 177
Text filters are lists of `{ "value", "exact_match", "exclude" }`; range filters (headcounts, founded years, years in role) are lists of `{ "min", "max", "exclude" }`. Accepted values: https://docs.fullenrich.com/api/v2/general/enums. `limi - Network accessline 201
"domain": "fullenrich.com", "company_name": "FullEnrich", "linkedin_url": "https://www.linkedin.com/in/demoge/" - Network accessline 202
"company_name": "FullEnrich", "linkedin_url": "https://www.linkedin.com/in/demoge/" } - Network accessline 203
"linkedin_url": "https://www.linkedin.com/in/demoge/"
skills/email-search/references/prospeo-api.md
- Network accessline 5
**Base URL:** `https://api.prospeo.io` - Access to secretsline 6
[Credential-related source omitted] - Network accessline 7
**Content-Type:** `application/json` **Method:** All endpoints use POST **API Key:** https://app.prospeo.io/api - Network accessline 9
**API Key:** https://app.prospeo.io/api - Network accessline 111
"last_name": "Doe", "full_name": "Jane Doe", "linkedin_url": "https://linkedin.com/in/janedoe", - Network accessline 112
"full_name": "Jane Doe", "linkedin_url": "https://linkedin.com/in/janedoe", "title": "VP of Sales", - Network accessline 113
"linkedin_url": "https://linkedin.com/in/janedoe", - Network accessline 121
"name": "Acme Corp", "website": "acme.com", "linkedin_url": "https://linkedin.com/company/acme", - Network accessline 122
"website": "acme.com", "linkedin_url": "https://linkedin.com/company/acme", "industry": "Software", - Network accessline 123
"linkedin_url": "https://linkedin.com/company/acme", - Network accessline 185
"last_name": "Doe", "full_name": "Jane Doe", "linkedin_url": "https://linkedin.com/in/janedoe", - Network accessline 186
"full_name": "Jane Doe", "linkedin_url": "https://linkedin.com/in/janedoe", "title": "VP of Sales", - Network accessline 187
"linkedin_url": "https://linkedin.com/in/janedoe", - Access to secretsline 205
[Credential-related source omitted] - Network accessline 227
}, { "linkedin_url": "https://linkedin.com/in/johndoe" - Network accessline 228
{ "linkedin_url": "https://linkedin.com/in/johndoe" } - Network accessline 229
"linkedin_url": "https://linkedin.com/in/johndoe"
skills/extruct-api/SKILL.md
- Network accessline 18
- https://www.extruct.ai/docs/api-reference/introduction - Network accessline 56
- https://www.extruct.ai/docs/api-reference/introduction - Network accessline 64
- if the user provides an Extruct table URL such as `https://app.extruct.ai/tables/<table_id>` or `http://app.extruct.ai/tables/<table_id>`, treat the path segment after `/tables/` as the table id - Network accessline 65
- if the user provides an Extruct task URL such as `https://app.extruct.ai/tasks/<task_id>` or `http://app.extruct.ai/tasks/<task_id>`, treat the path segment after `/tasks/` as the Deep Search task id - Network accessline 78
Add a funding column to http://app.extruct.ai/tables/0a1a669a-9a40-497c-bb00-d49dd8ee5b74 - Network accessline 98
Check results for https://app.extruct.ai/tasks/d530c3ad-626c-4d7b-ab15-181d4058e4f8 - Network accessline 258
- https://www.extruct.ai/docs/api-reference/introduction - Network accessline 328
{ "id":"<row_id>", "data":{"input":"https://extruct.ai"} - Network accessline 329
"id":"<row_id>", "data":{"input":"https://extruct.ai"} } - Network accessline 330
"data":{"input":"https://extruct.ai"} - Network accessline 558
"input":"Jane Doe, VP Sales, https://linkedin.com/in/jane-doe", - Network accessline 682
- `references/finding-people-at-companies.md`: branch from company tables into people workflows - `references/researching-people.md`: enrich standalone or generated people tables - official Extruct API reference: https://www.extruct.ai/docs - Network accessline 684
- official Extruct API reference: https://www.extruct.ai/docs/api-reference/introduction
skills/extruct-api/assets/logo.svg
- Network accessline 1
<svg width="30" height="30" viewBox="0 0 30 30" fill="none" xmlns="http://www.w3.org/2000/svg">
skills/extruct-api/references/column-guide.md
- Network accessline 360
{ "status": "mixed", "evidence_url": "https://company.example/careers", - Network accessline 361
"status": "mixed", "evidence_url": "https://company.example/careers", "notes": "Some roles are remote-friendly." - Network accessline 362
"evidence_url": "https://company.example/careers", - Network accessline 377
{ "remote_hiring_status": "mixed", "careers_page_url": "https://company.example/careers" - Network accessline 378
"remote_hiring_status": "mixed", "careers_page_url": "https://company.example/careers" } - Network accessline 379
"careers_page_url": "https://company.example/careers"
skills/extruct-api/references/researching-people.md
- Network accessline 56
"input":"Jane Doe, VP Sales, https://linkedin.com/in/jane-doe",
skills/inbox-reply/SKILL.md
skills/inbox-reply/references/instantly-inbox-api.md
skills/post-engagers/SKILL.md
- Network accessline 209
| Unified CRM view | `revops/db/crm.db` (`raw_content_*`, `stg_people`, `mart_people`) | | Attio records (optional) | Pushed via `crm_to_attio_content.py` | | Extruct people table (optional) | `https://app.extruct.ai/tables/{table_id}` | - Network accessline 211
| Extruct people table (optional) | `https://app.extruct.ai/tables/{table_id}` |
skills/table-creation/SKILL.md
- Network accessline 43
Ask the user: - **New table**: Delegate to the extruct-api skill to create a company table - **Existing table**: User provides table ID or URL (`https://app.extruct.ai/tables/{id}`) - Network accessline 45
- **Existing table**: User provides table ID or URL (`https://app.extruct.ai/tables/{id}`) - Network accessline 78
Provide: - Table URL: `https://app.extruct.ai/tables/{table_id}` - Network accessline 79
- Table URL: `https://app.extruct.ai/tables/{table_id}` - Network accessline 115
- **Domain format**: Strip protocol and trailing slash. `https://www.example.com/` → `example.com`
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.
Signal confidence: 86 facts
- Network access in prose of documentationaccount-research/references/deep-research-engine.md:16Low confidence
- Access to secrets in prose of documentationaccount-research/references/deep-research-engine.md:17Low confidence
- Network access in prose of documentationaccount-research/references/deep-research-engine.md:18Low confidence
- Network access in prose of documentationaccount-research/references/deep-research-engine.md:20Low confidence
- Network access in prose of documentationaccount-research/references/deep-research-engine.md:21Low confidence
- Network access in prose of documentationaccount-research/references/deep-research-engine.md:22Low confidence
- Network access in prose of documentationaccount-research/references/deep-research-engine.md:23Low confidence
- Access to secrets in prose of documentation: a variable, placeholder or call, not a stored valueaccount-research/references/deep-research-engine.md:24Low confidence
- Network access in code of documentationaccount-research/references/deep-research-engine.md:41Low confidence
- Access to secrets in code of documentationaccount-research/references/deep-research-engine.md:41Low confidence
- Network access in code of documentationaccount-research/references/deep-research-engine.md:42Low confidence
- Network access in code of documentationaccount-research/references/deep-research-engine.md:43Low confidence
- Network access in code of documentationaccount-research/references/deep-research-engine.md:52Low confidence
- Network access in code of documentationaccount-research/references/deep-research-engine.md:53Low confidence
- Network access in code of documentationaccount-research/references/deep-research-engine.md:54Low confidence
- Network access in prose of documentationcampaign-sending/references/instantly-api.md:3Low confidence
- Access to secrets in prose of documentationcampaign-sending/references/instantly-api.md:4Low confidence
- Network access in prose of the SKILL.mdcompetitor-monitoring/SKILL.md:249Low confidence
- Network access in prose of the SKILL.mdcompetitor-monitoring/SKILL.md:251Low confidence
- Executable script in prose of a file that runsemail-generation/references/render_emails.py:1Low confidence
- Network access in prose of documentationemail-search/references/fullenrich-api.md:5Low confidence
- Network access in prose of documentationemail-search/references/fullenrich-api.md:6Low confidence
- Access to secrets in prose of documentation: a variable, placeholder or call, not a stored valueemail-search/references/fullenrich-api.md:6Low confidence
- Network access in prose of documentationemail-search/references/fullenrich-api.md:7Low confidence
- Network access in prose of documentationemail-search/references/fullenrich-api.md:8Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:57Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:58Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:59Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:104Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:105Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:106Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:119Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:120Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:121Low confidence
- Network access in prose of documentationemail-search/references/fullenrich-api.md:177Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:201Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:202Low confidence
- Network access in code of documentationemail-search/references/fullenrich-api.md:203Low confidence
- Network access in prose of documentationemail-search/references/prospeo-api.md:5Low confidence
- Access to secrets in prose of documentation: a variable, placeholder or call, not a stored valueemail-search/references/prospeo-api.md:6Low confidence
- Network access in prose of documentationemail-search/references/prospeo-api.md:7Low confidence
- Network access in prose of documentationemail-search/references/prospeo-api.md:9Low confidence
- Network access in code of documentationemail-search/references/prospeo-api.md:111Low confidence
- Network access in code of documentationemail-search/references/prospeo-api.md:112Low confidence
- Network access in code of documentationemail-search/references/prospeo-api.md:113Low confidence
- Network access in code of documentationemail-search/references/prospeo-api.md:121Low confidence
- Network access in code of documentationemail-search/references/prospeo-api.md:122Low confidence
- Network access in code of documentationemail-search/references/prospeo-api.md:123Low confidence
- Network access in code of documentationemail-search/references/prospeo-api.md:185Low confidence
- Network access in code of documentationemail-search/references/prospeo-api.md:186Low confidence
and 36 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/account-research/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/account-research/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/account-research/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/account-research/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/account-research/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.
