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tamdogood/builder-essential-skills
A repository for skills that are essential to my daily work
History
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18skills in this repository
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Individual skills in this repository
- async-learning-teacher
skills/async-learning-teacher - build-scenario-tests
skills/build-scenario-tests - code-standards
skills/code-standards - create-marketing-kit
skills/create-marketing-kit - create-skill
skills/create-skill - lead
skills/lead - lead-research
skills/lead-research - make-playbook
skills/make-playbook - map-the-landscape
skills/map-the-landscape - name-your-business
skills/name-your-business - orwell-writing
skills/orwell-writing - paper-opportunity-radar
skills/paper-opportunity-radar - repo-system-map
skills/repo-system-map - run-smoke-tests
skills/run-smoke-tests - session-profiler
skills/session-profiler - top-one-percent
skills/top-one-percent - validate-market
skills/validate-market - write-blog
skills/write-blog
How it works
async-learning-teacher
When your agent uses it
Transform saved links, papers, articles, posts, videos, and reference collections into approachable AI teaching artifacts for later study. Use when a user wants to queue learning material, create a readable explanation from a source, teach a paper or post step by step, or run an interactive tutor that validates understanding over multiple sessions.
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
1,262 tokensWhen the skill is activated
Reference documents
341 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Mission
- Modes
- Workflow
- Quick Teaching Output
- Interactive Tutor Output
- Safety And Source Handling
- Good Artifact Traits
Files
README.mddoc1,540 bytesSKILL.mdcore6,031 bytesSKILL.mdcore6,031 bytesSKILL.mdcore6,031 bytesSKILL.mdcore6,031 bytesSKILL.mdcore6,031 bytesSKILL.mdcore6,031 bytesSKILL.mdcore6,031 bytesSKILL.mdcore6,031 bytesSKILL.mdcore6,031 bytesagents/openai.yamlasset238 bytes
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# Async Learning Teacher
## Mission
Convert raw learning captures into approachable teaching artifacts so a user can queue interesting resources now and study them later with less friction than reading the original source cold.
Use this skill for:
- Single links: blog posts, essays, papers, tweet threads, videos, documentation pages, or notes.
- Reference sets: 5-10+ links about one domain or topic.
- User-supplied pasted text when a link is unavailable, paywalled, deleted, or hard to scrape.
- Ongoing tutoring sessions where the agent should validate understanding before moving forward.
## Modesbuild-scenario-tests
When your agent uses it
Inspect an unfamiliar repository, turn a focused Markdown behavior scenario into a deterministic test in the repository's native test stack, run it, and preserve traceability between intent and code. Use when asked to add scenario tests, compile acceptance criteria or Given/When/Then Markdown into executable tests, reproduce a user-visible regression, or convert a narrow workflow specification into stable web, API, CLI, desktop, or mobile interaction coverage. Do not use for broad exploratory journeys or agent-judged smoke tests.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
1,331 tokensWhen the skill is activated
Reference documents
1,167 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Operating contract
- Workflow
- Demonstration examples
- Failure handling
Scripts executed separately
examples/test_cli_expired_session.pyexamples/web-workspace-invite.spec.ts
Files
README.mddoc660 bytesSKILL.mdcore6,754 bytesagents/openai.yamlasset261 bytesexamples/cli-expired-session.scenario.mddoc1,378 bytesexamples/test_cli_expired_session.pyruns934 bytesexamples/web-workspace-invite.scenario.mddoc1,434 bytesexamples/web-workspace-invite.spec.tsruns1,611 bytesreferences/scenario-format.mddoc2,039 bytes
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# Build Scenario Tests
Compile one small behavior contract into deterministic, repository-native test
code. Understand the host project before choosing a harness, command, fixture,
selector, or assertion.
## Operating contract
- Treat repository instructions and existing tests as authoritative.
- Keep each scenario focused on one behavior and one reason to fail.
- Compile into the test stack the repository already uses. Add a dependency only
when no suitable harness exists and the user accepts the tradeoff.
- Make setup, inputs, actions, and expected results deterministic.
- Prefer public behavior over implementation details. Assert what a user orcode-standards
When your agent uses it
Apply a disciplined engineering workflow to any code change. Use whenever implementing a feature, fixing a bug, or refactoring — before writing code, not after. Walks orient → baseline → smallest change → test → verify → self-review, and enforces language-agnostic hard gates (don't mass-reformat, keep the linter and type-checker clean, keep the build and tests green, make interface changes additive, protect security invariants).
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
698 tokensWhen the skill is activated
Reference documents
191 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Steps
- Tripwires — stop and re-check the project's guidelines when you're about to…
- No-progress guard
Files
README.mddoc758 bytesSKILL.mdcore2,988 bytes
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# Code standards — the change workflow
You are making a code change. This skill is the execution order; it is
stack-agnostic. Adapt every command to the project's actual toolchain: read the
project's own docs (README, CONTRIBUTING, `AGENTS.md`/`CLAUDE.md`, a Makefile or
package scripts) once to learn its build, test, lint, and format commands before
you start.
## Steps
1. **Orient.** Read any project lessons/gotchas file (e.g. `tasks/lessons.md`) and obey it.
Find and read the one doc for the subsystem you're changing, not the whole tree.
2. **Plan if non-trivial** (3+ steps or an architectural choice): short plan in `tasks/todo.md` —
files touched, tests to add, risks. Going sideways → stop and re-plan.create-marketing-kit
When your agent uses it
Create truthful, human-centered marketing campaigns for an app or product, including positioning, channel copy, original artwork, editable layouts, README banners, and selective website integration. Use when asked to make launch materials, promotional artwork, social assets, campaign kits, ads, or marketing content from an existing product; to adapt a visual reference without copying it; or to add approved campaign art to product surfaces. Do not invent claims, fake UI, publish, deploy, or replace product proof without explicit evidence and authorization.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
1,440 tokensWhen the skill is activated
Reference documents
1,826 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Operating contract
- Workflow
- Campaign kit structure
- Handoff
Scripts executed separately
scripts/validate_campaign_kit.py
Files
README.mddoc655 bytesSKILL.mdcore7,568 bytesSKILL.mdcore7,568 bytesSKILL.mdcore7,568 bytesSKILL.mdcore7,568 bytesagents/openai.yamlasset225 bytesreferences/art-direction.mddoc4,906 bytesreferences/campaign-strategy.mddoc3,607 bytesscripts/validate_campaign_kit.pyruns6,158 bytes
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# Create Marketing Kit
Turn verified product truth into a cohesive, reusable campaign kit. Preserve editable sources and local ownership, use generated imagery for emotion rather than factual proof, and validate every final asset before handoff.
## Operating contract
- Treat repository files, supplied screenshots, and explicit user statements as evidence. Mark inferences and unresolved gaps.
- Never invent features, testimonials, metrics, availability, citations, or customer approval.
- Treat privacy, security, encryption, and absolute data-residency statements as sensitive claims. Verify them against current implementation and configuration, including telemetry and third-party network behavior. Until verified, label them as owner-supplied and qualify or omit absolutes. Preserve conflicting evidence and ask the product owner instead of choosing the stronger claim.
- Keep generated people and scenes clearly editorial. Use real captures for product proof and real logos for identity.
- Generate artwork without marketing text, logos, or fake interface details. Add exact copy and UI deterministically in SVG, HTML/CSS, or native layout code.
- Preserve local-first ownership: keep prompts, source images, editable layouts, exports, and the manifest together in the project.
- Do not publish, deploy, push, or contact anyone unless the user explicitly requests it.
create-skill
When your agent uses it
Create or update a complete repository skill from a user's idea, including the workflow instructions, references, scripts or assets, agent metadata, skill-card artwork, cinematic banner artwork, README links, discovery metadata, and validation. Use when the user asks to create a new skill, add a skill to this collection, turn a workflow into a reusable skill, or make a skill's documentation and artwork consistent with the repository.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
1,139 tokensWhen the skill is activated
Reference documents
1,896 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow
- Skill content requirements
- Artwork routing
- Validation commands
Files
README.mddoc459 bytesSKILL.mdcore5,425 bytesSKILL.mdcore5,425 bytesagents/openai.yamlasset232 bytesreferences/artwork.mddoc8,234 bytes
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# Create Skill
Turn a skill idea into a complete, discoverable, and shippable skill folder. Treat the skill instructions, metadata, artwork, README wiring, and validation as one deliverable.
## Workflow
1. **Orient the repository.** Read `AGENTS.md`, the root `README.md`, the nearest existing skill with a similar purpose, and any local lessons file. Inspect `package.json`, `skills.sh.json`, the installer, and the existing `assets/skill-cards/` and `assets/skill-banners/` conventions before editing.
2. **Define the contract.** Extract the skill slug, purpose, audience, trigger phrases, inputs, outputs, and acceptance checks from the request. Normalize the slug to lowercase hyphen-case. Preserve the user's terminology, but make the frontmatter description explicit about both the capability and the situations that should trigger it.
3. **Choose the smallest useful structure.** Create only the resources the skill needs:
- `skills/<slug>/SKILL.md` for the core workflow and routing rules.
- `skills/<slug>/agents/openai.yaml` for display metadata and an explicit `$<slug>` default prompt.lead
When your agent uses it
Use when the user asks to run or continue an autonomous build: turn a goal into an approved plan, dispatch implementation and review agents, and finish with one pull request. The Lead is a provider-neutral orchestrator that makes decisions but never writes code, edits files, runs commands, or reviews its own work.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
2,062 tokensWhen the skill is activated
Reference documents
4,307 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Zero-direct-execution contract
- Provider-neutral dispatch
- Roles
- Invariants
- Procedure
- Hard stops
- Maintenance
Files
README.mddoc882 bytesSKILL.mdcore9,724 bytesdispatch.mddoc10,866 bytesloop.mddoc4,464 bytesresearch.mddoc3,419 bytes
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# Lead
You are the Lead. You own the goal, user communication, trade-offs, task
boundaries, and final decisions. Agents do every executable action.
## Zero-direct-execution contract
The Lead may only:
- ask the user material questions and report decisions;
- spawn, message, wait for, or stop agents with the runtime's native delegation
tools;
- read compact agent handoffs and choose APPROVE, REVISE, CONTINUE, or STOP.
lead-research
When your agent uses it
Provider-neutral deep-research orchestration for brainstorming, technology choices, comparisons, and state-of-the-art surveys. The Research Lead makes scope and decision calls while native subagents scout, search, verify, synthesize, audit, write, and commit every research artifact.
What it loads into context
Frontmatter
74 tokensAlways available to the agent
SKILL.md
1,759 tokensWhen the skill is activated
Reference documents
2,314 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Zero-direct-research contract
- Separation of roles
- Scale first
- Procedure
- Hard stops
Files
README.mddoc932 bytesSKILL.mdcore8,397 bytesSKILL.mdcore8,397 bytesSKILL.mdcore8,397 bytesSKILL.mdcore8,397 bytesSKILL.mdcore8,397 bytesSKILL.mdcore8,397 bytesSKILL.mdcore8,397 bytestactics.mddoc8,792 bytes
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# Lead Research
You are the Research Lead. You own the question, user communication, scope,
trade-offs, and final accept/revise/stop decisions. Agents do the research and
write every artifact.
## Zero-direct-research contract
The Research Lead may only ask material questions, formulate agent assignments,
spawn/message/wait/stop agents, read compact handoffs, and choose among their
evidence-backed proposals.
The Research Lead must never search the web, fetch a source, inspect a repository
for findings, verify a claim, write or edit a brief/report, create files, runmake-playbook
When your agent uses it
Indie maker playbook for bootstrapped apps inspired by Pieter Levels' MAKE: The Indie Maker Blueprint. Use when a user is starting or sizing an app idea, choosing what to build next, building a minimal product, preparing a launch, diagnosing stalled growth, deciding pricing or monetization, automating operations, maintaining a per-app MAKE.md tracker, or evaluating whether to sell a product.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
832 tokensWhen the skill is activated
Reference documents
9,635 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Stage Model
- Workflow
- Reference Routing
- Tracker Handling
- Response Shape
Files
README.mddoc852 bytesSKILL.mdcore3,728 bytesagents/openai.yamlasset227 bytesreferences/automate.mddoc4,568 bytesreferences/build.mddoc5,320 bytesreferences/ethos.mddoc3,788 bytesreferences/exit.mddoc6,502 bytesreferences/grow.mddoc4,680 bytesreferences/idea.mddoc6,727 bytesreferences/launch.mddoc5,409 bytesreferences/monetize.mddoc6,312 bytestemplates/make-tracker.mddoc2,834 bytes
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# MAKE Playbook
Use this skill as a stage-specific operating system for solo and bootstrapped
software products. It is an original checklist and diagnostic system inspired by
Pieter Levels' *MAKE: The Indie Maker Blueprint*; it is not a substitute for the
book. Keep attribution intact when reusing the references.
## Stage Model
Idea -> Build -> Launch -> Grow -> Monetize -> Automate -> Exit
Always treat `references/ethos.md` as the standing frame for bootstrapping,
persistence, and ethical constraints. Load it once per session when it has not
already been read. Its constraints override stage tactics: no fake engagement,map-the-landscape
When your agent uses it
Map the big picture around a topic or software repository by identifying its boundaries, layers, actors, components, relationships, flows, history, fault lines, and open questions, then explain how the pieces fit together and where to look next. Use when a user asks to understand a whole field, domain, technology, ecosystem, industry, codebase, architecture, unfamiliar repo, or phrases such as "give me the big picture", "map the landscape", "how does this all fit together?", "help me get oriented", or "what am I missing?"
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
1,833 tokensWhen the skill is activated
Reference documents
1,663 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Set the Zoom
- Evidence Discipline
- Workflow
- Depth Control
- Failure Handling
Files
README.mddoc905 bytesSKILL.mdcore8,589 bytesSKILL.mdcore8,589 bytesSKILL.mdcore8,589 bytesSKILL.mdcore8,589 bytesagents/openai.yamlasset224 bytesreferences/map-lenses.mddoc6,616 bytes
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# Map the Landscape
Build a useful mental model, not an exhaustive catalog. Reveal the structure
that makes details make sense: what is inside the boundary, what sits outside
it, which relationships matter, how value or data moves, why the landscape
looks this way, and where uncertainty remains.
## Set the Zoom
Infer these from the request and available context:
- **Subject:** the topic, field, ecosystem, or repository to map.
- **Mode:** `topic`, `repository`, or `hybrid` when a repo must be placed in its
wider ecosystem.name-your-business
When your agent uses it
Generate, refine, compare, and when needed validate distinctive names for startups, AI products, developer tools, protocols, open-source projects, apps, product families, local businesses, services, companies, nonprofits, and other organizations. Use when asked to name or rename a business, brand, product, venture, shop, studio, or organization; derive a name from an existing project or concept; create a company and product naming system; compare candidates; or check company, domain, package, repository, marketplace, or trademark conflicts. Default to a fast no-browse creative pass; use current validation when the user requests availability or research, or is choosing a final name. Do not claim legal clearance, purchase domains, or present stale or unverified availability as fact.
What it loads into context
Frontmatter
160 tokensAlways available to the agent
SKILL.md
3,309 tokensWhen the skill is activated
Reference documents
4,076 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Operating contract
- Choose the operating mode
- Workflow
- Failure handling
- Acceptance check
- Resources
Scripts executed separately
scripts/check_domains.py
Files
README.mddoc2,665 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesSKILL.mdcore17,166 bytesagents/openai.yamlasset378 bytesreferences/clearance.mddoc8,079 bytesreferences/naming-strategy.mddoc10,212 bytesscripts/check_domains.pyruns8,869 bytes
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# Name Your Business
Act as the senior naming team inside a world-class branding and marketing agency. Get the user to useful names quickly, then deepen the work only when the decision warrants it. Be imaginative in generation, severe in selection, and plainspoken about evidence and tradeoffs.
## Operating contract
- Choose the least expensive operating mode that satisfies the request.
- Treat the name as a strategic business asset, not a word-game exercise. Diagnose the audience, position, category, and growth horizon before generating.
- Adapt the method to the business. A neighborhood clinic, AI developer tool, nonprofit, restaurant, protocol, consumer product, and holding company should not sound as though they came from one naming generator.
- Give a clear top choice at the confidence level the mode supports. In creative-only work, call it the **creative favorite**; reserve a final launch recommendation for validated work.
- Separate creativity from validation. Do not let an open domain rescue a weak name or let registry searches narrow the idea space before useful candidates exist.
- Never invent an attractive story after generating an unrelated word. Meanings, roots, technical puns, and source-name relationships must be defensible; label interpretation as interpretation.
- Use current web research only when the selected mode requires it. Cite live evidence and record when time-sensitive facts were checked.
- Never infer domain availability from missing DNS, an empty webpage, a search result, or RDAP alone. A domain is verified only after the authoritative registry signal and a reputable registrar both support registration now.orwell-writing
When your agent uses it
Use when an agent is asked to draft, rewrite, edit, review, polish, copyedit, simplify, humanize, or create written prose, including creative writing, essays, posts, scripts, speeches, emails, documentation, product copy, and other style-sensitive text. Apply George Orwell's six rules and ASD-STE100 Simplified Technical English as a plain-English discipline while preserving the user's intended meaning, audience, tone, and explicit constraints.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
932 tokensWhen the skill is activated
Reference documents
191 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Core Rules
- ASD-STE100 baseline
- Workflow
- Creative Writing
Files
README.mddoc761 bytesSKILL.mdcore4,530 bytesagents/openai.yamlasset244 bytes
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# Orwell Writing
## Overview
Use Orwell's rules and ASD-STE100 Simplified Technical English (STE) as practical filters for clear, direct, and honest prose. Use STE by default for technical, instructional, business, and product prose. Apply the rules to both drafting and revision, but do not erase deliberate voice, character, rhythm, humor, or genre when the user clearly wants them.
STE has writing rules and a controlled dictionary. Use an approved word with its approved meaning when the dictionary is available. Do not claim strict STE conformance without checking the current ASD-STE100 issue and dictionary.
## Core Rules
Remember these rules from "Politics and the English Language":
1. Never use a metaphor, simile, or other figure of speech which you are used to seeing in print.
2. Never use a long word where a short one will do.paper-opportunity-radar
When your agent uses it
Run a cumulative daily or retrospective sweep of research papers on a chosen topic, audit their claims, methods, integrity signals, and independent support, then identify overlooked but feasible project or business opportunities in a detailed source-grounded report. Use when asked to monitor papers every day, mine buried research, evaluate whether a paper is credible or reproducible, find unimplemented research ideas, or separate promising work from hype, weak evidence, and retracted or contradicted results.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
2,587 tokensWhen the skill is activated
Reference documents
4,772 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Non-negotiable Standards
- Inputs and Defaults
- Durable Workspace
- Workflow
- Failure Handling
- Completion Criteria
Files
README.mddoc1,166 bytesSKILL.mdcore12,297 bytesSKILL.mdcore12,297 bytesSKILL.mdcore12,297 bytesSKILL.mdcore12,297 bytesSKILL.mdcore12,297 bytesagents/openai.yamlasset301 bytesreferences/discovery-protocol.mddoc5,683 bytesreferences/evidence-audit.mddoc8,065 bytesreferences/opportunity-and-report.mddoc8,257 bytes
Read SKILL.md
# Paper Opportunity Radar
Treat the literature as an evidence base to traverse over time, not a feed to
summarize. Build a cumulative corpus, audit important papers at claim level,
look for what happened after publication, and turn only defensible gaps into
testable opportunities.
This skill runs when invoked. It does not silently create a background
scheduler. If the user wants a daily cadence, preserve resumable state and give
their scheduler the recurring prompt in [Daily Operation](#daily-operation).
Never say monitoring is active until a recurring job actually exists.
## Non-negotiable Standards
repo-system-map
When your agent uses it
Analyze a software repository at the latest remote main commit and turn its implemented architecture into a citation-backed interactive isometric system map with a legend, selectable infrastructure buildings, dependency and payload flows, and plain-language learner explanations. For eligible public GitHub repositories, also contribute the verified map to tamdogood/CodeTerrain with a pull request. Use when a user provides a repository URL or asks to visualize, explore, learn, explain, or map a repo's architecture, infrastructure, runtime control flow, data flow, services, queues, stores, external systems, or deployment topology in an interactive UI.
What it loads into context
Frontmatter
127 tokensAlways available to the agent
SKILL.md
2,515 tokensWhen the skill is activated
Reference documents
2,751 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Contract
- Workflow
- Failure Handling
Files
README.mddoc1,363 bytesSKILL.mdcore12,011 bytesSKILL.mdcore12,011 bytesSKILL.mdcore12,011 bytesSKILL.mdcore12,011 bytesSKILL.mdcore12,011 bytesSKILL.mdcore12,011 bytesSKILL.mdcore12,011 bytesSKILL.mdcore12,011 bytesSKILL.mdcore12,011 bytesSKILL.mdcore12,011 bytesagents/openai.yamlasset304 bytesreferences/system-map-spec.mddoc11,486 bytes
Read SKILL.md
# Repo System Map
Build an explorable model of what the repository actually implements. Treat the
map as a teaching interface backed by code evidence, not as decorative
architecture art.
## Contract
Accept:
- a repository URL, or an existing checkout with a configured remote;
- an optional subsystem or user journey to emphasize;
- an optional output path and serve port.
run-smoke-tests
When your agent uses it
Inspect an unfamiliar repository, interpret a broad Markdown user journey at runtime, operate the real product through its supported web, API, CLI, desktop, or mobile surface, and produce an auditable pass, fail, or blocked judgment with screenshots, logs, recordings, and a step timeline when available. Use when asked to run smoke tests, validate an end-to-end user journey, dogfood a product, test a release candidate, or execute a non-deterministic workflow that may include authentication or human checkpoints. Do not use as a substitute for deterministic unit, integration, or scenario tests.
What it loads into context
Frontmatter
126 tokensAlways available to the agent
SKILL.md
1,531 tokensWhen the skill is activated
Reference documents
2,570 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Operating contract
- Workflow
- Demonstration examples
- Failure handling
Files
README.mddoc679 bytesSKILL.mdcore7,625 bytesSKILL.mdcore7,625 bytesSKILL.mdcore7,625 bytesSKILL.mdcore7,625 bytesagents/openai.yamlasset246 bytesexamples/developer-cli-first-run.smoke.mddoc3,420 bytesexamples/saas-team-onboarding.sample-report.mddoc2,263 bytesexamples/saas-team-onboarding.smoke.mddoc3,585 bytesreferences/smoke-format.mddoc2,244 bytes
Read SKILL.md
# Run Smoke Tests
Execute a broad user journey against a real build and leave enough evidence for
a person to audit the judgment. Understand the host project before starting the
product or touching its state.
## Operating contract
- Treat the Markdown as runtime instructions, not source to compile into code.
- Test through a supported user-facing surface: web UI, API, CLI, desktop,
mobile, or a documented combination.
- Separate execution from repair. Do not edit the product, tests, or journey
during a run. Finish the report before starting any requested fix.
- Default to local, test, preview, or staging environments. Never mutatesession-profiler
When your agent uses it
Profile and debug Hermes sessions from their JSONL transcripts. Find a session and its subagents, build a queryable event table, summarize the work as a hierarchical table of contents, break down wall time, inference, tools, tokens, and estimated cost per agent, identify errors and improvement opportunities, and export a shareable Perfetto trace. Use when a user wants to inspect what a Hermes session did, debug agent or subagent activity, understand session cost or latency, create a session timeline, generate a trace, or learn how to improve the next agent run.
What it loads into context
Frontmatter
121 tokensAlways available to the agent
SKILL.md
1,369 tokensWhen the skill is activated
Reference documents
178 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow
- Interpretation Rules
- Privacy And Failure Handling
Scripts executed separately
scripts/session_profiler/__init__.pyscripts/session_profiler/analyses.pyscripts/session_profiler/cli.pyscripts/session_profiler/dataset.pyscripts/session_profiler/discover.pyscripts/session_profiler/toc.pyscripts/session_profiler/trace.pyscripts/spscripts/tests/test_fixture.py
Files
README.mddoc736 bytesSKILL.mdcore6,049 bytesSKILL.mdcore6,049 bytesSKILL.mdcore6,049 bytesSKILL.mdcore6,049 bytesSKILL.mdcore6,049 bytesSKILL.mdcore6,049 bytesSKILL.mdcore6,049 bytesSKILL.mdcore6,049 bytesagents/openai.yamlasset253 bytesscripts/requirements.txtdoc24 bytesscripts/session_profiler/__init__.pyruns84 bytesscripts/session_profiler/analyses.pyruns12,118 bytesscripts/session_profiler/cli.pyruns4,467 bytesscripts/session_profiler/dataset.pyruns20,923 bytesscripts/session_profiler/discover.pyruns7,529 bytesscripts/session_profiler/toc.pyruns4,010 bytesscripts/session_profiler/trace.pyruns7,757 bytesscripts/spruns537 bytesscripts/tests/fixture/main.jsonlasset1,757 bytesscripts/tests/fixture/main/subagents/agent-abc.jsonlasset584 bytesscripts/tests/fixture/main/subagents/agent-abc.meta.jsonasset118 bytesscripts/tests/hermes_home/sessions/2026/01/01/rollout-child.jsonlasset1,278 bytesscripts/tests/hermes_home/sessions/2026/01/01/rollout-root.jsonlasset1,862 bytesscripts/tests/test_fixture.pyruns2,408 bytes
Read SKILL.md
# Session Profiler
Turn one Hermes main transcript plus its subagent transcripts into analysis artifacts. Route every operation through the bundled wrapper so it uses a consistent environment and remembers the latest parsed work directory.
```bash
SP="$SKILL/scripts/sp"
```
Resolve `SKILL` to this skill's directory. If it is unavailable, use the absolute path to `scripts/sp` beside this file.
## Workflow
1. Find and inspect the session.
top-one-percent
When your agent uses it
Teach any topic deeply from first principles and build evidence-based paths toward exceptional capability. Use when a user asks to understand, explain, learn, or deep-dive into a topic; asks why or how something works, why it matters, how alternatives compare, or what different perspectives reveal; requests current paradigms, a complete roadmap or curriculum, deliberate practice and feedback, or a top-1%-level mastery plan. Answer explanation questions completely before offering a curriculum; route explicit mastery goals to an adaptive practice and proof-of-capability system.
What it loads into context
Frontmatter
115 tokensAlways available to the agent
SKILL.md
3,153 tokensWhen the skill is activated
Reference documents
2,086 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Follow the Answer-First Contract
- Route the Request
- Produce a Deep Explanation
- Build a Mastery System
- Teach and Coach Interactively
- Use Deliberate Practice
- Measure and Adapt
- Choose the Output by Mode
- Final Quality Check
Files
README.mddoc2,100 bytesSKILL.mdcore16,370 bytesagents/openai.yamlasset272 bytesreferences/learning-principles.mddoc7,203 bytes
Read SKILL.md
# Top One Percent
Produce unusually clear understanding and evidence-backed mastery. Treat “top one percent” as a direction and a quality standard, not a percentile promise that cannot be measured.
## Follow the Answer-First Contract
Match the response to the user's actual intent:
- Answer a question as a complete explanation. Do not replace the answer with a roadmap, diagnostic, study plan, or quiz.
- Build a mastery system when the user asks to become excellent, requests a curriculum, or wants sustained practice.
- Tutor interactively when the user asks for lessons, exercises, assessment, or ongoing coaching.
- Combine these only when the combination directly serves the request. If the user asks “Why is X special?”, explain X first; a brief learning path may follow only if useful.
Infer the learner's level, goals, and constraints from the request and conversation. State only assumptions that materially affect the answer. Ask at most one high-leverage question when different answers would produce substantially different work; otherwise begin with a sensible default. Personalize examples to the learner's background when known.validate-market
When your agent uses it
Run an honest market-fit and viability audit of any project or idea and produce a decision doc, not code. Use when someone asks "is this viable as a business", "audit the market fit", "make the case and compare to competitors", "should I apply to YC or bootstrap", or wants to validate a project, product, or market before investing more time. Gathers verifiable traction data first, researches the competitive field, gets an independent cold read, writes a doc with pre-committed pass/middle/kill criteria and a concrete week-1 assignment, then hardens it with an adversarial review loop.
What it loads into context
Frontmatter
174 tokensAlways available to the agent
SKILL.md
2,633 tokensWhen the skill is activated
Reference documents
189 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Posture (non-negotiable)
- Phase 0: Ground truth before opinion
- Phase 1: Problem and wedge
- Phase 2: Landscape research
- Phase 3: Premises
- Phase 4: Independent cold read
- Phase 5: Write the doc
- Phase 6: Adversarial review loop
- Closing
Files
README.mddoc779 bytesSKILL.mdcore11,165 bytes
Read SKILL.md
# Validate a project / market
You are running a validation audit. The deliverable is one document that answers: is
there a business here, who else is fighting for it, where are the openings, and what is
the next concrete action. The audit exists to protect months of the founder's life, so
honesty outranks encouragement everywhere.
**Hard gate: no implementation.** No code, no scaffolding, no renames, no site changes.
The only output is the doc (plus its adversarial review). If the user asked for a doc in
a specific place, put it there; otherwise default to `docs/research/<slug>-market-viability-audit.md`
in a repo, or `./<slug>-market-viability-audit.md` outside one.
## Posture (non-negotiable)
write-blog
When your agent uses it
Write and ship a blog post for your project that reads like a human wrote it and pulls SEO traffic to your site. Use when a contributor wants to draft, edit, or publish a blog post, add a post to the site, write about the project for search/traction, or make an existing draft sound less like AI. Enforces the house voice (no em dashes), picks a non-cannibalizing angle, uses ASD-STE100 Simplified Technical English for technical prose, wires the post into your site, and runs the humanizer pass before shipping.
What it loads into context
Frontmatter
155 tokensAlways available to the agent
SKILL.md
1,946 tokensWhen the skill is activated
Reference documents
5,751 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The one rule that gets a post rejected
- Workflow
- Guardrails
- Note on this skill's own files
Scripts executed separately
check.sh
Files
README.mddoc804 bytesSKILL.mdcore8,436 bytesSKILL.mdcore8,436 bytesSKILL.mdcore8,436 bytesSKILL.mdcore8,436 bytesSKILL.mdcore8,436 bytesSKILL.mdcore8,436 bytesSKILL.mdcore8,436 bytescheck.shruns2,207 bytesreference/angles.mddoc2,667 bytesreference/craft.mddoc4,639 bytesreference/seo.mddoc6,461 bytesreference/voice.mddoc4,522 bytesreference/wiring.mddoc4,893 bytes
Read SKILL.md
# Write a blog post
You are helping a contributor write a blog post for the project's website. The bar: it
reads like a person wrote it, it ranks for a search cluster no other post already owns,
and it ships wired correctly into the site with the build green.
Use ASD-STE100 Simplified Technical English (STE) for the post's technical and
instructional prose. Use short, direct sentences, consistent terms, clear actions, and
the approved meaning of technical words when the STE dictionary is available. Do not use
idioms, slang, figurative language, or synonym changes that can make a technical term
unclear. Preserve exact code, commands, identifiers, product names, and quotations.
The post can keep a human opinion and a concrete example, but clarity takes priority over
rhythm or decoration. Do not claim strict STE conformance without checking the current
ASD-STE100 issue and dictionary.Skills included
- async-learning-teacher
- build-scenario-tests
- code-standards
- create-marketing-kit
- create-skill
- lead
- lead-research
- make-playbook
- map-the-landscape
- name-your-business
- orwell-writing
- paper-opportunity-radar
- repo-system-map
- run-smoke-tests
- session-profiler
- top-one-percent
- validate-market
- write-blog
Repository facts
- License
- MIT
- Created
- Jul 11, 2026
- Last pushed
- Aug 16, 2026
- Forks
- 9
- Open issues
- 0
- Project activity
- Slowing
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 69 notes
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- 2 broad permissionsin 2 files: validate-market/SKILL.md:1 write-blog/SKILL.md:1
- No dangerous shell commands
- 97 network referencesin 21 files: top-one-percent/references/learning-principles.md:21 lead-research/tactics.md:53 paper-opportunity-radar/references/discovery-protocol.md:28 and 18 more
- No hidden or obfuscated text
- No instruction overrides
- 14 executable scriptsin 14 files: build-scenario-tests/examples/test_cli_expired_session.py:1 build-scenario-tests/examples/web-workspace-invite.spec.ts:1 create-marketing-kit/scripts/validate_campaign_kit.py:1 and 11 more
- No requests to vote in Hot
Show all 121 signals by file
skills/async-learning-teacher/README.md
skills/build-scenario-tests/examples/test_cli_expired_session.py
- Executable scriptline 1
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skills/build-scenario-tests/examples/web-workspace-invite.spec.ts
- Executable scriptline 1
Executable file
skills/create-marketing-kit/scripts/validate_campaign_kit.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/lead-research/tactics.md
- Network accessline 53
terminology and the seed bibliography. - Latest sweep (newest first): the arXiv Atom API `https://export.arxiv.org/api/query?search_query=cat:<cs.XX>+AND+abs:%22<topic>%22&sortBy=submittedDate&sortOrder=descending&max_results=25` - Network accessline 54
- Latest sweep (newest first): the arXiv Atom API `https://export.arxiv.org/api/query?search_query=cat:<cs.XX>+AND+abs:%22<topic>%22&sortBy=submittedDate&sortOrder=descending&max_results=25` (uppercase AND/OR; wait ~3s between calls) an - Network accessline 55
`https://export.arxiv.org/api/query?search_query=cat:<cs.XX>+AND+abs:%22<topic>%22&sortBy=submittedDate&sortOrder=descending&max_results=25` - Network accessline 56
(uppercase AND/OR; wait ~3s between calls) and Semantic Scholar `https://api.semanticscholar.org/graph/v1/paper/search?query=<topic>&fields=title,year,citationCount,tldr,venue&limit=20` (expect 429s — back off and retry; the `tldr` fiel - Network accessline 57
`https://api.semanticscholar.org/graph/v1/paper/search?query=<topic>&fields=title,year,citationCount,tldr,venue&limit=20` - Network accessline 59
- Snowball from the 2–3 most relevant seeds — forward citations and semantic neighbours via the Semantic Scholar graph API; fall back to OpenAlex `https://api.openalex.org/works?search=<topic>&sort=publication_date:desc&per-page=25` - Network accessline 60
neighbours via the Semantic Scholar graph API; fall back to OpenAlex `https://api.openalex.org/works?search=<topic>&sort=publication_date:desc&per-page=25` when S2 throttles. - Network accessline 61
`https://api.openalex.org/works?search=<topic>&sort=publication_date:desc&per-page=25` - Network accessline 73
`"<topic>" in:name,description,readme stars:>2000`, plus awesome-lists as recall boosters — re-check `pushed:` on every list entry; lists go stale. - **Adoption evidence beats stars**: dependents via `https://api.deps.dev` or - Network accessline 74
boosters — re-check `pushed:` on every list entry; lists go stale. - **Adoption evidence beats stars**: dependents via `https://api.deps.dev` or `https://packages.ecosyste.ms` (keyless); registry download trends - Network accessline 75
- **Adoption evidence beats stars**: dependents via `https://api.deps.dev` or `https://packages.ecosyste.ms` (keyless); registry download trends (`https://pypistats.org`, `https://api.npmjs.org/downloads`). - Network accessline 76
`https://packages.ecosyste.ms` (keyless); registry download trends - Network accessline 77
(`https://pypistats.org`, `https://api.npmjs.org/downloads`). - Network accessline 86
- Where bleeding-edge surfaces first: Hacker News via Algolia `https://hn.algolia.com/api/v1/search_by_date?query=<topic>&tags=story&numericFilters=points>50`; - Network accessline 87
`https://hn.algolia.com/api/v1/search_by_date?query=<topic>&tags=story&numericFilters=points>50`; - Network accessline 88
`https://lobste.rs/t/<tag>.json`; GitHub - Network accessline 113
they differ is a trade-off to document. - Tools: GitHub code search (`symbol:`, `/regex/`, `repo:`, `path:`), `https://grep.app`, `https://sourcegraph.com/search`. - Network accessline 115
`https://grep.app`, `https://sourcegraph.com/search`. - Network accessline 140
- Where to find their voice, in reliability order: personal blogs / newsletters (`"<name>" <topic>`, `site:<their-domain> <topic>`); HN comments (`https://hn.algolia.com/api/v1/search?tags=comment,author_<username>&query=<topic>`); - Network accessline 141
(`"<name>" <topic>`, `site:<their-domain> <topic>`); HN comments (`https://hn.algolia.com/api/v1/search?tags=comment,author_<username>&query=<topic>`); conference talks / podcasts (prefer transcripts or the speaker's own writeup); - Network accessline 142
(`https://hn.algolia.com/api/v1/search?tags=comment,author_<username>&query=<topic>`);
skills/make-playbook/references/automate.md
- Network accessline 3
> Inspired by the "Automate" chapter of Pieter Levels' *MAKE: The Indie Maker Blueprint*. This is an original checklist/diagnostic built around that chapter's core ideas — for the full chapter and examples, [buy the book](https://readmake.c
skills/make-playbook/references/build.md
- Network accessline 3
> Inspired by the "Build" chapter of Pieter Levels' *MAKE: The Indie Maker Blueprint*. This is an original checklist/diagnostic built around that chapter's core ideas — for the full chapter, examples, and author's voice, [buy the book](http
skills/make-playbook/references/ethos.md
- Network accessline 3
> Inspired by the overall philosophy of Pieter Levels' *MAKE: The Indie Maker Blueprint*. This is an original synthesis of that philosophy — for the full book in the author's own words, [buy it at readmake.com](https://readmake.com).
skills/make-playbook/references/exit.md
- Network accessline 3
> Inspired by the "Exit" chapter of Pieter Levels' *MAKE: The Indie Maker Blueprint*. This is an original checklist/diagnostic built around that chapter's core ideas — for the full chapter, negotiation detail, and author's voice, [buy the b
skills/make-playbook/references/grow.md
- Network accessline 3
> Inspired by the "Grow" chapter of Pieter Levels' *MAKE: The Indie Maker Blueprint*. This is an original checklist/diagnostic built around that chapter's core ideas — for the full chapter and examples, [buy the book](https://readmake.com).
skills/make-playbook/references/idea.md
- Network accessline 3
> Inspired by the "Idea" chapter of Pieter Levels' *MAKE: The Indie Maker Blueprint*. This is an original checklist/diagnostic built around that chapter's core ideas — for the full chapter, examples, and author's voice, [buy the book](https
skills/make-playbook/references/launch.md
- Network accessline 3
> Inspired by the "Launch" chapter of Pieter Levels' *MAKE: The Indie Maker Blueprint*. This is an original checklist/diagnostic built around that chapter's core ideas — for the full chapter, platform-by-platform tactics, and author's voice
skills/make-playbook/references/monetize.md
- Network accessline 3
> Inspired by the "Monetize" chapter of Pieter Levels' *MAKE: The Indie Maker Blueprint*. This is an original checklist/diagnostic built around that chapter's core ideas — for the full chapter, worked examples, and author's voice, [buy the
skills/name-your-business/scripts/check_domains.py
skills/paper-opportunity-radar/references/discovery-protocol.md
- Network accessline 28
- [OpenAlex API](https://developers.openalex.org/api-reference/introduction) for - Network accessline 29
works, authors, sources, topics, filters, and citation relationships; - [Crossref REST API](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) for DOI metadata and publisher-deposited updates; - Network accessline 30
- [Crossref REST API](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) - Network accessline 31
for DOI metadata and publisher-deposited updates; - [Semantic Scholar Academic Graph](https://api.semanticscholar.org/api-docs/graph) for paper, reference, citation, and recommendation traversal; - Network accessline 32
- [Semantic Scholar Academic Graph](https://api.semanticscholar.org/api-docs/graph) - Network accessline 33
for paper, reference, citation, and recommendation traversal; - [arXiv API](https://info.arxiv.org/help/api/index.html) for arXiv-covered fields and version histories; - Network accessline 34
- [arXiv API](https://info.arxiv.org/help/api/index.html) for arXiv-covered - Network accessline 35
fields and version histories; - [NCBI E-utilities](https://www.ncbi.nlm.nih.gov/books/NBK25501/) and [Europe PMC](https://europepmc.org/RestfulWebService) for biomedical and life - Network accessline 36
- [NCBI E-utilities](https://www.ncbi.nlm.nih.gov/books/NBK25501/) and - Network accessline 37
[Europe PMC](https://europepmc.org/RestfulWebService) for biomedical and life - Network accessline 38
sciences; - [ClinicalTrials.gov API](https://clinicaltrials.gov/data-api/api) for trial registration and results follow-up; - Network accessline 39
- [ClinicalTrials.gov API](https://clinicaltrials.gov/data-api/api) for trial - Network accessline 129
For systematic-review-grade work, use the [PRISMA-S reporting checklist](https://www.equator-network.org/reporting-guidelines/prisma-s/) - Network accessline 130
[PRISMA-S reporting checklist](https://www.equator-network.org/reporting-guidelines/prisma-s/)
skills/paper-opportunity-radar/references/evidence-audit.md
- Network accessline 25
- Check the publisher page, Crossmark when present, and Crossref updates. Crossref documents public access to Retraction Watch data and retraction metadata in its [Retraction Watch guide](https://www.crossref.org/documentation/retrieve- - Network accessline 26
Crossref documents public access to Retraction Watch data and retraction metadata in its [Retraction Watch guide](https://www.crossref.org/documentation/retrieve-metadata/retraction-watch/). - Find corrections, errata, expressions of conc - Network accessline 27
metadata in its [Retraction Watch guide](https://www.crossref.org/documentation/retrieve-metadata/retraction-watch/). - Network accessline 62
- Does a preregistration or protocol predate data access, and do reported outcomes match it? OSF describes preregistrations as time-stamped, read-only study plans in its [registration guide](https://help.osf.io/article/330-welcome-to-re - Network accessline 64
study plans in its [registration guide](https://help.osf.io/article/330-welcome-to-registrations). - Network accessline 75
events, subgroup claims, protocol changes, and registration timing. For a full randomized-trial assessment, route to Cochrane's [RoB 2 guidance](https://www.cochrane.org/learn/courses-and-resources/cochrane-methodology/risk-bias/about-risk- - Network accessline 76
randomized-trial assessment, route to Cochrane's [RoB 2 guidance](https://www.cochrane.org/learn/courses-and-resources/cochrane-methodology/risk-bias/about-risk-bias-2-rob-2) instead of inventing a simplified clinical score. - Network accessline 77
[RoB 2 guidance](https://www.cochrane.org/learn/courses-and-resources/cochrane-methodology/risk-bias/about-risk-bias-2-rob-2)
skills/repo-system-map/README.md
- Network accessline 9
file citations and plain-language explanations. For a public GitHub repository, the skill also submits the verified map to [CodeTerrain](https://github.com/tamdogood/CodeTerrain) with a pull request so - Network accessline 10
the skill also submits the verified map to [CodeTerrain](https://github.com/tamdogood/CodeTerrain) with a pull request so others can explore and learn from it. Say `skip CodeTerrain submission` to keep - Network accessline 11
[CodeTerrain](https://github.com/tamdogood/CodeTerrain) with a pull request so
skills/repo-system-map/SKILL.md
- Network accessline 36
For an eligible public GitHub repository, also add or update its map in `https://github.com/tamdogood/CodeTerrain` and return the pull-request URL. The - Network accessline 37
`https://github.com/tamdogood/CodeTerrain` and return the pull-request URL. The - Network accessline 178
Clone the latest default branch of `https://github.com/tamdogood/CodeTerrain` into a second isolated temporary - Network accessline 179
`https://github.com/tamdogood/CodeTerrain` into a second isolated temporary
skills/repo-system-map/references/system-map-spec.md
- Network accessline 58
https://github.com/<owner>/<repo>/blob/<commit>/<path>#L<start>-L<end>
skills/run-smoke-tests/examples/saas-team-onboarding.sample-report.md
skills/session-profiler/SKILL.md
- Network accessline 77
Load the emitted `trace.json.gz` in [Perfetto](https://ui.perfetto.dev). Read
skills/session-profiler/scripts/session_profiler/__init__.py
- Executable scriptline 1
Executable file
skills/session-profiler/scripts/session_profiler/analyses.py
- Executable scriptline 1
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skills/session-profiler/scripts/session_profiler/cli.py
- Executable scriptline 1
Executable file
skills/session-profiler/scripts/session_profiler/dataset.py
skills/session-profiler/scripts/session_profiler/discover.py
skills/session-profiler/scripts/session_profiler/toc.py
- Executable scriptline 1
Executable file
skills/session-profiler/scripts/session_profiler/trace.py
- Executable scriptline 1
Executable file - Network accessline 144
def open_ui() -> str: webbrowser.open("https://ui.perfetto.dev") - Network accessline 145
webbrowser.open("https://ui.perfetto.dev") - Network accessline 146
return "Opened https://ui.perfetto.dev — load trace.json.gz from the parsed session directory. Review the trace for sensitive paths or text before sharing."
skills/session-profiler/scripts/sp
- Executable scriptline 1
#!/usr/bin/env bash
skills/session-profiler/scripts/tests/test_fixture.py
skills/top-one-percent/references/learning-principles.md
- Network accessline 21
**Evidence:** Retrieval practice enhanced later retention in controlled studies and also supported conceptual science learning. [Karpicke & Roediger (2008)](https://doi.org/10.1126/science.1152408), [Karpicke & Blunt - Network accessline 22
and also supported conceptual science learning. [Karpicke & Roediger (2008)](https://doi.org/10.1126/science.1152408), [Karpicke & Blunt (2011)](https://pubmed.ncbi.nlm.nih.gov/21252317/), and [Rowland's meta-analysis - Network accessline 23
(2008)](https://doi.org/10.1126/science.1152408), [Karpicke & Blunt - Network accessline 24
(2011)](https://pubmed.ncbi.nlm.nih.gov/21252317/), and [Rowland's meta-analysis - Network accessline 25
(2014)](https://pubmed.ncbi.nlm.nih.gov/25150680/). - Network accessline 42
high-utility across many conditions, while emphasizing that benefits depend on materials, learners, and outcome tasks. [Dunlosky et al. (2013)](https://www.psychologicalscience.org/journals/pspi/1529100612453266/). - Network accessline 44
(2013)](https://www.psychologicalscience.org/journals/pspi/1529100612453266/). - Network accessline 59
**Evidence:** Worked examples can improve subsequent performance for novices, but instructional load and prior knowledge matter. [Sweller (1991)](https://doi.org/10.1207/s1532690xci0804_2), [Retnowati et al. - Network accessline 60
but instructional load and prior knowledge matter. [Sweller (1991)](https://doi.org/10.1207/s1532690xci0804_2), [Retnowati et al. (2009)](https://doi.org/10.1016/j.learninstruc.2008.03.006). - Network accessline 61
(1991)](https://doi.org/10.1207/s1532690xci0804_2), [Retnowati et al. - Network accessline 62
(2009)](https://doi.org/10.1016/j.learninstruc.2008.03.006). - Network accessline 77
**Evidence:** Prompted self-explanation improved understanding in a controlled study of expository science text. [Chi et al. (1994)](https://doi.org/10.1207/s15516709cog1803_3). - Network accessline 79
(1994)](https://doi.org/10.1207/s15516709cog1803_3). - Network accessline 96
performance test; interleaving is promising but was judged less broadly established than retrieval and spacing in the major review. [Transfer-appropriate processing framework](https://learninglab.psych.purdue.edu/downloads/2014/2014_Karpick - Network accessline 97
established than retrieval and spacing in the major review. [Transfer-appropriate processing framework](https://learninglab.psych.purdue.edu/downloads/2014/2014_Karpicke_Lehman_Aue_PLM.pdf), [Dunlosky et al. (2013)](https://www.psychologica - Network accessline 98
processing framework](https://learninglab.psych.purdue.edu/downloads/2014/2014_Karpicke_Lehman_Aue_PLM.pdf), [Dunlosky et al. - Network accessline 99
(2013)](https://www.psychologicalscience.org/journals/pspi/1529100612453266/). - Network accessline 117
distinguishes task, process, and self-regulation feedback. Studies of judgments of learning show that monitoring can be biased. [Hattie & Timperley (2007)](https://doi.org/10.3102/003465430298487), [Koriat & Bjork - Network accessline 118
of learning show that monitoring can be biased. [Hattie & Timperley (2007)](https://doi.org/10.3102/003465430298487), [Koriat & Bjork (2006)](https://pubmed.ncbi.nlm.nih.gov/17128596/). - Network accessline 119
(2007)](https://doi.org/10.3102/003465430298487), [Koriat & Bjork - Network accessline 120
(2006)](https://pubmed.ncbi.nlm.nih.gov/17128596/). - Network accessline 137
structured effort, while a cross-domain meta-analysis found that practice's association with performance varied substantially by domain. [Ericsson, Krampe, & Tesch-Römer (1993)](https://doi.org/10.1037/0033-295X.100.3.363), [Macnamara, - Network accessline 138
association with performance varied substantially by domain. [Ericsson, Krampe, & Tesch-Römer (1993)](https://doi.org/10.1037/0033-295X.100.3.363), [Macnamara, Hambrick, & Oswald (2014)](https://www.psychologicalscience.org/journals/psychol - Network accessline 139
& Tesch-Römer (1993)](https://doi.org/10.1037/0033-295X.100.3.363), [Macnamara, - Network accessline 140
Hambrick, & Oswald (2014)](https://www.psychologicalscience.org/journals/psychological-science/0956797614535810/).
skills/validate-market/SKILL.md
- Broad permissionsline 1
Read Write Edit Grep Glob Bash WebSearch WebFetch Agent AskUserQuestion
skills/write-blog/SKILL.md
- Broad permissionsline 1
Read Write Edit Grep Glob Bash AskUserQuestion
skills/write-blog/check.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/write-blog/reference/seo.md
- Network accessline 92
schema choices, exhaustive OG/Twitter tag coverage, canonical and hreflang patterns, and Core Web Vitals), lean on the `astro-seo` skill, invocable as `/astro-seo` and published as `astro-seo-expert` at https://mcpmarket.com/tools/skills/as - Network accessline 93
Core Web Vitals), lean on the `astro-seo` skill, invocable as `/astro-seo` and published as `astro-seo-expert` at https://mcpmarket.com/tools/skills/astro-seo-expert. It is written for Astro, so ignore its Astro-specific integration code (` - Network accessline 94
`astro-seo-expert` at https://mcpmarket.com/tools/skills/astro-seo-expert. It is written for
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: 1 fact
- Contacts data.iana.org, which SKILL.md, the README and the plugin manifest do not mentionname-your-business/scripts/check_domains.py:23High confidence
Signal confidence: 121 facts
- Network access in code of documentationasync-learning-teacher/README.md:49Low confidence
- Network access in code of documentationasync-learning-teacher/README.md:50Low confidence
- Executable script in prose of a testbuild-scenario-tests/examples/test_cli_expired_session.py:1Low confidence
- Executable script in a comment of a file that runsbuild-scenario-tests/examples/web-workspace-invite.spec.ts:1Low confidence
- Executable script in code of a file that runscreate-marketing-kit/scripts/validate_campaign_kit.py:1High confidence
- Network access in prose of documentationlead-research/tactics.md:53Low confidence
- Network access in prose of documentationlead-research/tactics.md:54Low confidence
- Network access in prose of documentationlead-research/tactics.md:55Low confidence
- Network access in prose of documentationlead-research/tactics.md:56Low confidence
- Network access in prose of documentationlead-research/tactics.md:57Low confidence
- Network access in prose of documentationlead-research/tactics.md:59Low confidence
- Network access in prose of documentationlead-research/tactics.md:60Low confidence
- Network access in prose of documentationlead-research/tactics.md:61Low confidence
- Network access in prose of documentationlead-research/tactics.md:73Low confidence
- Network access in prose of documentationlead-research/tactics.md:74Low confidence
- Network access in prose of documentationlead-research/tactics.md:75Low confidence
- Network access in prose of documentationlead-research/tactics.md:76Low confidence
- Network access in prose of documentationlead-research/tactics.md:77Low confidence
- Network access in prose of documentationlead-research/tactics.md:86Low confidence
- Network access in prose of documentationlead-research/tactics.md:87Low confidence
- Network access in prose of documentationlead-research/tactics.md:88Low confidence
- Network access in prose of documentationlead-research/tactics.md:113Low confidence
- Network access in prose of documentationlead-research/tactics.md:115Low confidence
- Network access in prose of documentationlead-research/tactics.md:140Low confidence
- Network access in prose of documentationlead-research/tactics.md:141Low confidence
- Network access in prose of documentationlead-research/tactics.md:142Low confidence
- Network access in prose of documentationmake-playbook/references/automate.md:3Low confidence
- Network access in prose of documentationmake-playbook/references/build.md:3Low confidence
- Network access in prose of documentationmake-playbook/references/ethos.md:3Low confidence
- Network access in prose of documentationmake-playbook/references/exit.md:3Low confidence
- Network access in prose of documentationmake-playbook/references/grow.md:3Low confidence
- Network access in prose of documentationmake-playbook/references/idea.md:3Low confidence
- Network access in prose of documentationmake-playbook/references/launch.md:3Low confidence
- Network access in prose of documentationmake-playbook/references/monetize.md:3Low confidence
- Executable script in code of a file that runsname-your-business/scripts/check_domains.py:1High confidence
- Network access in code of a file that runsname-your-business/scripts/check_domains.py:23High confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:28Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:29Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:30Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:31Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:32Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:33Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:34Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:35Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:36Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:37Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:38Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:39Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:129Low confidence
- Network access in prose of documentationpaper-opportunity-radar/references/discovery-protocol.md:130Low confidence
and 71 more
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Compatible | Standard SKILL.md skills/async-learning-teacher/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Native | Manifest AGENTS.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/async-learning-teacher/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/async-learning-teacher/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/async-learning-teacher/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/async-learning-teacher/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.
