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Found 13 Skills
Analyzes repositories for AI agent development efficiency. Scores 8 aspects (documentation, architecture, testing, type safety, agent instructions, file structure, context optimization, security) with ASCII dashboards. Use when evaluating AI-readiness, preparing codebases for Claude Code, or improving repository structure for AI-assisted development.
Transform legacy codebases into AI-ready projects with Claude Code configurations. Use when (1) analyzing old projects to generate AI coding configurations, (2) creating CLAUDE.md, skills, subagents, slash commands, hooks, or rules for existing projects, (3) user wants to enable vibe coding for a codebase, (4) onboarding new team members with AI-assisted development, (5) user mentions "make project AI-ready", "generate Claude config", or "create coding standards for AI".
Make any repo AI-ready — analyzes your codebase and generates AGENTS.md, copilot-instructions.md, CI workflows, issue templates, and more. Mines your PR review patterns and creates files customized to your stack. USE THIS SKILL when the user asks to "make this repo ai-ready", "set up AI config", or "prepare this repo for AI contributions".
Designs and refactors software codebases to be AI-friendly by aligning the filesystem with domain/feature boundaries, creating deep (greybox) modules with small public interfaces, enforcing import boundaries, and tightening tests/feedback loops. Use when the user asks to "make the codebase AI-ready", "reduce coupling", "introduce deep modules", "create module boundaries", "restructure folders by feature", "define service interfaces", or "plan a refactor + tests so AI agents can work safely".
Use when normal web_fetch cannot read a page, when a site blocks basic fetching, or when the user needs YouTube content in AI-ready form. Guides fallback use of Firecrawl for single-page web scraping and SerpApi for YouTube search/video metadata/transcripts only.
Transforms article content or summaries into minimalist hand-drawn style JSON prompts for AI image generation tools. Use this skill whenever the user wants to create any kind of visual from text content — including banners, article illustrations, inline diagrams, infographics, or concept visuals. Trigger on requests like "turn this into a visual", "create an image prompt", "make an illustration for this", "generate a diagram from this article", "I need a sketch for this section", or any request combining content analysis with image/visual prompt generation. Always use this skill when the user provides text content and wants an AI-ready image prompt output.
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
Use when the user wants to author, refine, or audit a Product Requirements Document for AI coding agents. Walks through an 8-phase pipeline (Socratic discovery → PRD draft → acceptance criteria → adversarial review → task decomposition → AI-readiness gate → test generation → handoff). Triggers on "write a PRD", "spec this feature", "draft requirements", "prepare X for Claude/Cursor/Copilot/Windsurf/Aider to build", "audit my PRD", "is this PRD AI-ready", "score this spec".
Complete, AI-ready playbook to migrate Motoko projects from mo:base to mo:core — phases, renames, data structure changes, agent strategy, verification scripts, upgrade tests, and production rollout.
Your team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so Claude can answer questions on your team's behalf. Team-scope sibling to second-brain (which is personal-scope). Seven modes — capture (drop something into the right structured dir), compile (process into wiki pages, update INDEX.md), query (answer from the corpus with trust weighting, save to outputs/), review (triage queue — verify / deprecate / supersede unreviewed and stale captures so wrong info never becomes context), lint (orphans / stale / contradictions / gaps), connect (suggest new wikilinks), search (quick lookup). Structured raw dirs (people/, companies/, meetings/, sops/, decisions/, customer-language/, recurring-questions/, sales-objections/) instead of second-brain's flat type-prefixed raw/. Multi-author aware — every capture stamps author + timestamp + trust status. Optional auto-sync from Fathom/Gong/Granola call transcripts, Slack/email exports, CRM. Defaults to a vault at ${COMPANY_BRAIN_VAULT:-$HOME/Documents/CompanyBrain}/. Triggers on "/company-brain," "/cb," "capture this into the team brain," "log this meeting," "add this person to the team brain," "save this SOP," "compile the company wiki," "query the team brain," "what does the team know about X," "review the company brain," "cull the team brain," "lint the company brain," "who's the internal expert on X."
Generate AI-ready metadata for design system components to enable intelligent UI generation. Analyzes component structure and generates structured metadata that helps AI understand when and how to use components correctly. Useful for teams building AI-consumable design systems.
Create a new specification file for the solution, optimized for Generative AI consumption.