Agent Skills are modular packages that extend Claude's capabilities with specialized domain knowledge, following Anthropic's Agent Skills Specification. This plugin ecosystem includes 175 local specialized skills across 48 plugins, enabling progressive disclosure and efficien. Use it as a repeatable review, validation or hardening pass.
Override defaults for individual tools. This example enables everything except bash:. Use it to ground design choices in named patterns, trade-offs and examples.
Generic AI output is a PM's worst enemy. When you tell your agent "write a PRD" without shared context, you get a generic document that no stakeholder trusts and no engineer can act on. Use it to build a structured path from fundamentals to hands-on practice.
Indexes any codebase into a knowledge graph — every dependency, call chain, cluster, and execution flow — then exposes it through smart MCP tools so AI agents never miss code. Use it to ground design choices in named patterns, trade-offs and examples.
This repository uses a Core/Library split to ensure efficiency and high-signal discovery for LLMs:. Use it to build a structured path from fundamentals to hands-on practice.
Goal: Replace the brainstorm server's vendored nodemodules with a single zero-dependency server.js using Node built-ins. Architecture: Single file with WebSocket protocol (RFC 6455 text frames), HTTP server (http module), and file watching (fs.watch).
Goal: Add spec and plan document review loops to the brainstorming and writing-plans skills. Architecture: Create reviewer prompt templates in each skill directory.
--- name: writing-plans description: Use when you have a spec or requirements for a multi-step task, before touching code --- Write comprehensive implementation plans assuming the engineer has zero context for our codebase and questionable taste. Document everything they need to know: which files to touch for each task, code, testing, docs they might need to check, how to test it.
NEVER: INSTEAD: Example: your human partner's rule: "External feedback - be skeptical, but check carefully" your human partner's rule: "You and reviewer both report to me. If we don't need this feature, don't add it." Push back when: How to push back: If you're uncomfortable pushing back out loud: Name that tension, then tell your partner about the issue you've seen.
Load this reference when: writing or changing tests, adding mocks, or adding cleanup/helper methods for tests. Two principles govern everything here: Strict TDD produces both naturally: a test written first and watched failing against real code has already proven it can fail, and only earns a mock when the real dependency proves slow or external.
Agent Skills are modular packages that extend Claude's capabilities with specialized domain knowledge, following Anthropic's Agent Skills Specification. This plugin ecosystem includes 175 local specialized skills across 48 plugins, enabling progressive disclosure and efficien. Use it as a repeatable review, validation or hardening pass.
Override defaults for individual tools. This example enables everything except bash:. Use it to ground design choices in named patterns, trade-offs and examples.
Generic AI output is a PM's worst enemy. When you tell your agent "write a PRD" without shared context, you get a generic document that no stakeholder trusts and no engineer can act on. Use it to build a structured path from fundamentals to hands-on practice.
Indexes any codebase into a knowledge graph — every dependency, call chain, cluster, and execution flow — then exposes it through smart MCP tools so AI agents never miss code. Use it to ground design choices in named patterns, trade-offs and examples.
This repository uses a Core/Library split to ensure efficiency and high-signal discovery for LLMs:. Use it to build a structured path from fundamentals to hands-on practice.
Goal: Replace the brainstorm server's vendored nodemodules with a single zero-dependency server.js using Node built-ins. Architecture: Single file with WebSocket protocol (RFC 6455 text frames), HTTP server (http module), and file watching (fs.watch).
Goal: Add spec and plan document review loops to the brainstorming and writing-plans skills. Architecture: Create reviewer prompt templates in each skill directory.
--- name: writing-plans description: Use when you have a spec or requirements for a multi-step task, before touching code --- Write comprehensive implementation plans assuming the engineer has zero context for our codebase and questionable taste. Document everything they need to know: which files to touch for each task, code, testing, docs they might need to check, how to test it.
NEVER: INSTEAD: Example: your human partner's rule: "External feedback - be skeptical, but check carefully" your human partner's rule: "You and reviewer both report to me. If we don't need this feature, don't add it." Push back when: How to push back: If you're uncomfortable pushing back out loud: Name that tension, then tell your partner about the issue you've seen.
Load this reference when: writing or changing tests, adding mocks, or adding cleanup/helper methods for tests. Two principles govern everything here: Strict TDD produces both naturally: a test written first and watched failing against real code has already proven it can fail, and only earns a mock when the real dependency proves slow or external.