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Found 231 Skills
Plan work before coding: do repo research, analyze options/risks, and ask clarifying questions before proposing an implementation plan. Use when the user asks for a plan, design/approach, scope breakdown, or implementation steps.
Bug Fixing Skill - Read bugs/pending.csv to select bugs and automatically execute the 8-stage repair process
Quick answers to questions without heavy note-taking overhead
Create AiderDesk agent profiles via interactive Q&A.
Build Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries, or knowledge bases.
Validate completed implementation against plan tasks and acceptance criteria. Use when: (1) Implementation is complete, (2) User wants validation before merging/shipping, (3) Quality gate check needed after implementation. Reviews ALL plan tasks for implementation correctness, test adequacy, and code quality. Produces structured feedback (approve, request changes, or comments) - does NOT fix code.
Guide for implementing Google Gemini API document processing - analyze PDFs with native vision to extract text, images, diagrams, charts, and tables. Use when processing documents, extracting structured data, summarizing PDFs, answering questions about document content, or converting documents to structured formats. (project)
Write, review, or debug end-to-end tests using Playwright. Use when asked to 'write e2e tests', 'add Playwright tests', 'test this user flow', 'fix flaky tests', 'create a test suite', or 'debug this e2e failure'. Invoke with /playwright-e2e or when user mentions e2e tests, Playwright, or test automation. Do NOT use for live browser interaction via MCP tools — use playwright-mcp for that. Do NOT use for unit/integration tests — use tdd-guide agent instead.
QUERY LENGTH LIMIT EXCEEDED. MAX ALLOWED QUERY : 500 CHARS
TDD feature build loop: spec (RED) → implement (GREEN) → refactor. Pass the feature name as argument.
Local RAG system management with RLAMA. Create semantic knowledge bases from local documents (PDF, MD, code, etc.), query them using natural language, and manage document lifecycles. This skill should be used when building local knowledge bases, searching personal documents, or performing document Q&A. Runs 100% locally with Ollama - no cloud, no data leaving your machine.
Design a product-led sales motion from usage signals to sales handoff and conversion.