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Found 1,564 Skills
Index and search Claude Code sessions using semantic embeddings (Gemini). Find past sessions by topic, relaunch the best match. Triggers on "find session", "which session did I", "relaunch the session where", "session about X".
Generate or update AGENTS.md for a repository or VS Code workspace, create CLAUDE.md and GEMINI.md shims, build a project matrix with stable project codes, and include UBIQUITOUS_LANGUAGE.md as required context.
OpenRouter integration. Manage data, records, and automate workflows. Use when the user wants to interact with OpenRouter data.
MCP Server Builder
Use Open CoDesign to generate prototypes, slides, and PDFs from prompts with Claude, GPT, Gemini, or local models
Produce a token-bounded context pack from the Obsidian wiki — a compact, structured slice of the most relevant pages for a topic or recent activity, designed for downstream consumption by another agent or skill. Use when the user says "/wiki-context-pack", "make a context pack", "give me a context slice for X", "pack the wiki for my agent", or "bounded context for Y". Different from wiki-query (which answers a question) — this produces reusable input material for a downstream task.
Debug AutoDeploy accuracy regressions vs a reference score (PyTorch backend or published baseline). Use when an AutoDeploy model's eval score is significantly below the reference and the root cause is unknown.
Iterate on RAG systems with structured evals instead of eyeballing. This skill should be used when the user is tuning a RAG pipeline — changing retrieval prompts, swapping models, adjusting chunking, or debugging poor answers — and wants a cheap, ranked set of experiments with cost tracking and structured feedback on the stack. Also use when the user asks "how do I know if my RAG is working?", "this RAG eval is burning money", or "what should I try next on retrieval?".
Build modular Agentic RAG systems with LangGraph, featuring hierarchical indexing, conversation memory, and multi-agent query processing
Build and deploy autonomous AI agents with CowAgent - planning, memory, knowledge base, skills, and multi-channel support
Rewrite text to remove AI tells. Use when editing or reviewing writing that sounds like a chatbot wrote it. Detects and replaces inflated significance, rule-of-three, em dash overuse, AI vocabulary, condescending openers, reader-lecturing, signposting, and other tells.
When the user wants to build or improve a sales bot's ability to automatically categorize why deals closed or died. Also use when the user mentions "win/loss analysis," "deal outcome," "loss reason," "closed reason," or "deal categorization."