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Found 44 Skills
Use when the requested deliverable includes creating, reviewing, moving, splitting, or updating a repository AGENTS.md; produces scoped repo guidance. Do not trigger for standalone skills, prompts, non-AGENTS instruction files, READMEs, changelogs, or architecture docs.
Guidelines for creating well-structured AI agent skills. Use when building a new skill, reviewing skill quality, or unsure how to organize a skill.
Use when building AI agent storage workflows on Tigris — forks for isolated dataset copies, workspaces for per-agent buckets with TTL, checkpoints for snapshot/restore, and coordination for event-driven pipelines via bucket webhooks. Triggers on "@tigrisdata/agent-kit", "agent storage", "agent workspace", "agent fork", "isolated agent environment", "checkpoint and restore", "bucket webhook", "multi-agent pipeline"
Use jj (Jujutsu) for local version control instead of git. Activate when: the repo has a .jj/ directory, the user or project config mentions jj, the user says 'use jj', or any version control operation is needed in a jj-managed repo. Also use this skill when the user asks to commit, branch, stash, rebase, or perform any git-like operation in a repo that uses jj. If unsure whether the repo uses jj, check for a .jj/ directory.
Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.
Turn a recurring chore into a Superset automation — drafts the agent prompt, confirms schedule and target, creates it with the CLI, and reviews the first run together. Use when the user wants a scheduled or recurring agent, a daily/weekly job, or to automate a repeating task with Superset.
Build LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.
Execute Python code in isolated rootless containers with MCP server proxying for token-efficient agent workflows
Design, apply, and maintain SKOS taxonomies for joelclaw agent workflows. Use when defining concept schemes, classifying agent inputs/outputs, mapping to external vocabularies, or integrating taxonomy metadata with Typesense retrieval.
Reference skill for building production-ready crw integrations. Covers verb selection, call surfaces (CLI/MCP/REST), post-filtering strategies, context-window hygiene, Hybrid RAG patterns, common pitfalls, and crw-specific operational considerations (search backend limits, renderer pool, proxy rotation). Load this when writing application code that embeds crw, designing a multi-step agent workflow, or debugging an integration that isn't behaving as expected.
Operational prompt engineering for production LLM apps: structured outputs (JSON/schema), deterministic extractors, RAG grounding/citations, tool/agent workflows, prompt safety (injection/exfiltration), and prompt evaluation/regression testing. Use when designing, debugging, or standardizing prompts for Codex CLI, Claude Code, and OpenAI/Anthropic/Gemini APIs.
Build voice AI agents with LiveKit Cloud and the Agents SDK. Use when the user asks to "build a voice agent", "create a LiveKit agent", "add voice AI", "implement handoffs", "structure agent workflows", or is working with LiveKit Agents SDK. Provides opinionated guidance for the recommended path: LiveKit Cloud + LiveKit Inference. REQUIRES writing tests for all implementations.