Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Diagnoses stuck agents, systemic blockers, and self-healing failures across the Loom orchestration layer. Use when an agent is looping, a recovery sweep fails to clear stalled beads, dispatch logs show repeated errors, or the organization itself is malfunctioning. Performs root cause analysis, loop detection, recovery pattern repair, and threshold tuning for Loom's agent infrastructure.
Avalie honestamente a qualidade do trabalho de IA usando um sistema de pontuação em dois eixos. Use após concluir uma tarefa, revisão de código ou sessão de trabalho para obter uma avaliação imparcial. Detecta inflação de pontuação, força raciocínio do advogado do diabo e persiste pontuações entre sessões.
Use when 王建硕 wants to evaluate whether a change to VoiceDrop's 挖矿 system prompt is actually better than the live version — runs the local eval harness (golden fixtures × champion-vs-candidate, same input), dispatches blind pairwise judge subagents, aggregates a win-rate verdict, and on approval promotes the candidate into agent/src/prompts/mine.js. Triggers — "评估 prompt"、"挖矿 prompt 改好了吗"、"eval prompt"、"比一比两版 prompt"、"/wjs-evaling-voicedrop-prompts".
Use when 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md, used by the every-6h tweet Action) and finding which prompt version produces the highest-reach tweets. Each prompt edit is a git-SHA-versioned, numbered experiment with a hypothesis; tweets are attributed to the version live at post time and judged on median impressions per tweet. Also mines per-tweet impression data for content-feature signals (angle / length / topic) that feed the next prompt edit. North-star = impressions per tweet. Triggers — "改 X 的 prompt", "X 内容改进", "哪版 prompt 最好", "什么内容 impression 高", "improve my tweets", "A/B test the X prompt", "/wjs-x-improving-content".
Use this skill when the user wants to build AI applications with Weaviate. It contains a high-level index of architectural patterns, 'one-shot' blueprints, and best practices for common use cases. Currently, it includes references for building a Query Agent Chatbot, Data Explorer, Multimodal PDF RAG (Document Search), Basic RAG, Advanced RAG, Basic Agent, Agentic RAG, and optional guidance on how to build a frontend for each of them.
Use when building a devtool with devframe — the framework- and build-tool-agnostic foundation for defining a devtool once and serving it in many places. Covers DevframeDefinition, picking the right deployment adapter (cli / build / spa / vite / embedded / mcp), designing RPC contracts, exposing an agent-native surface over MCP, and wiring the author's SPA client. For host-level features (docks, terminals, palette, etc.), the devframe can be mounted into a host that provides them — Vite DevTools is one supported target, reached via the `vite` adapter. Triggers on `devframe` imports, `defineDevframe`, `createCac`, `createMcpServer`, `connectDevframe`, and on migrations of existing inspectors (eslint-config-inspector, unocss-inspector, node-modules-inspector-style tools) to devframe.
Automatic quality auditor and continuous improvement engine for the scholar-skill suite. Four modes: (1) OBSERVE — post-skill output audit (run after any skill); (2) AUDIT — skill-suite structural health check; (3) IMPROVE — propose and apply fixes to skill definitions; (4) EVOLVE — cross-session pattern analysis and systemic improvements. Designed to run automatically at the end of any scholar-skill invocation.
Implement multi-agent coordination patterns (supervisor-subagent, router, orchestrator-worker, handoffs) for LangGraph applications. Use when users want to (1) implement multi-agent systems, (2) coordinate multiple specialized agents, (3) choose between coordination patterns, (4) set up supervisor-subagent workflows, (5) implement router-based agent selection, (6) create parallel orchestrator-worker patterns, (7) implement agent handoffs, (8) design state schemas for multi-agent systems, or (9) debug multi-agent coordination issues.
Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the `deepagents` package. Use when users need to create agents with built-in planning/filesystem/subagents, configure middleware/backends/checkpointing/HITL, migrate from `create_react_agent` or `create_agent`, scaffold projects with repo scripts, validate agent config files, and confirm compatibility with current LangChain/LangGraph/LangSmith docs.
Implement LangGraph error handling with current v1 patterns. Use when users need to classify failures, add RetryPolicy for transient issues, build LLM recovery loops with Command routing, add human-in-the-loop with interrupt()/resume, handle ToolNode errors, or choose a safe strategy between retry, recovery, and escalation.