Structured code review through the shared OpenClaw agent-skills installation.
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2.4천건 · 관련도순
Structured code review through the shared OpenClaw agent-skills installation.
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Write Estuary derivations in Python — async-generator transforms, Pydantic-typed source/output, optional persistent state, and dependency installation via uv. Use for ML/embeddings, async API calls, or any transform where SQL and TypeScript are awkward. Derivations add complexity and cost (and Python derivations require a private/BYOC data plane) — confirm the user wants a Python derivation before reaching for this. Use when user says "python derivation", "write a derivation in python", "embed with a python derivation", "ML derivation", "call an API from a derivation", or anything mentioning model2vec / huggingface / pydantic in a derivation context.
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Diagnose and fix rad-mem installation issues. Checks PM2 worker status, database integrity, service health, dependencies, and provides automated fixes for common problems.
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Use when converting an existing repo to the current LocalSetup framework with backup, blocker, install, and verification gates.
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Refresh owner-specific mutable provenance and generated documentation without rewriting release or installed-target evidence.
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LocalSetup framework context - overview, invariants, resolver rules, install layout, and skills index. Load first when working in a repo that uses LocalSetup.
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Security-first skill vetting for AI agent skills. Use before installing any skill from public registries (e.g. skill hubs, GitHub) or other sources. Checks for red flags, permission scope, and suspicious patterns.
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Match user tasks to installed LocalSetup skills, recommend top matches, and run single-task or batch skill-selection flow with minimal interruption. Delegates complementary public-skill discovery to ls-find-public-skills.
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Use when managing cron from a repo-local manifest: time triggers, on-boot-with-delay, sequenced tasks; create, remove, reorder, install.
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VibeFarsi offline/vendor workflow: production-level CLI install in local project; no MCP. Use when implementing, reviewing or testing vibefarsi offline/vendor workflow for React, Vite, Next.js or Perfect_AI projects.
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Use tsift for token-efficient repository navigation, code search and reading, call graphs, diffs, logs, tests, session context, and workspace memory. TRIGGER: exploring or changing a codebase with tsift installed, or working on the tsift repo. SKIP: plain file-level glob, non-code web search. VERSION CHECK: compare `tsift --version` against tsift-version below before trusting any command text copied from this file.
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How to migrate the tdmcp Claude Desktop bundle from legacy .dxt to .mcpb (MCP Bundle) and keep it building — covers the manifest schema (verify against the installed packer, never hardcode), the build-dxt.mjs packer/zip-fallback flow, the npm scripts, and the full .dxt→.mcpb reference sweep across docs and scripts. Use when packaging or migrating the tdmcp desktop bundle.
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Add released versions of Apache SkyWalking AI Sessionizer to its apt repository, static/apt in apache/skywalking-website, which the website serves at https://skywalking.apache.org/apt. Verifies the voted .deb packages, adds them to the index with apt-ftparchive, signs the index with the release manager's key, writes the .htaccess redirects, installs through them with apt, and opens a pull request to apache/skywalking-website. Use after a version is published.
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Publish a released version of the Apache SkyWalking AI Sessionizer LangChain plugin, apache-skywalking-asz-langchain, to PyPI. Says what a release manager sets up before the first upload, downloads the voted source package, verifies it, builds the source distribution and the wheel from plugins/langchain inside it, checks and installs them, and uploads them with twine. Use after a version is published.
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Resolve all map-referenced skills through 4-tier chain and provision installs with idempotent updates
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Bootstrap a new Gradle module in any Kotlin Android project. Walks the user through choosing one of three module types — library module (`com.android.library`), feature module (regular Android library that hosts a feature/UI, install-time only), or dynamic feature module (`com.android.dynamic-feature`, on-demand/conditional/instant delivery). Use whenever the user wants to add a module, modularize code, extract a util/data/ui layer, create a new feature, set up Play Feature Delivery, or asks "should this be a library or feature module?". Discovers the project's existing conventions (plugin aliases, namespaces, sibling modules, version catalog) instead of hardcoding any. Pushes back on bad combos (e.g. utility code in a dynamic feature). Stops at scaffolding; app-level wiring is out of scope.
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Use Kanpeki component library in React projects. Activate when user wants to add, use, customize, or compose accessible UI components from Kanpeki (@kanpeki/*). Covers installation, component patterns, forms, theming, and dark mode with React Aria Components + Tailwind CSS 4.1 + CVA.
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How to upgrade `alchemy` and the `effect` constellation across this repo and keep them consistent for consumers: which packages pin what, why `effect` and every `@effect/*` companion move as one set, what breaks in a typical upgrade, and how to verify a standalone `npm install` still resolves a single `effect`. Use when asked to upgrade or bump alchemy or effect, when a deploy dies inside an alchemy provider with a `TypeError` naming a missing combinator, when `check:npm-effect-resolution` fails, or when deciding whether the alchemy patch is still needed.
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Route Tavily web-search, extraction, mapping, crawling, and cited-research requests to the narrowest `tvly` command. Use for command-line Tavily work, installation checks, authentication setup, or choosing among the specialized Tavily skills.
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Guide for creating or updating effective portable skills with specialized knowledge, workflows, or tool integrations. Use for Codex, Claude Code, GitHub Copilot, or shared-catalog skill work after selecting the target host and installation path.
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Guide users through configuring key MongoDB MCP server options. Use this skill when a user has the MongoDB MCP server installed but hasn't configured the required environment variables, or when they ask about connecting to MongoDB/Atlas and don't have the credentials set up.
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Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow.
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Expert en ComfyUI workflows avancé (install, custom nodes, model management, workflow design)
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Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.
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