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Found 2,167 Skills
Create or update a DESIGN.md from an existing product repository or public website. Use when asked to document an interface's design language, reconstruct its visual system, extract design tokens and guidance from current evidence, or give coding agents persistent UI context. Do not modify product source or promote accidental implementation patterns into design decisions.
Use when building or editing any AI feature in n8n: AI Agents, Text Classifier, Information Extractor, Sentiment Analysis, Summarization Chain, Basic LLM Chain, embeddings, vector stores, single one-shot LLM calls, or AI media generation (image / audio / video) via the native LangChain provider nodes. Triggers on any `@n8n/n8n-nodes-langchain.*` node, "agent", "chat assistant", "LLM with tools", "tool calling", "fromAi", "system prompt", "memory window", "structured output", "outputParser", "function calling", "RAG", "vector store", "embeddings", "classify with AI", "extract fields with LLM", "sentiment analysis", "summarize with LLM", "single LLM call", chat triggers with files, AI image / video / audio generation, or any multi-turn or one-shot LLM behavior.
Author, audit, and improve Grafana SKILL.md files against Anthropic's published Agent Skills guidance and the four-dimension rubric the grafana/skills CI gate uses (conciseness, actionability, workflow clarity, progressive disclosure). Applies the canonical SKILL.md structure (YAML frontmatter + body + references/ + scripts/ + assets/), the "pushy description" trigger pattern, the three-level progressive-disclosure model, and the validate-fix-rerun feedback loop. Use when creating a new skill in this repo, when reviewing a skill PR, when a skill's Tessl review score is below 75 (the merge gate), when a skill's description isn't getting picked up by agents, when restructuring a long SKILL.md into a bundle, or when the user asks how to write, improve, optimize, audit, or fix a skill - even if they don't say "skill" explicitly (e.g. "this isn't triggering", "Tessl scored this 72", "split this doc").
Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code works but feels brittle, when error handling seems thin, when orphaned resources or missing cleanup are suspected, or when the agent claims done but hidden debt may exist. Catches the specific failure patterns AI agents produce that humans would not.
Deterministic issue-relationship graph over GitHub native sub-issues + dependencies — compute the ready set / parent-rollup candidates / close-kick targets as pure calculation (scripts, no LLM judgment), write real edges when creating spin-off issues, and mutually exclude terminal actions across parallel agents via claim-comment fencing. Called by issue-sweep (candidate injection), issue-review (edge writing + rollup), and the future agent:ready producer routine.
OpenAI Codex (CLI / IDE / cloud) の公式リファレンス。 codex CLI, codex exec, AGENTS.md, rules, subagents, prompting, approvals, sandbox, permission profiles, auto-review, config.toml, profiles, MCP 設定, 環境変数, GitHub Action, Codex SDK, Agents SDK 連携, cloud 委譲, administration, roles, provisioning, analytics API, compliance API, Codex Security 脆弱性スキャン, deep scan, triage, findings, SARIF export, threat model, security hardening, cloud / local / worktree 環境, git worktrees, Record & Replay, GitHub / Linear / Slack 連携。
Create, validate, and enrich Open Knowledge Format (OKF) bundles — the open spec for representing organizational knowledge as markdown files with YAML frontmatter. Use when the user mentions 'OKF', 'Open Knowledge Format', 'knowledge bundle', 'OKF bundle', 'create a knowledge base for agents', 'validate OKF', 'convert to OKF', 'enrich knowledge docs', 'agent-readable knowledge', 'LLM wiki', 'knowledge catalog', 'kcmd', or wants to structure knowledge as markdown files for AI agent consumption. Also use when the user has a directory of markdown files and wants to make them interoperable or conformant with the OKF standard. Even for simple requests like 'make this folder OKF conformant' — the skill has critical structural rules the agent needs.
Best practices for writing Remotion animations that stay intuitive for agents and editable in Remotion Studio Visual Mode.
Guides agents through complete, maintainable MoonBit bindings for C/C++ libraries, from upstream source survey through vendoring, safe API design, documentation tests, and ASan validation. Use when creating or hardening MoonBit native FFI bindings, wrapping C APIs, vendoring C sources into native-stub, or turning a C library into a MoonBit package.
Thin router for Pipefy build and configure asks: map user intent to the correct domain skill (pipes, automations, AI agents, iPaaS, portals, etc.). Use when the user wants to create, build, configure, or integrate something in Pipefy and you need to know which skill to open. Do not use this skill as a delivery playbook — if the user already has a building skill or a detailed build prompt, follow that for how to build; use this skill only for which Pipefy domain skill to read.
Generate a documentation pack for a .NET repository so AI coding agents can reason about it — AGENTS.md, architecture, ADRs, data model, infrastructure, plus a `docs/dotnet.md` deep-dive covering the solution/project graph, target frameworks, EF Core data access, DI, configuration & secrets, analyzers, and CI. Invoke with `/arkandia:agent-context-dotnet [en|es]`.
Set up and maintain a repository's agent instruction layer — one real root instruction file, an AGENTS.md symlink so every agent reads the same document, a comment-convention block, and a drift check that catches documented commands, paths, structures, and counts that no longer match the repo.