Total 32,714 skills, AI & Machine Learning has 5280 skills
Showing 12 of 5280 skills
BF 워크플로우의 사람-시스템 경계 허브. orchestrate를 모드별로 스폰하고, 에픽 단위 루프를 돌며 사람과 소통하는 유일한 경계이다.
Monitor 패턴으로 에픽 내 모든 Story의 구현을 조율한다. 모든 난이도의 Story를 agent에게 위임하고, sprint-status.yaml은 Lead만 갱신한다.
문제를 분석하여 Agent Teams를 자동 구성하고, 4가지 조율 패턴(distribute/monitor/discourse/sequence) 중 최적 패턴으로 협업한다. "/teams {문제 설명}"으로 호출한다.
모드 기반 완전 자율 실행기. plan 모드에서 Story 구조를 생성하고, epic 모드에서 에픽 1개를 자율 실행(implement → E2E → review)한다. 사람과 직접 소통하지 않는다.
Vox single-entry voice orchestration skill. Used to complete environment guarding, CLI installation, on-demand model download, ASR transcription, voice cloning, pipeline execution and task troubleshooting through natural language. It is used when users only describe the target without providing specific commands.
Build a structured taxonomy of failure modes from open-coded trace annotations. Use this skill whenever the user has freeform annotations from reviewing LLM traces and wants to cluster them into a coherent, non-overlapping set of binary failure categories (axial coding). Also use when the user mentions "failure modes", "error taxonomy", "axial coding", "cluster annotations", "categorize errors", "failure analysis", or wants to go from raw observation notes to structured evaluation criteria. This skill covers the full pipeline: grouping open codes, defining failure modes, re-labeling traces, and quantifying error rates.
Use this skill when crafting, reviewing, or improving prompts for LLM pipelines — including task prompts, system prompts, and LLM-as-Judge prompts. Triggers include: requests to write or refine a prompt, diagnose why an LLM produces inconsistent or incorrect outputs, bridge the gap between intent and model behavior, reduce ambiguity in instructions, add few-shot examples, structure complex prompts, or improve output formatting. Also use when the user needs help distinguishing specification failures (unclear instructions) from generalization failures (model limitations), or when iterating on prompts based on observed failure modes. Do NOT use for general coding tasks, document creation, or non-LLM writing.
Generate a custom trace annotation web app for open coding during LLM error analysis. Use when the user wants to review LLM traces, annotate failures with freeform comments, and do first-pass qualitative labeling (open coding). Also use when the user mentions "annotate traces", "trace review tool", "open coding tool", "label traces", "build an annotation interface", "review LLM outputs", or wants to manually inspect pipeline traces before building a failure taxonomy. This skill produces a tailored Python web application using FastHTML, TailwindCSS, and HTMX.
Generates Nano Banana Pro prompts for 4-panel engineer humor comics. Use when user mentions "漫画作成", "エンジニア漫画", "4コマ", or "あるある".
Guide for using the AI's persistent journal database
Complete GRACE methodology reference. Use when explaining GRACE to users, onboarding new projects, or when you need to understand the GRACE framework — its principles, semantic markup, knowledge graphs, contracts, and unique tag conventions.
Use the ClawHub CLI to search, install, update, and publish agent skills from clawhub.ai with advanced caching and compression. Use when you need to fetch new skills on the fly, sync installed skills to latest or a specific version, or publish new/updated skill folders with optimized performance.