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概要と使いどころ
Reconcile this identity with the relay — republish keys, replenish one-time keys, rotate the fallback, and resend unacknowledged mail. Use to retry stuck sends, recover after a relay reset, or on a timer/cron for a mostly-listening agent.
日本語の概要は準備中です。原文の説明を表示しています。
Runtime per-test healing with evidence: multi-attribute selector healing, environment-aware diagnosis, flake classification, quarantine management, and confidence-scored auto-repair. Goes beyond simple locator fallbacks to cover action-level healing, data healing, and observable repair workflows. Use when: "flaky test," "test stability," "self-healing locator," "broken locator recovery," "unreliable test," "quarantine flaky test." Not for: bulk regenerating selectors after a planned UI refactor — use selector-drift-recovery (this skill heals ONE test at runtime; that one rewrites many tests offline). Not for: classifying/clustering CI failures into bug reports — use ai-bug-triage. Related: playwright-automation, selector-drift-recovery, ci-cd-integration, qa-metrics, ai-bug-triage.
日本語の概要は準備中です。原文の説明を表示しています。
DyNote: systematically and efficiently extract raw Douyin/DY video data and analyze videos, comments, accounts, hashtags, and short-video scenes into evidence-graded learning notes, summaries, research briefs, scripts, and knowledge-base material. Use when the user asks to 抓取/提取/整理 抖音视频字幕、视频文案、ASR 转写、Qwen3-ASR 中文转写、原始材料归档、学习笔记、analysis plan、note budget、避免返工、复用已有素材、评论洞察、账号分析、赛道/话题研究、竞品拆解、电商/本地生活视频分析、事实核查、自动搜索素材, save Douyin content as Markdown/TXT, or use subtitle/local ASR as the factual spine with logged-in Douyin Web built-in AI / Doubao fallback as visual or quick-reading supplements.
日本語の概要は準備中です。原文の説明を表示しています。
Builds a content strategy a small team can execute: audience needs backed by counted evidence, an audit of what is already published, a topic map with one page per search intent, a scored backlog, a 90-day calendar sized to real capacity, and the numbers that will show whether it worked. Use when someone asks "what should we write about", "plan our blog", "content strategy", "content plan for next quarter", "topic clusters", "content audit", "our blog traffic is falling", or wants a pile of content ideas turned into a prioritised plan.
日本語の概要は準備中です。原文の説明を表示しています。
A/B and multivariate testing methodology. Design experiments, calculate sample sizes, determine statistical significance, avoid common pitfalls, and interpret results. Platform-agnostic framework applicable to landing pages, emails, ads, pricing, and product features. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, or conversion experiments.
日本語の概要は準備中です。原文の説明を表示しています。
Patterns for building robust, reproducible genomics analysis pipelines. Covers workflow managers, NGS data processing, variant calling, RNA-seq, and common bioinformatics pitfalls. Use when ", " mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when "keywords, file_patterns, code_patterns, " mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
Expert patterns for AI-powered image editing including inpainting, outpainting, ControlNet, image-to-image, and API integration with Replicate, Stability AI, and FalUse when "ai image editing, inpainting, outpainting, controlnet, image to image, remove object from image, extend image, flux inpaint, sdxl editing, image-editing, inpainting, outpainting, controlnet, stable-diffusion, flux, replicate, stability-ai, comfyui" mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
De-slop pass for any text. Scans for the statistical fingerprints of AI writing (negative parallelism / "not X but Y", em-dash density, rule-of-three, false ranges, puffery vocabulary, uniform cadence, hedged both-sidesing), rewrites by meaning, then re-scans until the text is clean and sits in the right register for its genre: academic article, tweet, reddit post, email, blog, docs, marketing. Use when the user says "deslopify", "deslop", "de-slop this", "remove the AI tells", "humanize this", "make this not sound like AI", or invokes /deslopify. Also use before publishing any agent-drafted prose.
日本語の概要は準備中です。原文の説明を表示しています。
Transparent, itemized impressions of AI-generated WRITING STYLE — the repo's ONLY non-integrity track. Two passes: a deterministic defensive-hedge density screen (tools/check_ai_style.py, AIS-DEFENSIVE-HEDGE) plus a fresh cross-model GROSS-cases-only semantic pass over the 13 AIS-* style tells (broken narrative arc, LLM phrase tics, jargon-stuffing, invented codenames, clause/formula walls, gratuitous pseudocode, bullet overuse, bold-module spam, restatement loops, focus drift, single-style figures, appendix dumping). Every finding is named, LOCATED, span-anchored to the evidence ledger (claims.json), carries not_integrity_finding:true + false_positive_risk:high + an fp_case, and gets ZERO verdict weight: the adjudicator forces it to info, excludes it from overall_verdict, and renders it in a SEPARATE report section. NOT an AI-text classifier — no scores, no "this is AI-written", no authorship probability; a paper can be CLEAN_GIVEN_EVIDENCE and still list many. Emits ai-style-impressions.findings.json; computes NO verdict; if a tell is actually substantive it routes to the integrity auditor. Triggers: "AI style", "vibe check", "writing fingerprint", "AI 文风", "vibe paper".
日本語の概要は準備中です。原文の説明を表示しています。
Checkable-ish surface presentation signals a reviewer notices first — duplicate/near-identical tables, leftover pipeline/template strings, too-few or LLM-looking figures, and page-padding. AUXILIARY ONLY and weak by design: a deterministic pass (tools/check_presentation.py — dup-table + pipeline-artifact) plus a fresh cross-model GROSS-cases-only semantic pass (thin-float, LLM-figure, page-padding), every above-info finding span-anchored to the evidence ledger (claims.json). The adjudicator CAPS everything at minor (SURFACE_ONLY_SKILLS + SURFACE_PATTERNS) — these contribute at most SOFT_FLAGS, never a HARD verdict — default false_positive_risk:high. NOTE: the pure AI writing-STYLE impressions (AI-flavor prose, defensive 'not-X-but-Y' hedging, narrative-arc, jargon-stuffing, invented codenames) MOVED to the zero-verdict-weight AIS track — for those use skills/ai-style-impressions, NOT this. Emits presentation-signals.findings.json; NEVER computes the verdict. Triggers: "presentation signals", "surface check", "duplicate tables", "排版信号".
日本語の概要は準備中です。原文の説明を表示しています。
Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
日本語の概要は準備中です。原文の説明を表示しています。
Binding-resolved Python symbol queries via pyright — every true caller (--refs), the real definition (--def), or an inferred signature (--hover) of a .py symbol, excluding the same-named false positives text search cannot tell apart. Use when a task needs ALL callers or users of a named Python symbol and grep would over-match. Python only, and only for the caller/definition question: measured 2026-07-04 on real issue-localization tasks, plain ripgrep tied or beat the semantic and indexed-regex tiers at 4-60x less wall-clock, so those tiers survive below without being a default.
日本語の概要は準備中です。原文の説明を表示しています。
Puts oaustegard/muninn-utilities on the container Python path — runs scripts/boot.sh, which shallow-clones the repo (codeload tarball as fallback) and writes the .pth. The boot payload itself comes from the Muninn MCP connector: call its `boot` tool after this script. Trigger on the first message of any new Muninn conversation, or when the user says "boot up" / "boot" — unless the message contains "skip boot". Idempotent: a warm container exits in ~0s via a sentinel.
日本語の概要は準備中です。原文の説明を表示しています。
Tulis dan sunting teks Bahasa Indonesia agar jelas, spesifik, alami untuk audiensnya, dan sesuai suara yang diminta. Gunakan untuk artikel, esai, blog, media sosial, atau permintaan mengurangi pola tulisan generik. Jangan menjanjikan hasil detektor AI.
日本語の概要は準備中です。原文の説明を表示しています。
Edit English prose for clarity, specificity, natural flow, and fit to the requested voice. Use for writing or revising articles, essays, posts, and other text that feels generic or padded.
日本語の概要は準備中です。原文の説明を表示しています。
Use when receiving code review feedback (especially if unclear or technically questionable), when completing tasks or major features requiring review before proceeding, or before making any completion/success claims. Covers three practices - receiving feedback with technical rigor over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before any status claims. Essential for subagent-driven development, pull requests, and preventing false completion claims.
日本語の概要は準備中です。原文の説明を表示しています。
Watch a video from YouTube, Instagram, X/Twitter, Vimeo, TikTok or any of ~1800 yt-dlp sites (or a local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or local mlx-whisper fallback, no API key), and hands the result to Claude so it can answer questions about what's in the video.
日本語の概要は準備中です。原文の説明を表示しています。
Query Google NotebookLM notebooks for source-grounded, citation-backed answers from user-uploaded documents. Manages notebook library, handles Google authentication, and supports smart discovery. Works standalone via /blog notebooklm or internally from blog-write and blog-researcher for Tier 1 research data. Falls back gracefully when not configured. Use when user says "notebooklm", "notebook", "query notebook", "ask notebook", "notebook research", "source grounded research", "document query", "notebook library".
日本語の概要は準備中です。原文の説明を表示しています。
AI image generation and editing for blog content powered by Gemini via MCP. Claude acts as Creative Director - interpreting intent, selecting domain expertise, constructing optimized 6-component prompts (Subject + Action + Context + Composition + Lighting + Style), and orchestrating Gemini for blog-quality results. Generates hero images, inline illustrations, social preview cards, and OG images. Edits existing blog images. Supports 6 blog-optimized domain modes (Editorial, Product, Landscape, UI/Web, Infographic, Abstract). Works standalone via /blog image or internally from blog-write and blog-rewrite workflows. Falls back gracefully when MCP is not configured. Use when user says "blog image", "generate hero image", "blog illustration", "social card", "generate blog image", "edit blog image", "image generate", "blog cover image", "inline image", "OG image".
日本語の概要は準備中です。原文の説明を表示しています。
Generate audio narration of blog posts using Google Gemini TTS. Supports summary narration, full article read-aloud, and two-speaker podcast/dialogue mode with 30 voice options. Outputs MP3 with HTML5 audio embed code. Works standalone via /blog audio or internally from blog-write. Falls back gracefully when API key is not configured. Use when user says "blog audio", "narrate blog", "audio version", "text to speech", "tts", "podcast mode", "read aloud", "audio narration", "voice", "narration", "generate audio".
日本語の概要は準備中です。原文の説明を表示しています。
Use Crabbox for ClawBench remote Linux validation. Default to Blacksmith Testbox; includes direct Blacksmith and owned AWS fallback notes when Crabbox fails.
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Use when turning any content into clear, professional visualizations - board slides, reports, proposals, research summaries, training materials, technical diagrams, infographics, process flows, timelines, benchmarks, waterfall charts, or data-backed visual specs for any audience.
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Computes a numeric or categorical answer to a quantitative data-science question by cleaning and analyzing local data files (CSV, Excel, TSV, and scientific formats .npz/.fits/.h5) with pandas, numpy, and scipy. Use whenever a task ships its own dataset (in whatever local directory it provides) and asks you to derive a value via statistical, geospatial, temporal, ratio, percentage, growth-rate, correlation, signal-processing, orbital, or forecasting analysis. Spans domains including archeology (demographics, paleoclimate proxies, radiocarbon dating, historical conflicts); biomedical/bioinformatics (tumor histology, genes, proteins, peptides, genomic variants); environmental (water quality, bacterial/Enterococcus exceedance, rainfall correlations, environmental justice); legal/consumer (fraud, identity theft, consumer complaints by state/metro); wildfire (incidents, causes, suppression costs, acres burned, fatalities, geospatial intersections); and astronomy/heliophysics/space weather (satellite telemetry TLE/Swarm/SP3, indices OMNI/GOES, solar activity, atmospheric density, orbital mechanics, forecasting).
日本語の概要は準備中です。原文の説明を表示しています。