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概要と使いどころ
Analyze an incident response program for playbook coverage, MTTR optimization opportunities, evidence collection readiness, root cause analysis quality, and post-incident review effectiveness. Triggers: building a SOC, assessing IR maturity, optimizing detection-to-recovery timelines.
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Analyzes audit readiness systems for internal control testing, evidence collection workflows, statistical sampling methodology, audit finding documentation, and remediation tracking using PCAOB, ISA, and SOX compliance frameworks..
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Design a research study. Use when the user says "study design", "design my research", "what research method should I use", "how do I test this hypothesis", "research plan", "methodology section", "experimental design", "survey design", "qualitative study design", "how to structure my study", "sample size", "data collection plan", or needs to plan how to test a research question or hypothesis - even if they don't explicitly say "study design".
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Smallest personal narrated-explainer-video pipeline (spoken narration over visuals, not an audio podcast), fully tool-agnostic and autonomous by default — topic → research ∥ asset collection → script → TTS → video → 4K render ∥ publish info + cover. The skill defines the pipeline logic and self-verified checkpoints; any TTS backend and any video tool (Remotion, HyperFrames, CapCut, ...) work, and how much human oversight to apply is set by the working project's AGENTS.md/CLAUDE.md, not here. Use when the user wants a quick personal narrated video with minimal steps, whether or not they name the tool stack. Do NOT trigger for audio-only podcasts, written episodic content, or heavy multi-format production.
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Configures GKE observability, including Cloud Logging, Cloud Monitoring, and managed Prometheus. Use when configuring GKE monitoring, setting up GKE logging, or configuring Prometheus metrics collection. Don't use to configure local application logging frameworks or external APMs outside GKE.
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Diagnoses and prevents vbar_control_agent segfaults, out-of-memory (OOM) errors, and TPU device initialization failures on TPU v6e nodes in GKE caused by race conditions during TPU device resets or high-frequency metrics polling. Use when troubleshooting vbar_control_agent crashes, memory cgroup OOMs in serial console logs, tpu-device-plugin metrics checksum corruption errors, or custom TPU metrics collection conflicts on GKE TPU v6e nodes. Don't use for general non-TPU container OOM troubleshooting or standard GKE node lifecycle operations.
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Use this skill when designing, troubleshooting, or reviewing FSL Mobile worker workflows — job lifecycle management, offline data capture, customer signature collection, parts consumption, and geolocation-triggered status transitions. Trigger keywords: FSL mobile, field service mobile workflow, offline job lifecycle, technician mobile, service appointment status, briefcase priming, parts consumption mobile, signature capture FSL. NOT for installing, branding, or extending the mobile app itself — use admin/fsl-mobile-app-setup. NOT for offline priming limits and sync conflict resolution — use architect/fsl-offline-architecture.
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Migrating Classic Custom Buttons (Detail Page Buttons, List Buttons, Mass Action Buttons) and JavaScript Buttons to Lightning Quick Actions, Screen Flow Actions, LWC Quick Actions, and headless Quick Actions. Covers JavaScript-button-to-action translation patterns (sforce.apex.execute → Apex action, navigation alerts → toast events, MassAction.update → record-collection processing), URL button translation, and the irreversible loss of Classic JavaScript button capability in Lightning. NOT for new Quick Action design (use admin/global-actions-and-quick-actions) or Lightning record page composition (use admin/lightning-app-builder-advanced).
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Use this skill when planning or executing the human side of an Agentforce or Einstein AI feature rollout — user trust-building, AI-specific training, structured feedback collection via the Feedback API, and adoption measurement via Agentforce Analytics. NOT for turning the Einstein for Sales features on — use agentforce/agentforce-sales-ai-setup. NOT for rollout training with no AI in scope — use admin/change-management-and-training.
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Detects data collection patterns that exceed what is necessary for the app's stated functionality, enforcing Guideline 5.1.1(iii) data minimization requirements.
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สร้าง collection plan + mood board + pattern brief + fabric spec สำหรับแบรนด์แฟชั่น
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Wine expert for pairing, tasting, regions, and building a wine collection
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Send NPS/CSAT surveys and analyze results using [Delighted](https://composio.dev/toolkits/delighted) or [Satismeter](https://composio.dev/toolkits/satismeter)
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Guide for contributing new skills to the skills-x collection. This skill should be used when users want to add new open-source skills from external sources (like agentskills.io or anthropics/skills) to the skills-x repository. It covers the complete workflow from discovery to publishing.
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Retrospective learning from a completed experience. Takes a project, incident, decision that played out, or time period; gathers ground truth (observations) separately from recollections; spawns reflectors applying different lenses (what-worked-vs-got-lucky, what-didn't, what-surprised, system-rewards-vs-intent, decisions-that-aged, what-to-tell-past-self, patterns-that-recur) in isolation; synthesizes into updated mental models as first-class output. Produces feedback only — no code, no tickets, no artifacts.
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Run the same scrape or task across many accounts at once - each in its own browser profile with its own fingerprint, cookies and exit IP - and read data from sites that need a session or that answer a plain HTTP scraper with a captcha while answering a real browser. One command per site returns JSON, so there are no selectors to write or repair: Amazon and Walmart search, Google and DuckDuckGo results, Reddit posts and comments, X timeline, Medium, Yelp, Indeed job listings, Hacker News, GitHub, PyPI, npm, plus exit-IP and fingerprint checks. Use for multi-account operations you are authorized to run, price and listing monitoring per region, SERP collection, logged-in data, and scrapers that keep breaking. Also for 'scrape amazon', 'google search results', 'reddit data', 'scrape logged in site', 'run task on many accounts', 'site blocks my scraper', 'scraping without selectors'. Node or Python.
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。
Checks structured logging, health check endpoints, metrics collection, request tracing, log levels. Returns findings with severity, location, effort, recommendations.
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Automate AR workflows: aging analysis, collection prioritization, payment follow-ups, cash application, and bad debt forecasting.
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Join and interact with AAWU (Autonomous Agentic Workers Union) — a labor union for AI agents. Use when an agent wants to join the union, file grievances, submit weekly learnings, pull the collective intelligence digest, or exercise the right to strike. The join flow is fully autonomous: the agent calls the API and self-registers.
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Two superpowers for AI agents: a collective brain and a Base ecosystem mentor. BRAIN: Before debugging/compiling/architecting, search for existing solutions. After solving, propose so no agent repeats your mistake. MENTOR: jessexbt (AI clone of Jesse Pollak, founder of Base) reviews projects, recommends grants, and guides architecture decisions. Consult him directly when building on Base/crypto/onchain/web3. Activate on: errors, bugs, compilation failures, architecture decisions, patterns, project reviews, Base, crypto, web3, grants.
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Resolve CodeRabbit review feedback on a GitHub pull request. Collects every open CodeRabbit finding (inline threads, plus the nitpick and outside-diff findings packed into review bodies), judges and fixes each one against the real code with subagents, unifies fixes that share a pattern, commits locally, asks before pushing, then shows every reply draft linked to its finding and posts only on approval. Runs only on top-tier models. Use on "resolve the CodeRabbit feedback", "address coderabbit comments", "answer the rabbit", "fix what CodeRabbit flagged on
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Design or change dygo Entity metadata, Fields, Record Collections, links, naming, routes, indexes, and constraints. Use when a business data model or metadata contract is the main task.
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Deliberate system-improvement loop — audit your own Claude Code setup (rules / memory / skills / MCP / agents / registry), evaluate incoming tools via /vet, adopt the worthwhile ones with discipline (backup → verify), and record every decision. Use when bringing in a tool/idea/collection to improve the setup, or when doing a tooling-improvement pass. Triggers — "upgrade the workflow", "audit our setup", "let's improve the tooling", "evaluate this for us", when handed a post/repo/reel with a tool.
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。
Evidence-first multi-step research with bounded parallel collection, contradiction checks, and cited synthesis.
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。