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Found 5,237 Skills
Spawn parallel adversarial subagents that DRIVE THE REAL SYSTEM (not reason from source) to break new code before it ships — schemas, contracts, parsers, serializers, protocols, state machines, security/redaction, freeze guards. Use as a standard step on any nontrivial feature/task once the implementation is green and BEFORE the final convergence (dootsabha) review. Each agent gets one disjoint attack surface + a "try to break X" charter; every finding is independently reproduced before it is believed, then fixed with a regression test. Trigger phrases include "adversarial review", "try to break this", "find the bugs I missed", "harden before done", "attack this schema/parser/contract", and the shux feature-protocol adversarial step.
The AI music + sound-design skill for social -- original/licensed audio beds and sound design for Reels/TikToks/Shorts/videos. Use when someone needs background music, a track, or sound effects for a social video, asks which AI music tool is safe to use, or asks "can I use this trending sound/song on my brand video?". The real brief is "audio that won't get muted, claimed, or sued," so it picks the safest licensed source and never uses copyrighted or trending music without a license. Uses the SCORE framework. Reads brand-profile + the video it scores first. The agent briefs the music + sound design, picks the safest licensed source (ElevenLabs Music/SFX or stock libraries over Suno/Udio; paid tier for commercial rights), and advises licensing/Content-ID/disclosure. The tool generates/licenses the audio; the creator bakes it in; WoopSocial publishes the video and does NOT generate music. Pure AI music may not be copyrightable; never "100% legally safe." Pairs with ai-voiceover.
Manage Huawei Cloud ModelArts training jobs and related resources through full lifecycle operations via hcloud CLI. Covers 52 API interfaces across 8 functional domains: training job management, algorithm management, training job tags, training experiments, training job events, model import, auto search (hyperparameter tuning), and training image save. All write operations require user confirmation before execution. Triggers include: "ModelArts training", "训练作业", "模型训练", "创建训练作业", "查询训练作业", "停止训练作业", "删除训练作业", "算法管理", "超参配置", "training job", "training management", "create training", "ModelArts 训练", "训练实验", "自动搜索", "超参调优".
Set up Claude Code hooks to intercept dangerous Git commands (push, reset --hard, clean, branch -D, etc.) before execution. Use this when users want to prevent destructive Git operations, add Git security hooks, or block git push/reset in Claude Code.
Convert deep research output (Claude Research, ChatGPT Deep Research, Gemini, Codex — any long-form MD/Word/text) into a consulting-quality HTML report: pyramid structure, numbers promoted to KPIs/charts, comparisons turned into framework tables, citations preserved. Use when the user provides research/investigation results and says レポートにして/HTMLにまとめて/資料にして/きれいにして, mentions ディープリサーチの結果/調査結果/リサーチ結果, or asks to research a topic AND deliver a report. NEVER invents numbers or facts not present in the source (盛らない原則).
How to use KubeSense MCP tools to query logs, traces, and metrics from Kubernetes clusters. Covers tool selection, the discovery-first workflow, and links to datasource-specific skills.
Huawei Cloud CCE Metric analysis skill using the Python dispatcher with hcloud-backed cloud service queries. Use this skill when the user wants to: (1) query Pod/Node/CoreDNS/nginx-ingress/autoscaler/control-plane CPU, memory, disk, QPS, latency, request, connection, certificate, scaling, or error-rate metrics, (2) get resource usage TopN rankings, (3) query ECS/ELB/EIP/NAT cloud resource metrics, (4) aggregate cluster monitoring data with anomaly detection, (5) detect threshold-based resource anomalies. Trigger: user mentions "metric analysis", "指标分析", "CCE metrics", "CCE 指标", "AOM metrics", "AOM 指标", "CoreDNS metrics", "CoreDNS 指标", "nginx ingress metrics", "nginx-ingress 指标", "autoscaler metrics", "autoscaler 指标", "HPA metrics", "HPA 指标", "apiserver metrics", "etcd metrics", "controller manager metrics", "scheduler metrics", "control plane metrics", "控制面指标", "certificate expiration", "证书过期", "resource metrics", "资源指标", "CPU usage", "CPU 使用率", "memory usage", "内存使用率", "performance monitoring", "性能监控", "TopN", "resource ranking", "资源排名"
Use to reverse-engineer why a piece of content went viral (or overperformed) — yours or someone else's — and extract the repeatable mechanism to apply to your own content. Run when the user says "why did this go viral," "break down this viral post/video," "reverse engineer," "what made this work," or wants to learn from viral content. Sources the observable signal first (intake, transcript, screenshots, top comments, visible stats — an agent usually can't watch a video from a link) and never fabricates what it can't see. Reads brand-profile and audience first, deconstructs the piece layer by layer, isolates the real driver, runs a replicability check, extracts the transferable principle, and applies it to the user's niche via the content skills. Mechanism, never a copy; flags non-replicable virality; visible signals only (no WoopSocial analytics). Single-POST teardown only: for the account-level competitive landscape use competitor-analysis; for riding a live trend use trend-jacking.
Enforce access via Policies and Gates; use authorize() and authorizeResource() to standardize controller protections
Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.
Reduce token waste by 40-60% through anti-sycophancy rules, tool-call budgets, one-pass coding, task profiles, and read-before-write enforcement. Inspired by drona23/claude-token-efficient.
Parse a local or remote FILE (PDF) into markdown or structured JSON with fastCRW. Use when the source is a file on disk — "parse this PDF", "extract text from this document", "read this report", "convert PDF to markdown". Routing rule: URL → use crw-scrape; file on disk → use crw-parse. Step 5 of the crw workflow ladder.