This skill should be used when the user asks about agent architecture, evaluation, metrics, production monitoring, debugging agents, or best practices for building reliable AI agents. Use for questions like "evaluate my agent", "set up production monitoring", "add guardrails", "detect hallucinations", "agent anti-patterns", "compare experiments", "create evaluation dataset".
Orze is a filesystem-coordinated GPU experiment orchestrator. It runs the loop: **generate ideas → train → evaluate → learn → repeat**.
Verify the integrity of a hybrid swarm run by analysing its trace data.
AdVooster_Electron 프로젝트(/Users/tk/AdVooster_Electron)의 Python 코드를 분석하여 viruagent-cli에 포팅할 비즈니스 로직, API 엔드포인트, 인증 흐름, 데이터 구조를 추출한다. 'AdVooster 분석', '카페 API 분석', '카페 가입 분석', 'AdVooster에서 가져와', 'advooster', '기존 코드 분석' 등을 언급하면 이 스킬을 사용할 것.
Vertical App Studio micro-demo with optional dataset creation (domo-data-generator), ETL (magic-etl-cli or magic-etl), then multi-page layout using advanced-app-studio + domo-app-theme. Picks industry packs from references/*.md. Use when building an App Studio walkthrough that needs realistic data pipelines and themed pages per vertical.
Own and manage all bug data — BUGS.md index, bugs/ individual files, bug fixes, quality metrics. Single source of truth for everything bug and quality related.
Add Gmail integration to OmniClaw. Can be configured as a tool (agent reads/sends emails when triggered from WhatsApp) or as a full channel (emails can trigger the agent, schedule tasks, and receive replies). Guides through GCP OAuth setup and implements the integration.
Guide agents through backtesting strategy ideas with automatic data fetching and performance analysis
Deploy the OpenTelemetry observability stack (Prometheus, Grafana, OTEL Collector) to a Kind cluster for testing registry server telemetry. Use when you need to set up monitoring, metrics collection, or observability infrastructure.
Create and configure A2A Agent Cards — the discovery document describing an agent's capabilities, skills, authentication, and endpoint. Use when defining what your agent exposes to other agents.
Guide PDF or web form filling; use when structured form completion is requested.
Harness for autonomous software development. Enforces lifecycle through alignment gates (PROJECT.md), adversarial generator/evaluator agents, CI/CD pipeline automation, and strict Git workflows (Conventional Commits, Branching).
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.
Quick Start:
Import/download skills (.zip/.skill), then place locally:
~/.claude/skills/ (Claude Code)
~/.codex/skills/ (Codex CLI)
One SKILL.md can be reused across tools.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.
This keeps agents lightweight while preserving enough context for complex tasks.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused across tools.
Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.
Example: retrieval + writing + automation scripts as one workflow.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills:
