Bridge audit trails and memory frames for comprehensive session recording. Greek: ζ (zeta) — Decision Trail, η (eta) — Proof Store. Use when recording audit sessions, creating memory bundles, linking audit trails to memory, or finalizing session proofs with memory archives.
Fully automated faceless YouTube channel. Send a topic, get a published documentary. Pipeline: script → voice → images (ComfyUI) → video (ffmpeg) → YouTube at 9am. No face required. 100% offline except YouTube upload.
You are recording the user's feedback on Discover section items to tune future recommendations.
Analyze audio recording quality - echo detection, loudness, speech intelligibility, SNR, spectral analysis. Use when the user wants to check a recording's quality, detect echo or duplication in audio files, measure speech clarity, compare original vs processed audio, diagnose why a recording sounds bad, or analyze audio tracks from Blackbox or any call recording app. Triggers on audio quality, recording analysis, echo detection, check recording, sound quality, analyze audio, speech quality, PESQ, STOI, loudness, SNR, audio diagnostics, recording sounds bad, echo in recording, audio duplication.
Write code using Agora SDKs (agora.io) for real-time communication. Covers RTC (video/voice, live streaming, screen sharing), RTM/signaling, Conversational AI voice agents, Cloud Recording, Server Gateway, and token generation. Use for Agora, RTC, RTM, video calling, voice calling, screen sharing, recording, tokens, signaling, or ConvoAI requests across Web, React, Next.js, iOS, Android, Go, and Python. Triggers include agora-rtc-sdk-ng, agora-rtc-react, agora-rtm, agora-agent-server-sdk, AgoraVoiceAI, AgoraClient, useConversationalAI, useTranscript, useAgentState, Cloud Recording, Server Gateway, and Agora authentication.
Build a phase-aware capability-evolution runtime for OpenClaw and coding agents. Use when the agent should classify work into task_light, task_full, agenda_review, or promotion_review mode; retrieve only the most relevant prior records; write canonical evidence into `.evolution/records`; maintain a capability map and learning agenda; and promote only validated, transferable strategies.
How to set up comprehensive logging and artifact extraction for Playwright e2e test suites. Use this skill when creating new e2e test suites, setting up Playwright from scratch, implementing BaseContext logging infrastructure, configuring test failure diagnostics, or adding custom test context logging. Essential for debugging e2e test failures with console logs, network logs, page errors, and Playwright artifacts (screenshots, videos, traces).
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:
