Methodical research assistant for exhaustive investigations through systematic research cycles. Best for literature reviews, competitive analysis, trend reports, and comprehensive topic exploration.
Clean up Kurtosis enclaves and artifacts. Remove stopped enclaves, running enclaves with -a flag, and stopped engine containers. Use when you need to free up resources or start fresh.
Autonomous Hyperliquid trading — 14 strategies (MM, momentum, arbitrage, LLM) with APEX multi-slot orchestrator, REFLECT performance review, DSL trailing stops, and builder fee revenue collection.
Automatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style evaluation. --- # Auto Arena Skill End-to-end automated model comparison using the OpenJudge `AutoArenaPipeline`: 1. **Generate queries** — LLM creates diverse test queries from task description 2. **Collect responses** — query all target endpoints concurrently 3. **Generate rubrics** — LLM produces evaluation criteria from task + sample queries 4. **Pairwise evaluation** — judge model compares every model pair (with position-bias swap) 5. **Analyze & rank** — compute win rates, win matrix, and rankings 6. **Report & charts** — Markdown report + win-rate bar chart + optional matrix heatmap ## Prerequisites ```bash # Install OpenJudge pip install py-openjudge # Extra dependency for auto_arena (chart generation) pip install matplotlib ``` ## Gather from user before running | Info | Required? | Notes | |------|-----------|-------| | Task description | Yes | What the models/agents should do (set in config YAML) | | Target endpoints | Yes | At least 2 OpenAI-compatible endpoints to compare | | Judge endpoint | Yes | Strong model for pairwise evaluation (e.g. `gpt-4`, `qwen-max`) | | API keys | Yes | Env vars: `OPENAI_API_KEY`, `DASHSCOPE_API_KEY`, etc. | | Number of queries | No | Default: `20` | | Seed queries | No | Example queries to guide generation style | | System prompts | No | Per-endpoint system prompts | | Output directory | No | Default: `./evaluation_results` | | Report language | No | `"zh"` (default) or `"en"` | ## Quick start ### CLI `
MUST USE when reviewing ClickHouse schemas, queries, or configurations. Contains 28 rules that MUST be checked before providing recommendations. Always read relevant rule files and cite specific rules in responses.
Use when creating cloned voices with Alibaba Cloud Model Studio CosyVoice customization models, especially cosyvoice-v3.5-plus or cosyvoice-v3.5-flash, from reference audio and then reusing the returned voice_id in later TTS calls.
Audit and score blog posts on a 5-category 100-point scoring system covering content quality, SEO optimization, E-E-A-T signals, technical elements, and AI citation readiness. Includes AI content detection (burstiness, phrase flagging, vocabulary diversity). Supports export formats (markdown, JSON, table) and batch analysis with sorting. Generates prioritized recommendations (Critical/High/Medium/Low) with specific fixes. Works with any format (MDX, markdown, HTML, URL). Use when user says "analyze blog", "audit blog", "blog score", "check blog quality", "blog review", "rate this blog", "blog health check". --- # Blog Analyzer -- Quality Audit & Scoring Scores blog posts on a 0-100 scale across 5 categories and provides prioritized improvement recommendations. Includes AI content detection analysis. Works with local files or published URLs.
Analyzes and fixes A2A Transport Compatibility Kit (TCK) issues by understanding the specification, reproducing the failure, implementing the fix, and validating it works.
Explains ev-node architecture, components, and internal workings. Use when the user asks how ev-node works, wants to understand the block package, DA layer, sequencing, namespaces, or needs architecture explanations. Covers block production, syncing, DA submission, forced inclusion, single vs based sequencer, and censorship resistance.
High-performance data analysis using Polars - load, transform, aggregate, visualize and export tabular data. Use for CSV/JSON/Parquet processing, statistical analysis, time series, and creating charts.
When the user wants to analyze Google Search Console data, use the GSC API, or interpret search performance. Also use when the user mentions "GSC," "Search Console," "indexing report," "Core Web Vitals," "Enhancements," "Insights report," "search performance," "search queries," "search performance report," "URL inspection," "impressions," "CTR," "average position," "index coverage," "GSC data analysis," "Search Console API," or "searchanalytics.query." When the user wants to rewrite title tags (not only report on them), use title-tag. For meta description rewrites, use meta-description.
通用数字永生框架：从聊天记录、社交媒体、文档等多平台数据中蒸馏任何人的数字分身——支持自己、同事、导师、亲人、伴侣/前任、朋友、公众人物 7 种角色模板，接入国内外 12+ 数据平台。
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:
