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Import Skills

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vllm-project vllm-project
from GitHub Data & AI
  • 📄 SKILL.md
data performance token

vllm-bench-random-synthetic

Run vLLM performance benchmark using synthetic random data to measure throughput, TTFT (Time to First Token), TPOT (Time per Output Token), and other key performance metrics. Use when the user wants to quickly test vLLM serving performance without downloading external datasets.

⬇0 ❤52 1 month ago · Uploaded Detail →
hanlulong hanlulong
from GitHub Testing & Security
  • 📄 identification-strategies.md
  • 📄 latex-tips.md
  • 📄 review-checklist.md
paper economics security

econ-write

Expert economics paper writing assistant synthesizing advice from 50+ top guides by Cochrane, McCloskey, Shapiro, Head, Bellemare, Goldin, Glaeser, Kremer, and other leading economists. USE THIS SKILL whenever the user writes, edits, reviews, rewrites, or structures any economics paper, thesis, job market paper, abstract, introduction, conclusion, results section, literature review, or referee response. Also handles LaTeX formatting, presentations, and paper audits. Covers all paper types (applied, theory, structural, mixed) and all sections.

⬇0 ❤52 1 month ago · Uploaded Detail →
Lyt060814 Lyt060814
from GitHub Data & AI
  • 📁 agents/
  • 📁 assets/
  • 📁 eval-viewer/
  • 📄 LICENSE.txt
  • 📄 SKILL.md
performance existing optimize

skill-creator

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

⬇0 ❤52 1 month ago · Uploaded Detail →
heliohq heliohq
from GitHub Data & AI
  • 📄 SKILL.md
design data what

arch-design

System architecture and design thinking — requirements analysis, component design, data modeling, scaling strategy, and trade-off analysis. Use when: "design this system", "what's the architecture for", "trade-offs for X", "how should we architect", "system design for", "API design", "data model for", "service boundaries", "architecture doc", "create an ADR". When the design thinking is done, this skill hands off to /ship:write-docs to write the design document. Note: this is NOT for visual design (use /ship:visual-design) or implementation planning (use /ship:design). --- # Architectural Design Think through system design decisions rigorously before writing them down. This skill is about the **thinking** — requirements, components, trade-offs, boundaries. When the design is ready, you MUST invoke `Skill("write-docs")` to write the design document — do not write the doc inline. ## Scale to Complexity Not every decision needs all 5 phases. Match the depth to the decision: - **Small** (single component, clear constraints) — Phase 1 briefly, Phase 2, Phase 5. Skip deep dive and scaling. - **Medium** (multi-component, some unknowns) — All 5 phases, but keep each concise. - **Large** (new system, significant unknowns, cross-team) — All 5 phases in full depth, with diagrams and explicit load estimates. ## Red Flag **Never:** - Skip requirements gathering and jump straight to a solution - Design without understanding existing constraints (tech stack, team, timeline) - Omit trade-off analysis — every decision has alternatives that were rejected for a reason - Skip the Boundaries section — it's the core anti-drift mechanism - Propose a design without verifying assumptions against the actual codebase - Conflate "what we want" with "what exists" — be explicit about the gap ## Phase 1: Requirements Gathering Before designing anything, understand what you're solving. ### Functional Requirements - What must the system do? List concrete capabilities. - What are the input/output co

⬇0 ❤50 1 month ago · Uploaded Detail →
FairladyZ625 FairladyZ625
from GitHub Docs & Knowledge
  • 📁 examples/
  • 📄 README.md
  • 📄 SKILL.md
article notes knowledge

article-notes-integration

Nightly pipeline for integrating newly captured external article notes into Brain knowledge surfaces. Use when: 文章整合, article notes integration, nightly article sync, update article relations, topic index update, article knowledge graph, 前一天文章整理, 或 run the 02:00 article pipeline. --- # Article Notes Integration 把前一天新增或待整合的 Article Notes，转成可检索、可关联、可继续提炼的 Brain 知识输入层。 ## Purpose 这个技能负责 **文章 ingestion 之后的 nightly integration**，而不是原始外部文章采集本身。 它处理的是： 1. 扫描昨天新增或尚未 integrated 的 article notes 2. 校验并补足结构 / frontmatter / relation 状态 3. **交叉引用更新**（见下方 Cross-Reference Protocol，每次 ingest 后执行） 4. 更新 topic / domain / project 相关的轻量图谱入口 5. 生成 open questions / pattern candidates / article-derived graph signals 6. 输出高价值 article candidates，供后续 flywheel amplification 使用 ## Primary Inputs - Brain root: `{{BRAIN_ROOT}}` - Source notes: `03-KNOWLEDGE/02-WORKING/01-ARTICLE-NOTES/` - Candidate set: - 前一天新增 article notes - 或 `integration_status != integrated` 的 article notes - Read-only context: - related domain notes - `03-KNOWLEDGE/99-SYSTEM/01-INDEXES/` 下已有 topic / topic-map / open-question surfaces - `05-PROJECTS/` 下 project briefs（若能稳定识别项目） ## Required Outputs

⬇0 ❤49 26 days ago · Uploaded Detail →

Skill File Structure Sample (Reference)

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

SKILL.md Requirements

├─ ⭐ 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

Why SkillWink?

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.

Keyword Search Version Updates Multi-Metric Ranking Open Standard Discussion

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.

FAQ

Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.

1. What are Agent Skills?

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.

2. How do Skills work?

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.

3. How can I quickly find the right skill?

Use these three together:

  • Semantic search: describe your goal in natural language.
  • Multi-filtering: category/tag/author/language/license.
  • Sort by downloads/likes/comments/updated to find higher-quality skills.

4. Which import methods are supported?

  • Upload archive: .zip / .skill (recommended)
  • Upload skills folder
  • Import from GitHub repository

Note: file size for all methods should be within 10MB.

5. How to use in Claude / Codex?

Typical paths (may vary by local setup):

  • Claude Code：~/.claude/skills/
  • Codex CLI：~/.codex/skills/

One SKILL.md can usually be reused across tools.

6. Can one skill be shared 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.

7. Are these skills safe to use?

Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.

8. Why does it not work after import?

Most common reasons:

  • Wrong folder path or nested one level too deep
  • Invalid/incomplete SKILL.md fields or format
  • Dependencies missing (Python/Node/CLI)
  • Tool has not reloaded skills yet

9. Does SkillWink include duplicates/low-quality skills?

We try to avoid that. Use ranking + comments to surface better skills:

  • Duplicate skills: compare differences (speed/stability/focus)
  • Low quality skills: regularly cleaned up