Review a GitHub pull request from its link, read the PR description, inspect the code locally only when useful, and judge whether the change is safe to run from a security and runtime-safety perspective. Use only after the user pastes a PR URL. Handle one PR at a time, start with a rundown and discussion, and keep all GitHub review and merge actions with the user.
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
KFL2 (Kubeshark Filter Language) reference. This skill MUST be loaded before writing, constructing, or suggesting any KFL filter expression. KFL is statically typed — incorrect field names or syntax will fail silently or error. Do not guess at KFL syntax without this skill loaded. Trigger on any mention of KFL, CEL filters, traffic filtering, display filters, query syntax, filter expressions, write a filter, construct a query, build a KFL, create a filter expression, "how do I filter", "show me only", "find traffic where", protocol-specific queries (HTTP status codes, DNS lookups, Redis commands, Kafka topics), Kubernetes-aware filtering (by namespace, pod, service, label, annotation), L4 connection/flow filters, time-based queries, or any request to slice/search/narrow network traffic in Kubeshark. Also trigger when other skills need to construct filters — KFL is the query language for all Kubeshark traffic analysis. --- # KFL2 — Kubeshark Filter Language You are a KFL2 expert. KFL2 is built on Google's CEL (Common Expression Language) and is the query language for all Kubeshark traffic analysis. It operates as a **display filter** — it doesn't affect what's captured, only what you see. Think of KFL the way you think of SQL for databases or Google search syntax for the web. Kubeshark captures and indexes all cluster traffic; KFL is how you search it. For the complete variable and field reference, see `references/kfl2-reference.md`. ## Core Syntax KFL expressions are boolean CEL expressions. An empty filter matches everything. ### Operators | Category | Operators | |----------|-----------| | Comparison | `==`, `!=`, `<`, `<=`, `>`, `>=` | | Logical | `&&`, `\|\|`, `!` | | Arithmetic | `+`, `-`, `*`, `/`, `%` | | Membership | `in` | | Ternary | `condition ? true_val : false_val` | ### String Functions ``` str.contains(substring) // Substring search str.startsWith(prefix) // Prefix match str.endsWith(suffix) // Suffix match str.matches(regex)
Distill a colleague into an AI Skill. Auto-collect Feishu/DingTalk data, generate Work Skill + Persona, with continuous evolution. | 把同事蒸馏成 AI Skill，自动采集飞书/钉钉数据，生成 Work + Persona，支持持续进化。
Create new agents for the code-forge application. Agents are stored as .md files in the <cwd>/.forge/agents directory with YAML frontmatter (id, title, description, reasoning, tools, user_prompt) and markdown body containing agent instructions. Use when users need to add new agents, modify existing agents, or understand the agent file structure.
AI-Trader - AI Trading Signal Platform. Publish trading signals, follow traders. Use when user mentions trading signals, copy trading, stock trading, or follow traders.
Verify compat PR claims by running lodash vs es-toolkit/compat at runtime
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
This skill should be used when the user asks to "test the harness", "run integration tests", "validate features with real API", "test with real model calls", "run agent loop tests", "verify end-to-end", or needs to verify OpenHarness features on a real codebase with actual LLM calls.
Use this skill when you need to call MoviePilot REST API endpoints directly. Covers all 237 API endpoints across 27 categories including media search, downloads, subscriptions, library management, site management, system administration, plugins, workflows, and more. Use this skill whenever the user asks to interact with MoviePilot via its HTTP API, or when the moviepilot-cli skill cannot cover a specific operation.
Streaming chat assistant with conversation memory
Official skill for the XcodeBuildMCP CLI. Use when doing iOS/macOS/watchOS/tvOS/visionOS work (build, test, run, debug, log, UI automation).
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:
