- 📄 SKILL.md
architecture-patterns
Internal skill. Use cc10x-router for all development tasks.
16 days ago · Uploaded
Detail →
Daily Featured Skills Count
04/16
04/17
04/18
04/19
04/20
04/21
04/22
♾️ Free & Open Source 🛡️ Secure & Worry-Free
Import Skills
- 📄 SKILL.md
maui-ai-debugging
End-to-end workflow for building, deploying, inspecting, and debugging .NET MAUI and MAUI Blazor Hybrid apps as an AI agent. Use when: (1) Building or running a MAUI app on iOS simulator, Android emulator, Mac Catalyst, macOS (AppKit), or Linux/GTK, (2) Inspecting or interacting with a running app's UI (visual tree, tapping, filling text, screenshots, property queries), (3) Debugging Blazor WebView content via CDP, (4) Managing simulators or emulators, (5) Setting up MauiDevFlow in a MAUI project, (6) Completing a build-deploy-inspect-fix feedback loop, (7) Handling permission dialogs and in-app/simulator alerts, (8) Managing multiple simultaneous apps via the broker daemon. Covers: the unified `maui devflow` CLI, androidsdk.tool, appledev.tools, adb, xcrun simctl, Linux `xdotool`-backed driver caveats, and dotnet build/run for all MAUI target platforms including macOS (AppKit) and Linux/GTK. Do not use for generic desktop automation, AppleScript macros, or arbitrary host-level `xdotool` control unrelated to MAUI app debugging. --- # MAUI AI Debugging Build, deploy, inspect, and debug .NET MAUI apps from the terminal. This skill enables a complete
5 days ago · Uploaded
Detail →
- 📁 .github/
- 📁 references/
- 📄 LICENSE
- 📄 README.md
- 📄 SKILL.md
temporal-developer
Develop, debug, and manage Temporal applications across Python, TypeScript, Go, Java and .NET. Use when the user is building workflows, activities, or workers with a Temporal SDK, debugging issues like non-determinism errors, stuck workflows, or activity retries, using Temporal CLI, Temporal Server, or Temporal Cloud, or working with durable execution concepts like signals, queries, heartbeats, versioning, continue-as-new, child workflows, or saga patterns.
20 days ago · Uploaded
Detail →
- 📁 references/
- 📄 SKILL.md
dagster-expert
Expert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, components, data tools or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific Dagster concept. --- ## Core Dagster Concepts Brief definitions only (see reference files for detailed examples): - **Asset**: Persistent object (table, file, model) produced by your pipeline - **Component**: Reusable building block that generates definitions (assets, schedules, sensors, jobs, etc.) relevant to a particular domain. ## Integration Workflow When integrating with ANY external tool or service, read the [Integration libraries index](./references/integrations/INDEX.md). This contains information about which integration libraries exist, and references on how to create new custom integrations for tools that do not have a published library. ## dg CLI The `dg` CLI is the recommended way to programmatically interact with Dagster (adding definitions, launching runs, exploring project structure, etc.). It is installed as part of the `dagster-dg-cli` package. If a relevant CLI command for a given task exists, always attempt to use it. ONLY explore the existing project structure if it is strictly necessary to accomplish the user's goal. In many cases, existing CLI tools will have sufficient understanding of the project structure, meaning listing and reading existing files is wasteful and unnecessary. Almost all `dg` commands that return information have a `--json` flag that can be used to get the information in a machine-readable format. This should be preferred over the default table output unless you are directly showing the information to the user. ## UV
21 days ago · Uploaded
Detail →
- 📁 .github/
- 📁 apis/
- 📁 examples/
- 📄 .gitignore
- 📄 AGENTS.md
- 📄 LICENSE
querying-indonesian-gov-data
Query 50 Indonesian government APIs and data sources — BPJPH halal certification, BPOM food safety, OJK financial legality, BPS statistics, BMKG weather/earthquakes, Bank Indonesia exchange rates, IDX stocks, CKAN open data portals, pasal.id (third-party law MCP). Use when building apps with Indonesian government data, scraping .go.id websites, checking halal certification, verifying company legality, looking up financial entity status, or connecting to Indonesian MCP servers. Includes ready-to-run Python patterns, CSRF handling, CKAN API usage, and IP blocking workarounds. --- # Querying Indonesian Government Data 🇮🇩 STARTER_CHARACTER = 🇮🇩 Route the user's intent to the right child reference, then follow its patterns. ## Router | User intent | Load reference | Quick pattern | |------------|---------------|---------------| | Halal certification, halal product check | [references/bpjph-halal.md](references/bpjph-halal.md) | `POST cmsbl.halal.go.id/api/search/data_penyelia` JSON, no auth | | Food/drug/cosmetic registration, BPOM | [references/bpom-products.md](references/bpom-products.md) | Session + CSRF → `POST cekbpom.pom.go.id/produk-dt` | | Is this fintech/investment legal, OJK | [references/ojk-legality.md](references/ojk-legality.md) | `GET sikapiuangmu.ojk.go.id/FrontEnd/AlertPortal/Search` | | Weather in Indonesia, earthquake, tsunami | [references/bmkg-weather.md](references/bmkg-weather.md) | `GET data.bmkg.go.id/DataMKG/TEWS/autogempa.json` | | GDP, inflation, population, trade stats | [references/bps-statistics.md](references/bps-statistics.md) | `GET webapi.bps.go.id/v1/api/...` (free API key) | | USD/IDR exchange rate, BI Rate | [references/bank-indonesia.md](references/bank-indonesia.md) | Scrape `bi.go.id/id/statistik/informasi-kurs/` | | Indonesian law, regulation, specific pasal | [references/pasal-id-law.md](references/pasal-id-law.md) | MCP (third-party): `claude mcp add --transport http pasal-id ...` | | Government datasets on any topic | [refere
23 days ago · Uploaded
Detail →
- 📄 SKILL.md
agent-setup
Initialize or update setup for pi-side-agents (running asynchronous agents spawned/controlled from main session)
21 days ago · Uploaded
Detail →
- 📁 assets/
- 📁 config/
- 📁 core/
- 📄 __init__.py
- 📄 _meta.json
- 📄 anima_doctor.py
anima-aios
An AI Agent cognitive growth system built on the native OpenClaw architecture. It provides agents with persistent memory management, visual intimacy progression, a 5-dimensional cognitive profile, gamified daily quests, team leaderboards, and a 5-layer memory architecture with Knowledge Palace, Pyramid thinking, and Ebbinghaus decay function. 基于 OpenClaw 原生架构的 AI Agent 认知成长体系，为 Agent 提供五层记忆架构、知识宫殿、金字塔知识组织、记忆衰减函数、LLM 智能处理、永久化记忆管理、可视化亲密度成长、五维认知画像、游戏化每日任务和团队排行榜。
22 days ago · Uploaded
Detail →
- 📄 SKILL.md
aggregates
Règle 05 : Aggregates et Aggregate Roots. Use when implementing DDD patterns.
7 days ago · Uploaded
Detail →
- 📄 __init__.py
- 📄 adrecs_skill.py
- 📄 example.py
ADReCS-query
Query the ADReCS (Adverse Drug Reaction Classification System) v3.3 database. Use whenever the user asks about adverse drug reactions, drug safety profiles, ADR classification, ADR severity/frequency, or wants to look up any entity (drug name, BADD Drug ID, DrugBank ID, ATC code, CAS RN, PubChem CID, KEGG ID, ADR term, ADReCS ID, MedDRA code, MeSH ID) in ADReCS. --- # ADReCS Query Skill Search ADReCS v3.3 records by any entity. Auto-detects type by prefix: | Input Pattern | Detected As | Example | |---|---|---| | `BADD_D00142` | BADD Drug ID | exact on drug_id column | | `DB00945` | DrugBank ID | resolved via Drug_information | | `A02BC01` | ATC code | resolved via Drug_information | | `50-78-2` | CAS RN | resolved via Drug_information | | `CID2244` or bare digits | PubChem CID | resolved via Drug_information | | `D00109` (5-digit) | KEGG ID | resolved via Drug_information | | `08.06.02.001` | ADReCS ID | substring on ADReCS_ID column | | `10003781` (8-digit) | MedDRA code | resolved via ADR_ontology | | `D######` (6+ digit) | MeSH ID | resolved via ADR_ontology | | anything else | free text | substring on drug_name OR ADR_term | ## API | Function | Input | Returns | |---|---|---| | `load_drug_adr(path)` | txt path | DataFrame (Drug–ADR pairs) | | `load_drug_info(path)` | xlsx path | DataFrame (drug metadata) | | `load_adr_ontology(path)` | xlsx path | DataFrame (ADR hierarchy) | | `search(entity)` | single entity string | DataFrame of matching Drug–ADR rows | | `search_batch(entities)` | list of entity strings | dict[str, DataFrame] | | `summarize(hits, entity)` | DataFrame + label | compact LLM-readable text | | `to_json(hits)` | DataFrame | list[dict] | ## Usage See `if __name__ == "__main__"` block in `62_ADReCS.py` for runnable examples covering: drug name lookup, BADD Drug ID, DrugBank ID, ADR term, ADReCS ID prefix, batch search, and JSON output. ## Data - **Source**: ADReCS v3.3 — [https://www.bio-add.org/ADReCS/](https://www.bio-add.org/ADReCS/) - **Primary
18 days ago · Uploaded
Detail →
- 📄 SKILL.md
compare-runs
Compare evaluated runs in a retort experiment along factor dimensions. Surfaces effects of each factor, aggregates across replicates, and highlights cells that diverge qualitatively — complementing (not replacing) retort's ANOVA analysis.
7 days ago · Uploaded
Detail →
- 📄 chain-of-verification-isolated.md
- 📄 chain-of-verification-process.md
- 📄 shared-capy-knowledge-protocol.md
chain-of-verification
Apply Chain-of-Verification (CoVe) prompting to improve response accuracy through self-verification. Use when complex questions require fact-checking, technical accuracy, or multi-step reasoning. --- # Chain-of-Verification (CoVe) CoVe is a verification technique that improves response accuracy by making the model fact-check its own answers. Instead of accepting an initial response at face value, CoVe instructs the model to generate verification questions, answer them independently, and revise the original answer based on findings. ## Conventions Read capy knowledge base conventions at [shared-capy-knowledge-protocol.md](shared-capy-knowledge-protocol.md). **Capy restriction:** CoVe is a read-only verification tool. Do NOT call `capy_index` or `capy_fetch_and_index` during this workflow. Use `capy_search` only. If corrections reveal knowledge worth persisting, the calling agent handles indexing after CoVe completes. ## When to Use This Skill
20 days ago · Uploaded
Detail →
- 📁 references/
- 📄 SKILL.md
stellar-ios-sdk
Build Stellar blockchain applications in Swift using stellar-ios-mac-sdk. Use when generating Swift code for transaction building, signing, Horizon API queries, Soroban RPC, smart contract deployment and invocation, XDR encoding/decoding, and SEP protocol integration. Covers 26+ operations, 50 Horizon endpoints, 12 RPC methods, and 17 SEP implementations with Swift async/await and callback-based streaming patterns. Full Swift 6 strict concurrency support (all types Sendable).
20 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.
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