Advisory/consulting mode — analysis and recommendations only, no code changes. Uses business-analyst and devils-advocate subagents for multi-perspective evaluation.
Analyze session replay patterns across experiment variants to understand user behavior differences. Use when the user wants to see how users interact with different experiment variants, identify usability issues, compare behavior patterns between control and test groups, or get qualitative insights to complement quantitative experiment results.
Use when manually publishing SDK packages to npm registry, after all changes are merged to main
Establish structured business context and project principles before problem discovery. Use as Step 0 of Problem-Based SRS to capture project identity, business principles, stakeholders, domain boundaries, and success criteria that feed into Customer Problems identification.
Generate a drt sync YAML configuration file. Use this skill whenever a user wants to create a new drt sync, connect a data warehouse table to an external service, or set up a Reverse ETL pipeline with drt. --- Create a drt sync YAML configuration file for the user. ## Steps 1. Ask the user for the following (or infer from context if already provided): - **Source table or SQL**: what data to sync (e.g. `ref('new_users')` or a SQL query) - **Destination**: where to send it (Slack, Discord, Microsoft Teams, REST API, HubSpot, GitHub Actions, Google Sheets, PostgreSQL, MySQL, ClickHouse, Parquet, CSV/JSON/JSONL, Jira, Linear, SendGrid, Staged Upload (async bulk APIs), or other) - **Sync mode**: full (every run), incremental (watermark-based, needs cursor column), upsert (dedup by key), or replace (TRUNCATE + INSERT for full table refresh) - **Frequency intent**: helps set `batch_size` and `rate_limit` 2. Generate a valid sync YAML using the exact field names from `docs/llm/API_REFERENCE.md`. 3. Output the YAML in a code block and suggest where to save it: `syncs/<name>.yml` 4. Show the command to validate and run it: ```bash drt validate drt run --select <name> --dry-run drt run --select <name> ``` ## Rules - Use `type: bearer` + `token_env` (never hardcode tokens) - Default `on_error: skip` for Slack/webhooks, `on_error: fail` for critical syncs - For incremental mode, always include `cursor_field` - Use `ref('table_name')` when the source is a single DWH table; raw SQL when filtering or joining - Jinja2 templates use `{{ row.<column_name> }}` — column names must come from the user ## Reference See `docs/llm/API_REFERENCE.md` for all fields, types, and defaults.
AnyCap CLI -- create media humans can see and hear (generate/edit images, produce video, compose music), understand media humans share (analyze images, video, audio), access the web (search, crawl), and deliver results humans can use (Drive for shareable file links, Page for hosted web pages). Use whenever a task involves creating visual or audio content, analyzing media, searching or reading the web, sharing files with humans, or publishing anything as a web page -- even if the user doesn't mention AnyCap by name. Also use for AnyCap authentication (login, API key, credentials), configuration, and feedback. Trigger on: image/video/music generation, media analysis, web search, web crawl, file sharing, page hosting, drive storage, delivering results to users, or any mention of AnyCap.
Manage Architecture Decision Records using the project's ADR CLI tool. Use when the user wants to create, list, view, lint, or index ADRs, or when working with docs/architecture/ files. Triggers on "create an ADR", "new ADR", "list ADRs", "lint ADRs", "what ADRs exist", "ADR domains".
Verifiable DID identity and end-to-end encrypted inbox for AI Agents. Built on ANP (Agent Network Protocol) and did:wba. Provides self-sovereign identity, Handle (short name) registration, content pages publishing, federated messaging, group communication, and HPKE-based E2EE — Web-based, not blockchain. Designed natively for autonomous Agents.
MetaMe npm 发布流程（版本 bump、pre-publish 审计、OTP publish）。触发：发布/打包/出包/bump/release + MetaMe 代码上下文。勿触发：自媒体/内容发布。
An example skill that does things
Activate when user wants to find actual companies in a value chain layer, especially Indian proxies for global themes. Always check connected sources first. Always apply SEBI compliance — research framing only. --- ## Design system — mandatory on all HTML artifacts Every HTML artifact generated by this skill MUST follow this design system. No exceptions. Never invent colours, never use dark backgrounds, never use purple or blue as primary backgrounds. **Palette:** - Page background: `#fafaf8` (warm white) - Card background: `white` - Primary text: `#2c2c2c` (soft charcoal) - Secondary text: `#6b6b6b` - Muted text: `#9a9690` - Accent / headers: `#1b5e52` (deep teal) — the ONLY accent colour - Border: `#e0ddd6` - Hover tint: `#f0f7f4` - Warning/amber: `#fff8f0` background, `#f0d090` border, `#5a3a00` text **Typography:** - Body: `'Georgia', serif`, 14px minimum - Monospace labels: `'Courier New', monospace` - Never below 11px anywhere **Component rules:** - All section headers: `background: #1b5e52; color: white` - Cards: `background: white; border: 1px solid #e0ddd6; border-radius: 8px` - Buttons: `background: #1b5e52; color: white` for primary actions - No gradients, no shadows, no dark backgrounds on outer containers **Pill colour system:** - Global company: `background:#e6f1fb; color:#0c447c; border:0.5px solid #85b7eb` - India listed: `background:#eaf3de; color:#27500a; border:0.5px solid #97c459` - India private: `background:#faeeda; color:#633806; border:0.5px solid #ef9f27` - Flagged: `background:#fcebeb; color:#791f1f; border:0.5px solid #f09595` - Neutral segment: `background:#f3f4f6; color:#374151; border:0.5px solid #d1d5db` - Seeded: `background:#eaf3de; color:#27500a` - Discovered: `background:#e6f1fb; color:#0c447c` **Attribution footer — mandatory on every artifact:** ```html <div style="margin-top:32px;padding-top:12px;border-top:1px solid #e0ddd6;
基于 reddit 的搜索能力，给出研究报告
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:
