Base UI unstyled React components. Covers forms, menus, overlays, composition. Use when building accessible, unstyled React UI components with @base-ui/react, composing with render props, or implementing custom-styled form controls, menus, and overlays. Keywords: @base-ui/react, render props, unstyled components.
Develop, review, or refactor Go services built with Anclax, including OpenAPI specs, handlers, service/business logic, database/sqlc changes, async tasks, and Wire dependency injection.
Configure Deep Agent backends (StateBackend, FilesystemBackend, StoreBackend, LocalShellBackend, CompositeBackend, Sandboxes). Use when choosing storage, configuring file access, or setting up persistent state.
Back up all agent data to GitHub — SQLite databases, Claude Code memory, identity, skills, brain notes. Use when the user says backup, back up, save everything, push to github, or snapshot. Also used by heartbeat for automated backups.
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, or extract information from web pages.
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, REST API, HubSpot, GitHub Actions, Google Sheets, PostgreSQL, MySQL, or other) - **Sync mode**: full (every run) or incremental (watermark-based, needs a cursor column) - **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.
Expert diagnosis for Tokio/async Rust issues including deadlocks, task leaks, performance bottlenecks, and cancellation safety
Execute SQL, SPARQL, SPASQL, SPARQL-FED, and GraphQL queries against live data spaces and knowledge graphs via OpenLink's OpenAPI-compliant web services. Use this skill whenever the user wants to query a database, RDF store, or SPARQL endpoint; explore a knowledge graph or data space; asks "How to ...", "Define the term ...", or poses a question against a known article or graph context; or mentions linkeddata.uriburner.com, Virtuoso, OPAL, or OpenLink services. Full query templates are in references/query-templates.md — load that file before constructing any predefined query.
View and change hmem memory settings, hooks, sync, and checkpoint configuration. Use this skill whenever the user types /hmem-config, asks to change memory settings, adjust parameters, tune bulk-read behavior, configure auto-checkpoints, manage hmem-sync, or troubleshoot memory-related issues. Also trigger when the user asks things like "how often does auto-save fire", "why is my context so large", "change checkpoint to auto", "how many tokens does startup cost", or "set up sync". --- # hmem-config — View and Change Settings This skill guides you through reading, explaining, and updating hmem's configuration. The config controls how memory is stored, displayed, checkpointed, and synced across devices. ## Locate and read the config The config lives at `hmem.config.json` in the same directory as your .hmem file. Located at `~/.hmem/hmem.config.json` (in the same directory as your .hmem file). Read the file directly — don't ask the user where it is. If it doesn't exist, offer to create one (only non-default values need to be specified). The config uses a unified format with a `"memory"` block and an optional `"sync"` block: ```json { "memory": { ... }, "sync": { ... } } ``` ## Show current settings Present a table of current values vs. defaults. Only highlight values that differ from defaults — the user cares about what they've customized, not the full list. ### Core parameters | Parameter | Default | Purpose | |-----------|---------|---------| | `maxCharsPerLevel` | [200, 2500, 10000, 25000, 50000] | Character limits per tree level [L1–L5]. L1 is always loaded at startup, so keeping it short saves tokens across every session. L5 is raw data, rarely accessed. | | `maxDepth` | 5 | Tree depth (1–5). Most users need 5. Lower values save storage but lose granularity. | | `defaultReadLimit` | 100 | Max entries per bulk read. Lower = faster startup, higher = more complete overview. | | `maxTitleChars` | 50 | Auto-extracted title length. Only applies to entries without explic
Auto-update CHANGELOG.md following Keep a Changelog format. Triggers: CL, changelog, 變更, 版本, version, 更新日誌, whatsnew.
Guide for querying and operating on data through the Agentic Data Protocol (ADP) MCP tools. This skill teaches how to use adp_discover, adp_describe, adp_validate, and adp_execute tools effectively. Use this skill whenever the user wants to explore available data resources, query data, look up records, insert new data, or update existing data through ADP — even if they don't mention "ADP" explicitly. Triggers include "find data", "query records", "look up customer", "insert rows", "update entries", "what data is available", "search for similar items", "show me the schema", or any data exploration and manipulation task where ADP MCP tools are connected. --- # ADP Skill — Data Access via Agentic Data Protocol This skill helps you interact with data through four ADP MCP tools that connect to an ADP Hypervisor. The Hypervisor is a policy-enforcing gateway that abstracts away backend differences (SQL databases, MongoDB, vector stores, file systems) behind a single intent-based interface. ## Core Workflow Always follow this sequence — each step builds on the previous one: ``` discover → describe → validate (optional) → execute ``` 1. **Discover** — Find what data resources are available 2. **Describe** — Read the usage contract for a specific resource (field schema, allowed operators, required predicates) 3. **Validate** — Dry-run your intent to catch errors before execution (recommended but optional) 4. **Execute** — Run the intent and get results Skipping `describe` leads to malformed intents because you won't know which fields exist, which predicates are required, or which operators are allowed. Always describe before building an intent. ## The Four MCP Tools ### 1. `adp_discover` — Browse Available Resources Find what data is available. Use filters to narrow results. **Parameters:** - `domain_prefix` (optional): Filter by domain, e.g. `"com.acme"` - `intent_class` (optional): `"LOOKUP"`, `"QUERY"`, `"INGEST"`, or `"REVISE"` - `keyword` (optional): Free-text search acr
Formats Claude's responses for consistent presentation. Use this skill whenever
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:
