Creates Java + Spring Boot projects: Web applications, full-stack apps with Vue.js or Angular or React or vanilla JS, PostgreSQL, REST APIs, and Docker. Use when creating Spring Boot projects, setting up Java microservices, or building enterprise applications with the Spring Framework.
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
Use when querying, modifying, or investigating database state — enforces schema-first approach and mutation safety
Use this skill to extract and list tables from Excel files using the eparse CLI. Call when you need to discover or extract tabular data from one or more Excel files or directories. Supports output to console, SQLite, or PostgreSQL.
Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.
opskat CLI for asset management and remote operations (SSH, SQL, Redis, file transfer). Use when: managing server assets, executing remote commands, writing opsctl scripts/automation, or working with approval/grant/session workflows. Also triggers for: deploying to servers, server diagnostics/troubleshooting, batch operations across fleet, database queries, file transfers between servers, server inventory/discovery.
Query code graph database for function callers, callees, dependencies, and dead code in CubeOS and MeshSat repos.
Build, validate, and manage semantic models using Sidemantic. Use when asked to create a semantic layer, define metrics/dimensions, model a database schema, generate models from SQL queries, import from Cube/dbt/LookML, or set up analytics definitions. Prioritizes CLI-first workflows, with YAML and optional Python API usage for advanced automation.
Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions "create", "build", "add", "write", "new", or "implement" with model, table, or SQL (3) Modifying existing model logic, columns, joins, or transformations (4) Implementing a model from schema.yml specs or expected output requirements Discovers project conventions before writing. Runs dbt build (not just compile) to verify. --- # dbt Model Development **Read before you write. Build after you write. Verify your output.** ## Critical Rules 1. **ALWAYS run `dbt build`** after creating/modifying models - compile is NOT enough 2. **ALWAYS verify output** after build using `dbt show` - don't assume success 3. **If build fails 3+ times**, stop and reassess your entire approach ## Workflow ### 1. Understand the Task Requirements - What columns are needed? List them explicitly. - What is the grain of the table (one row per what)? - What calculations or aggregations are required? ### 2. Discover Project Conventions ```bash cat dbt_project.yml find models/ -name "*.sql" | head -20 ``` Read 2-3 existing models to learn naming, config, and SQL patterns. ### 3. Find Similar Models ```bash # Find models with similar purpose find models/ -name "*agg*.sql" -o -name "*fct_*.sql" | head -5 ```
Complete architecture guide for building features from database to UI. Routes to frontend/, fullstack/, mobile/, backend/, or sdks/ based on detected stack. Covers decomposition, multi-tenant isolation, auth model, query patterns, and component patterns.
Use when implementing the data layer in Android — Repository pattern, Room local database, offline-first synchronization, and coordinating local and remote sources.
This skill should be used when optimizing AMD GPU kernels on MI300 using the aiter project, including running op tests, benchmarking, iterating on kernel changes, and recording results in the kernel experiment database.
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:
