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Found 2,042 Skills
Guidance for working with the Beltic KYA (Know Your Agent) ecosystem - a credential-based trust framework for AI agents. Use when: (1) Working in any Beltic repository (beltic-spec, beltic-cli, beltic-sdk, fact-python, kya-platform, wizard, nasa), (2) Implementing agent credential signing/verification, (3) Using @belticlabs/kya SDK or beltic-sdk Python, (4) Understanding agent safety certification, (5) Working with verifiable credentials for AI. Triggers on: Beltic CLI commands, agent credentials, HTTP message signatures (RFC 9421), safety scores, KYB tier verification, trust chain validation.
Analyze codebases for anti-patterns, code smells, and quality issues using ast-grep structural pattern matching. Use when reviewing code quality, identifying technical debt, or performing comprehensive code analysis across JavaScript, TypeScript, Python, Vue, React, or other supported languages.
Enforces using shell script one-liners as the primary approach for scripts. Prohibits Node.js and Python usage. Use TypeScript with Deno only when variables or complex branching are necessary. MUST ALWAYS be applied when creating scripts, automation tasks, or executing commands.
This skill should be used when building data processing pipelines with CocoIndex v1, a Python library for incremental data transformation. Use when the task involves processing files/data into databases, creating vector embeddings, building knowledge graphs, ETL workflows, or any data pipeline requiring automatic change detection and incremental updates. CocoIndex v1 is Python-native (supports any Python types), has no DSL, and is currently under pre-release (version 1.0.0a1 or later).
Set up uv (Rust-based Python package manager) in CI/CD pipelines. Use when configuring GitHub Actions workflows, GitLab CI/CD, Docker builds, or matrix testing across Python versions. Includes patterns for cache optimization, frozen lockfiles, multi-stage builds, and PyPI publishing with trusted publishing. Covers GitHub Actions setup-uv action, Docker multi-stage production/development builds, and deployment patterns.
Technology-agnostic prompt generator that creates customizable AI prompts for scanning codebases and identifying high-quality code exemplars. Supports multiple programming languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable analysis depth, categorization methods, and documentation formats to establish coding standards and maintain consistency across development teams.
Create comprehensive unit tests, integration tests, and end-to-end tests using pytest for Python projects. Specializes in FastAPI testing with TestClient, async testing with pytest-asyncio, SQLModel/SQLAlchemy database testing, fixture generation, and test configuration setup. Use when you need test coverage, want to implement TDD/BDD, create test suites for functions or API endpoints, add edge case testing, or improve code quality with automated testing. Triggers include requests like "write tests for this module", "create pytest fixtures", "test this FastAPI endpoint", "setup pytest configuration", or "generate test file".
Enforce Vertical Slice Architecture (VSA) when building applications in any language (Go, .NET/C#, Java, Kotlin, TypeScript, Python, etc.) and any type (web API, mobile backend, CLI, event-driven). Organize code by feature/use-case instead of technical layers. Each feature is a self-contained vertical slice with a single entry point that receives the router/framework handle and its dependencies. Use when the user says "vertical slice architecture", "VSA", "organizar por feature", "feature-based architecture", "slice architecture", or when building a new app or feature and the project already follows VSA conventions. Also use when reviewing or refactoring code to align with VSA principles.
Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.
FOX v0.1 — Fully autonomous multi-strategy trading for Hyperliquid perps via Senpi MCP. Forked from Wolf v7 + v7.1 data-driven optimizations (14-trade analysis: 2W/12L). Tighter absolute floor (0.02/lev, ~20% max ROE loss), aggressive Phase 1 timing (30min hard timeout, 15min weak peak, 10min dead weight), green-in-10 floor tightening, time-of-day scoring (+1 for 04-14 UTC, -2 for 18-02 UTC), rank jump minimum (≥15 OR vel>15). Scoring system (6+ pts), NEUTRAL regime support, tiered margin (6 entries max), BTC 1h bias alignment, market regime refresh 4h. 8-cron architecture. Independent from Wolf. Requires Senpi MCP, python3, mcporter CLI, OpenClaw cron system.
This skill should be used when the user asks to "integrate GitHub Copilot into an app", "use the Copilot SDK", "build a Copilot-powered agent", "embed Copilot in a service", or needs guidance on the GitHub Copilot SDK for Python, TypeScript, Go, or .NET.
Use this skill for mathematical code verification. Use when reviewing math-heavy code, verifying algorithm correctness, checking numerical stability, aligning with mathematical standards. Do not use when general algorithm review - use architecture-review. DO NOT use when: performance optimization - use parseltongue:python-performance.