Search Skills

Ensures code quality via automated linting and formatting workflows, preventing unverified 'production ready' claims with rule-based resolution.

Optimizes system prompts and CLAUDE.md configurations for Claude Code CLI, transforming negative instructions into positive patterns per Anthropic's best practices.

Provides local server for GGUF-based LLM inference with OpenAI-compatible API, enabling offline AI tool development and troubleshooting.

Generates Claude Code agents using Anthropic best practices, supporting new agent creation, configuration modification, and specialized subagent design.

Reference for Claude Code plugin development: plugin.json schema, marketplace configuration, and validation.

Configures and troubleshoots clang-format settings for consistent code styling, addressing brace styles, indentation, and formatting conventions.

Automates creation and validation of Python package READMEs for PyPI, resolving rendering issues and format configuration.

Verifies hypothesis-action alignment before execution to prevent misaligned implementations in automated workflows.

Applies hypothesis-driven scientific reasoning to systematically investigate complex technical problems, root causes, and architectural challenges when initial solutions fail.

Configures and optimizes GitLab CI/CD pipelines, including .gitlab-ci.yml, Docker-in-Docker workflows, and local testing.

Reference guide for Claude Code's slash command syntax, frontmatter, and plugin namespacing, aiding developers in creating custom commands.

Comprehensive documentation for Hatchling, a modern Python build backend implementing PEP 517/518/621/660 standards, covering configuration, packaging, and build processes.

Enforces role boundaries, scope discipline, and DONE/BLOCKED status signaling for specialist subagents in supervisor-delegated agent systems.

Comprehensive reference for Claude Code hooks system, covering events, matchers, exit codes, JSON output control, and plugin hook implementation for developers.

Provides a structured self-assessment checklist to verify task completion before finalizing, ensuring evidence-based claims and preventing premature closure.

Expert guidance for Astral's uv, a high-speed Python package manager for dependency management, virtual environments, and project setup.

Enables high-performance application development using Python asyncio, async/await patterns for non-blocking I/O operations in APIs and concurrent systems.

Detects hallucinations, timeline fabrications, and unverified assumptions in AI agent outputs to ensure reliability and trustworthiness.

A Python library for unified API calls to LLM providers, supporting OpenAI, Anthropic, local servers, and implementing retry/fallback logic.

Structures pattern and anti-pattern descriptions using narrative framework to accelerate LLM agent detection across diverse domains.

Provides a structured template for delegating tasks to sub-agents using the WHERE-WHAT-WHY framework, streamlining agent-based workflow automation.

Comprehensive guide for creating and managing MkDocs documentation projects with Material theme, including CLI reference and configuration settings.

Verifies prerequisite information completeness in specs, PRDs, and architecture designs before planning begins.

Provides standardized coding practices and UI guidelines for consistent Svelte-based dashboard development within the PolyFlup project.