Skill UI

Establishes a complete local development loop for building and thoroughly testing Clay integrations. This setup allows developers to simulate the full data flow: pushing test data via webhooks, allowing Clay to perform cloud-based enrichment, and finally receiving the enriched results back to a local endpoint (via ngrok). Ideal for iterative development of data pipelines and webhooks.

This sophisticated skill automates the entire Pull Request (PR) quality assurance lifecycle. It systematically fetches and categorizes CI check failures and detailed review feedback using the LOGAF priority scale. The workflow guides users through root cause analysis, proposing minimal, targeted fixes, replying to reviewers, and committing updates iteratively until all checks pass, ensuring high code quality before merging.

Guides multi-agent workflows through an iterative context retrieval loop—dispatching broad queries, evaluating relevance, refining criteria, and looping up to three times so sub-agents gather exactly the files they need without overwhelming limits.

This skill simulates a complete, multi-agent software development lifecycle following the principles of Agile and Scrum. It guides the project through six distinct, iterative phases: Architectural Planning, Parallel Implementation, Comprehensive Testing (Unit/E2E), Review, and Finalization. The process uses a convergent diffusion model, refining specifications and resolving conflicts across multiple specialized agents until the project converges and is deemed complete.

This comprehensive guide assists developers in adopting Test-Driven Development (TDD) practices. It covers generating unit tests for various languages (Python, JS, Java), analyzing test coverage gaps across multiple frameworks (Jest, Pytest, JUnit), and guiding the iterative Red-Green-Refactor workflow. Use this guide when generating stubs, improving test coverage, or adhering to spec-first development methodologies.

Automates iterative use of the plugin-dev skill-reviewer agent to diagnose and fix Claude Code SKILL.md issues, driving repeated review/fix cycles until standards are met before emitting the completion marker.

This skill provides a systematic methodology for iteratively improving complex skill documentation. By utilizing a reviewer agent, it identifies and categorizes issues (Critical, Major, Minor) in a structured loop. The process ensures that the skill meets high standards of quality, reliability, and functional completeness before deployment, moving beyond simple manual edits.

This skill manages complex coding tasks from initial implementation through testing, review incorporation, deployment, and final runtime verification. It ensures the task meets explicit Definition of Done (DoD) criteria iteratively, minimizing the need for manual prompting across multiple development stages.

Autonomous experiment loop that optimizes any file by a measurable metric. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Ideal for optimizing code speed, reducing bundle size, improving test pass rates, enhancing prompts, or boosting content quality through iterative measured attempts.

This tool automates the entire process of compiling a LaTeX source file into a submission-ready PDF. It handles multi-pass compilation, diagnoses common LaTeX errors (e.g., missing packages, undefined references, bibtex syntax errors), and iteratively attempts fixes to ensure the final document is accurate and adheres to specified page limits.

The Santa Method is a multi-agent adversarial verification framework designed to improve output quality by requiring two independent reviewers (Agent B and Agent C) to pass the content before publication. It eliminates single-agent bias and systematically improves outputs through iterative refinement loops until convergence or manual escalation.

A comprehensive guide and skill set for optimizing Go (Golang) application performance. It establishes a core philosophy of profiling before optimizing, focusing on reducing allocations, improving CPU efficiency, and addressing structural anti-patterns. Provides systematic modes for deep review (hot path, architecture review) and follows an iterative methodology (Benchmark -> Diagnose -> Improve -> Compare) to ensure measurable and stable performance gains.