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Found 182 Skills
Meta-skill for validating the integrity and quality of other skills. automatically checks for SKILL.md existence, script syntax errors (via Godot CLI), and metadata completeness. Use this skill to verify the entire skill library. Trigger keywords: validation, continuous_integration, quality_assurance, syntax_check, metadata_check.
Deep Python code review of changed files using git diff analysis. Focuses on production quality, security vulnerabilities, performance bottlenecks, architectural issues, and subtle bugs in code changes. Analyzes correctness, efficiency, scalability, and production readiness of modifications. Use for pull request reviews, commit reviews, security audits of changes, and pre-deployment validation. Supports Django, Flask, FastAPI, pandas, and ML frameworks.
Conducts comprehensive requirements review including completeness validation, clarity assessment, consistency checking, testability evaluation, and standards compliance. Produces detailed review reports with findings, gaps, conflicts, and improvement recommendations. Use when reviewing requirements documents (BRD, SRS, user stories), validating acceptance criteria, assessing requirements quality, identifying gaps and conflicts, or ensuring standards compliance (IEEE 830, INVEST criteria). Trigger when users mention "review requirements", "validate requirements", "check requirements quality", "find requirement issues", or "assess BRD/SRS quality".
Comprehensive multi-dimensional skill reviews across structure, content, quality, usability, and integration. Task-based operations with automated validation, manual assessment, scoring rubrics, and improvement recommendations. Use when reviewing skills, ensuring quality, validating production readiness, identifying improvements, or conducting quality assurance.
Orchestrates complete skill lifecycle from creation to optimization. Use for comprehensive skill development, reviewing skills, or managing skill quality.
James Bach's HTSM Product Factors (SFDIPOT) analysis for comprehensive test strategy generation. Use when analyzing requirements, epics, or user stories to generate prioritized test ideas across Structure, Function, Data, Interfaces, Platform, Operations, and Time dimensions.
Scans lyrics for phrases that may match existing songs using web search and LLM knowledge. Use before release to check for unintentional borrowing.
Run verification commands and confirm output before claiming success. Use when about to claim work is complete, fixed, or passing, before committing or creating PRs.
Validates code changes against DeepRead's mandatory patterns and standards defined in AGENTS.md. Use this after writing or modifying code to catch violations before committing.
Master plugin testing, quality assurance, and validation. Learn unit testing, integration testing, and how to ensure plugin quality.
Open source contribution best practices. Creating quality pull requests, writing good issues, following project conventions, and collaborating effectively with maintainers.
Multi-agent code review with specialized perspectives (security, performance, patterns, simplification, tests)