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Found 139 Skills
A professional tool for generating customized checklists for current features based on user requirements. Specifically designed for requirement quality validation, it creates "English-style unit tests" to verify the completeness, clarity, and consistency of requirements. Trigger words: speckit-checklist, checklist, requirements validation, quality check, quality review, spec review
pytest, data validation, Great Expectations, and quality assurance for data systems
PR review with parallel specialized agents. Use when reviewing pull requests or code.
Systematic methodology for debugging bugs, test failures, and unexpected behavior. Use when encountering any technical issue before proposing fixes. Covers root cause investigation, pattern analysis, hypothesis testing, and fix implementation. Use ESPECIALLY when under time pressure, "just one quick fix" seems obvious, or you've already tried multiple fixes. NOT for exploratory code reading.
Automated code review with security, performance, and best practices analysis. Use when reviewing pull requests or analyzing code for vulnerabilities, performance issues, or maintainability concerns.
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.
Review only git diff for impact, regression, correctness, compatibility, and side effects. Scope-only atomic skill; output is a findings list for aggregation.
Master plugin testing, quality assurance, and validation. Learn unit testing, integration testing, and how to ensure plugin quality.
Coordinator workflow for orchestrating dockeragents through fix-review-iterate-present loop. Use when delegating any task that produces code changes. Ensures agents achieve 10/10 quality before presenting to human.
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".
Open source contribution best practices. Creating quality pull requests, writing good issues, following project conventions, and collaborating effectively with maintainers.