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Found 2,762 Skills
Validate PRD, UX, Architecture and Epics specs are complete. Use when the user says "check implementation readiness".
Validate multi-skill workflows defined in CLAUDE.md by checking skill existence, inter-skill data contracts (JSON schema compatibility), file naming conventions, and handoff integrity. Use when adding new workflows, modifying skill outputs, or verifying pipeline health before release.
Critically review strategy drafts from edge-strategy-designer for edge plausibility, overfitting risk, sample size adequacy, and execution realism. Use when strategy_drafts/*.yaml exists and needs quality gate before pipeline export. Outputs PASS/REVISE/REJECT verdicts with confidence scores.
Write the minimal production code needed to make all existing failing tests pass. No extra features, no test modifications, no refactoring. Use after tests are written and confirmed failing.
Review football data code and visualisations for correctness. Use after building a chart, data pipeline, or analysis. Dispatches specialised reviewers for data correctness, chart conventions, visual inspection, and interactive edge cases.
Validate built features through conversational UAT
Validate, lint, audit, or fix .gitlab-ci.yml pipelines, stages, and jobs.
Apply rigorous survey design principles including construct operationalization, Likert scale development, reliability and validity assessment, and common method variance control. Use this skill when the user designs questionnaires, develops measurement items, needs to evaluate Cronbach's alpha or AVE, or when they ask 'how do I operationalize this construct', 'is my scale reliable', or 'how do I control for CMV'.
Use when you need to take a `*.plan.md` file and turn it into OpenSpec change artifacts by validating OpenSpec installation, initializing or reusing an OpenSpec project, and creating or updating a change proposal/spec/tasks flow. Includes a concrete workflow based on `examples/requirements-examples/problem1/requirements/openspec`. Part of the skills-for-java project
Audit experiment integrity before claiming results. Uses cross-model review (GPT-5.4) to check for fake ground truth, score normalization fraud, phantom results, and insufficient scope. Use when user says "审计实验", "check experiment integrity", "audit results", "实验诚实度", or after experiments complete before writing claims.
Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.
Run all quality checks (tests, lint, typecheck), fix failures, update the changelog, commit, push, and create/update the pull request or merge request.