GitHub
template-engine
Load, validate, and populate templates consistently across frameworks. Use when relevant to the task.
☆ 79View skill
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GitHub
skill-builder
Build Claude skills from extracted documentation. Use after doc-scraper/pdf-extractor to generate uploadable skill packages. Use when relevant to the task.
☆ 79View skill
GitHub
skill-builder
Build Claude skills from extracted documentation. Use after doc-scraper/pdf-extractor to generate uploadable skill packages.
☆ 79View skill
GitHub
review-synthesis
Aggregate multi-reviewer feedback into consolidated, actionable recommendations. Use when relevant to the task.
☆ 79View skill
GitHub
flaky-detect
Identify flaky tests from CI history and test execution patterns. Use when debugging intermittent test failures, auditing test reliability, or improving CI stability.
☆ 79View skill
GitHub
flaky-fix
Suggest and apply fixes for flaky tests based on detected patterns. Use after flaky-detect identifies unreliable tests that need repair. Use when relevant to the task.
☆ 79View skill
GitHub
voice-apply
Applies a voice profile to transform content. Use when user asks to write in a specific voice, match a tone, apply a style, or transform content to sound like a particular voice profile.
☆ 79View skill
GitHub
quality-checker
Validate skill quality, completeness, and adherence to standards. Use before packaging to ensure skill meets quality requirements. Use when relevant to the task.
☆ 79View skill
GitHub
venv-manager
Create, manage, and validate Python virtual environments. Use for project isolation and dependency management.
☆ 79View skill
GitHub
llms-txt-support
Detect and use llms.txt files for LLM-optimized documentation. Use when checking if a site has LLM-ready docs before scraping.
☆ 79View skill
GitHub
skill-enhancer
AI-powered enhancement of skill SKILL.md files. Use to transform basic templates into comprehensive, high-quality skill documentation. Use when relevant to the task.
☆ 79View skill
GitHub
architecture-evolution
Manage architecture changes with impact analysis, ADR generation, and migration planning. Use when relevant to the task.
☆ 79View skill
GitHub
performance-digest
Generate executive-ready performance summaries with insights and recommendations. Use when relevant to the task.
☆ 79View skill
GitHub
generate-factory
Auto-generate test data factories from schemas, types, or models. Use when creating test data infrastructure, setting up fixtures, or reducing test setup boilerplate.
☆ 79View skill
GitHub
mutation-test
Run mutation testing to validate test quality beyond code coverage. Use when assessing test effectiveness, finding weak tests, or validating test suite quality.
☆ 79View skill
GitHub
pytest-runner
Execute Python tests with pytest, supporting fixtures, markers, coverage, and parallel execution. Use for Python test automation.
☆ 79View skill
GitHub
workspace-health-check-skill
Assesses workspace alignment and suggests cleanup or realignment actions at key transition points. Use when relevant to the task.
☆ 79View skill
GitHub
voice-blend
Combine multiple voice profiles with weighted mixing to create hybrid voices. Use when relevant to the task.
☆ 79View skill
GitHub
repo-analyzer
Analyze GitHub repositories for structure, documentation, dependencies, and contribution patterns. Use for codebase understanding and health assessment.
☆ 79View skill
GitHub
incident-triage
Rapid incident classification, severity assessment, and response coordination. Use when relevant to the task.
☆ 79View skill
GitHub
competitive-intel
Gather and analyze competitive intelligence from multiple sources. Use when relevant to the task.
☆ 79View skill
GitHub
source-unifier
Merge multiple documentation sources (docs, GitHub, PDF) with conflict detection. Use when combining docs + code for complete skill coverage.
☆ 79View skill
GitHub
ralph-loop
Detect requests for iterative AI task loops and invoke the Ralph command
☆ 79View skill
GitHub
traceability-check
Verify bidirectional traceability from requirements to code to tests. Use when relevant to the task.
☆ 79View skill
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.