Browse published AI skills maintained by krzemienski. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
56Repository stars across indexed skills
64Repository forks
8Average repository stars per indexed skill
1Repository stars may repeat across skills from the same repository.
Use when creating new skills, editing existing skills, or verifying skills work before deployment - applies TDD to process documentation with Shannon quantitative validation by testing with subagents before wri...
Verify test quality by injecting mutations into code and measuring catch rate. Calculate mutation score (0.00-1.00) showing test effectiveness. Auto-generate missing tests to improve coverage. Integrate with Se...
Use before skill deployment to verify pressure resistance via TDD RED-GREEN-REFACTOR cycle with Serena metrics tracking - measures compliance score (0.00-1.00) across pressure scenarios
Use when partner provides a complete implementation plan to execute in controlled batches with review checkpoints - loads plan, reviews critically with Shannon quantitative analysis, executes tasks in complexit...
Autonomous task execution with library discovery, 3-tier validation, and atomic git commits. Integrates with Shannon CLI Python modules for platform-specific execution. Invokes /shannon:wave for code generation...
Coordinates Serena MCP knowledge graph operations for Shannon Framework. Enforces standardized entity naming (shannon/* namespace), relation creation patterns, search protocols, and observation management. Prev...
Use when needing to discover available skills across project/user directories - automatically scans for SKILL.md files, parses YAML frontmatter, extracts metadata (name, description, type, MCP requirements), an...
Track performance benchmarks and detect regressions exceeding 10% threshold. Analyze historical trends and alert on degradation. Calculate regression score (0.00-1.00) for performance health. Integrate with Ser...
Use when about to claim work is complete, fixed, passing, or successful, before committing or creating PRs - requires running verification commands and confirming output before making ANY success claims; eviden...
FLEXIBLE skill orchestrating comprehensive analysis workflows. Adapts to analysis type (codebase, architecture, technical debt, complexity) and automatically invokes appropriate sub-skills (spec-analysis, proje...
Use at development completion - guides branch integration with Shannon 3-tier validation (0.00-1.00 readiness score), MCP merge pattern analysis, and Serena risk assessment
Intelligent MCP server recommendation engine based on quantitative domain analysis. Maps project domains (Frontend %, Backend %, Database %, etc.) to appropriate MCP servers using tier-based priority system (Ma...
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack with quantitative tracking, adding instrumentation when n...
Restore previous session state from Serena MCP checkpoints. Retrieves checkpoint by ID or auto-selects most recent, deserializes context, restores command/skill/phase/wave state. Use when: resuming after contex...
8-dimensional quantitative complexity analysis with domain detection. Analyzes specifications across structural, cognitive, coordination, temporal, technical, scale, uncertainty, dependency dimensions producing...
Use when testing Claude Code plugins programmatically, building SDK applications, encountering plugin loading failures (plugins array empty), getting AttributeError on message.type, or needing verified Claude A...
5-check quantitative validation ensuring 90% confidence before implementation. Prevents wrong-direction work through systematic verification: duplicate check (25%), architecture compliance (25%), official docs...
Use automatically when prompts exceed 3000 characters, files exceed 500 lines, or large files are referenced - enforces complete line-by-line reading protocol with quantitative comprehension verification before...
Comprehensive post-build verification of Shannon Framework's application outputs using three-layer methodology: Flow Verification (execution trace analysis), Artifact Verification (physical output inspection),...
Enforce NO MOCKS testing philosophy with real systems. Iron Law: no mock objects, no unit tests, no stubs. Test with real browsers (Puppeteer MCP), real databases, real APIs. Enforced via post_tool_use.py hook....
Use at session start - establishes Shannon Framework workflows including mandatory 8D analysis before implementation, NO MOCKS testing enforcement, wave-based execution for complexity >=0.50, and automatic Sere...
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation with quantitative tracking, pattern analysis, hypothesis testing, imp...
Use when implementing any feature or bugfix, before writing implementation code - write the test first using REAL systems (NO MOCKS), watch it fail, write minimal code to pass; ensures tests actually verify beh...
True parallel sub-agent coordination with wave-based execution. Analyzes phase dependencies, groups independent phases into waves for parallel execution, allocates agents based on complexity score. Proven 3.5x...
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.