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Found 62 Skills
Drives a disciplined explore → plan → implement → verify loop for changing an AI agent's behavior with confidence — whether fixing a reported failure or introducing a new requirement, business rule, or policy. Grounds the diagnosis in MLflow traces, codifies the desired behavior as a regression test suite (`mlflow.genai.evaluate` assertions in `@mlflow.test` pytest tests), and iterates the agent — not the test — until green, resisting quick system-prompt patches when the real fix is upstream (missing tool, retrieval source, or capability). Use whenever the user wants to fix or change how an agent behaves — e.g. "fix this issue in my agent", "this answer is wrong", "the agent is hallucinating", "improve my agent based on this trace", "make the agent do X instead of Y", "I want the agent to lead with/prioritize/recommend X", "new business rule: the agent should X", "always/never do X", "change the agent's default behavior" — or shares a trace they want addressed.
Use when implementing any code in RLM Phase 3. Enforces strict RED-GREEN-REFACTOR discipline with The Iron Law - no production code without a failing test first.
Use when creating new skills, editing existing skills, or verifying skills work before deployment
Use this whenever you encounter any bugs, test failures, or abnormal behavior, and execute it before proposing a fix
Use when implementing any Swift or SwiftUI feature or bugfix, before writing implementation code
Execute and implement approved specification proposals in sequence using a test-and-validation-first approach. It is used for implementing changes, applying proposals, executing specification tasks, or building according to approved plans. Trigger words include "speckit-implement", "speckit-develop", "develop", "implement", "realize proposal", "apply change", "execute specification", "complete tasks in order", "build feature", "start implementation".
Refactor code with safety nets — tests green before and after, no behavior change
Guide for creating effective skills. This command should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Use when creating new skills, editing existing skills, or verifying skills work before deployment - applies TDD to process documentation by testing with subagents before writing, iterating until bulletproof against rationalization
Create Test Specifications (TSPEC) - Layer 10 artifact for unit, integration, smoke, and functional test cases
Use this for development.
TDD workflow for migrations - orchestrate agents, zero main context growth
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.