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Found 78 Skills
Iterative worker-reviewer cycle that spawns a critic subagent to score work 1-10 and provide actionable feedback, then revises until a quality gate is met. Use when implementing features, writing specs, reviewing existing code, or completing any task where quality matters more than speed. Trigger phrases: "use review-loop", "polish this", "iterate on this", "/review-loop", "review with feedback loop".
Full review of a plan or spec to be sure there are no surprises, gaps, or mistakes. Use to harden a spec before implementation.
Validate whether an implementation matches its stated goal. Use this skill when a skill or agent wants a second opinion on its own output, when the user says "check this implementation", "validate what you did", "is this correct?", "review the output", or "did you do this right?". Also spawned automatically as a subagent by other skills (memory-bridge, daily-update) to self-check their outputs before presenting to the user. Returns a structured pass/warn/fail verdict with specific actionable issues.
Generates eval test cases from an eval suite plan (output of /eval-suite-planner) or a plain-English agent description. Supports both single-response and conversation (multi-turn) evaluation modes. Outputs a Copilot Studio test set table, a CSV file for import (single-response only), and a docx report for human review.
Use when building features with **Cursor** in any codebase and the work should go through a disciplined build → review → test → fix loop. Triggers on "run the build loop", "build the next task", "continue the plan", "build this feature properly", or any request to implement work from a plan file or a direct feature prompt. Builds from the plan (or the prompt if no plan exists), runs Cursor's `/review` and fixes every issue found, tests and verifies the feature end to end, fixes anything testing surfaces, and reports back once complete. Repeats until all plan tasks are checked off.
Analyze gaps between implementation plans and actual codebase implementation for the Rust self-learning memory project
Master plugin testing, quality assurance, and validation. Learn unit testing, integration testing, and how to ensure plugin quality.
Align spec files with implementation. Detects drift between spec and code, surfaces discrepancies, user decides whether to update spec or code. Use when both a spec file and its implementation are in context.
Orchestrator-only workflow for migrating/rewriting codebases with full TDD and agent delegation
Implement a task with automated LLM-as-Judge verification for critical steps
Amazon Bedrock AgentCore Evaluations for testing and monitoring AI agent quality. 13 built-in evaluators plus custom LLM-as-Judge patterns. Use when testing agents, monitoring production quality, setting up alerts, or validating agent behavior.
Comprehensive testing and validation strategies for spec-driven development. Learn phase-specific validation techniques, quality gates, and testing approaches to ensure high-quality implementation.