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Found 17 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".
Autonomous multi-round research review loop. Repeatedly reviews via Codex MCP, implements fixes, and re-reviews until positive assessment or max rounds reached. Use when user says "auto review loop", "review until it passes", or wants autonomous iterative improvement.
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
Use when iterative review-fix cycles are needed on a plan or implementation — bounded loop with severity gating, automatic fixes, and finding disposition.
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
Self-improving review loop for Ralph Wiggum skills. Reviews skills against best practices, implements improvements, and continues until two consecutive clean reviews. Use when validating or improving the ralph-prompt-* skill suite.
Implement important or complex changes in a git repository through independent review-and-fix cycles. Use when a user asks for an implementation to be repeatedly reviewed, hardened, or iterated until it is ready to ship.
Visual self-review loop for motion graphics — render a draft, build contact sheets at every timeline beat, look at them, critique against a checklist, fix, repeat. Use after any change to a video's visuals or timing, and before calling a video done.
Thoroughly improve code quality by requesting a review from a sub-agent after completing tasks requiring code editing, before returning the conversation turn to the user. Use for any task that edits code or configuration. Read this skill before starting edits, and after completing the requested changes run an isolated sub-agent review loop before returning the final response.
Use when one agent is implementing code and another agent must review the resulting changes, compare the summary against the actual files, decide whether to fix now or move on, and write the next tightly scoped prompt with context handoff guidance.
Use when addressing open PR review comments from any reviewer (human or bot) within the current agent session. For a fresh-context-per-comment approach, use ralph-wiggum-loop instead.
Iteratively review and fix a commit, branch, ref, range, current change set, or historical feature until an independent reviewer explicitly approves it under a strict code-quality standard. Use for a thermo-nuclear review loop, strict review/fix cycles, or when a change must survive an adversarial reviewer without growing defensive code.