1.6천건 · 관련도순
Refine an existing implementation plan with a second iteration. Re-analyzes the codebase for gaps, missing tasks, and wrong dependencies. Use after /unikit-plan or to improve a /unikit-fix plan, or when the user says "improve the plan", "what's wrong with the plan", or "update the plan from research". Optional +check flag validates refinements via a fresh-context subagent.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Adopt declscope on an existing Go codebase and drive its diagnostics to zero. Read this when introducing declscope to a repository, when choosing its configuration, when running declscope shrink for the first time, or when clearing a declscope baseline. Covers sizing each rule before enabling it, reading the diagnostics as structure, the remedy for each shape, and the measurement traps that produce false confidence. For writing new code in a repository that already runs declscope, use declscope-authoring.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Core conventions and patterns used in the Squad codebase
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Strategic planning consultant that produces decision-complete work plans through Socratic interview, codebase exploration, Metis gap analysis, and optional Momus high-accuracy review. MUST USE when the task has 5+ steps, scope is ambiguous, multiple modules are involved, or the user asks for a plan. Triggers: plan this, create a work plan, interview me, start planning, prometheustic, plan mode, help me plan this, break this down.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Proactive bug-hunting workflow. Assesses codebase risk through complexity, coverage, and structural analysis, then spawns focused investigators that write reproducing tests to validate suspected bugs. Thoroughness over speed. Advisory only — produces findings and proposes tickets; does not implement fixes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Architectural review workflow. Analyzes codebase organization via noun analysis and produces a target blueprint. Advisory only — does not implement changes. Always offers to cut tickets for the planned work; orchestrators receive the proposal and apply their own autonomy judgment to approve / edit / decline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apply these opinionated Python 3.11+ conventions when writing Python in this codebase: type hints with mypy, async/await, pytest fixtures, dataclasses, Poetry packaging, production patterns.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit and maintain the learnings in `docs/solutions/` against the current codebase — fix drifted paths and links, merge overlapping docs, replace guidance the code no longer supports, delete obsolete docs, reconcile the `CONCEPTS.md` glossary, then report and commit. Also bootstraps `CONCEPTS.md` on request and, when asked, culls docs whose reasoning the code already explains. Use for "refresh the learnings", "audit docs/solutions", "are our solution docs still accurate", "clean up / prune the learnings", "create a CONCEPTS.md", or after a refactor, rename, migration, or dependency upgrade that likely invalidated documented solutions. Not for capturing a new learning — use `learn-with-docs`.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run a multi-category code audit on the cqs codebase. Spawns parallel agents per batch.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Capture architectural decisions made during Claude Code sessions as structured ADRs. Auto-detects decision moments, records context, alternatives considered, and rationale. Maintains an ADR log so future developers understand why the codebase is shaped the way it is.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Search a codebase or UI for places that don't animate but should, and reject everything that shouldn't. Read-only; it proposes motion with exact values, it does not implement it. Use when the user asks "what could be animated here?" or wants to "make this feel more alive". For fixing existing animations, use improve-animations or review-animations instead.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Scans codebase for hardcoded secrets. Returns normalized findings with severity and remediation guidance. Pre-commit hook integration.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Persistent cross-session knowledge base. Manages per-project learnings that compound across sessions — patterns, pitfalls, preferences, and project quirks. The agent gets smarter on your codebase over time. At session start, searches relevant learnings. During sessions, logs discoveries. Supports review, search, prune, and export. Use proactively when the agent encounters a durable project quirk, user preference, or pattern that would save 5+ minutes next time. Also use at session start to recover context. Triggers: "remember this", "log this", "what have we learned?", "show learnings".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when reviewing a codebase for architectural friction, shallow modules, poor test seams, scattered behavior, duplicated orchestration, or opportunities to deepen modules before implementing a ticket or workflow.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when designing or improving a module interface, choosing a seam, reducing shallow modules, making code easier to test, or reviewing architecture for locality, leverage, adapters, and deep-module design.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze any GitHub repository in AI-friendly format. Convert entire repos to single markdown documents, generate architecture diagrams with Mermaid, inspect structure trees, language breakdowns, and recent activity. Includes GitHub URL tricks, API shortcuts, and advanced search techniques. Read-only analysis — never executes code from repositories. Built for AI agents — Python stdlib only, no dependencies. Use for repository analysis, code architecture review, open source research, GitHub intelligence, repo documentation, and codebase understanding.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Adopt better-result in an existing TypeScript codebase. Use when replacing try/catch, Promise rejection handling, null sentinels, or thrown domain exceptions with typed Result workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Score a repository's agentic legibility from repo-visible evidence only. Use when Codex needs to audit how easy a codebase is for coding agents to discover, bootstrap, validate, and navigate, especially for harness-engineering reviews, developer-experience audits, repo cleanup, or before/after comparisons after improving docs, tooling, or architectural constraints.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Knip dead code detection best practices for JavaScript and TypeScript projects. Use when configuring Knip, analyzing unused code, setting up CI integration, or cleaning up codebases. Triggers on knip.json, dead code, unused exports, unused dependencies, bundle optimization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Code auditor that grades software projects and the agents/developers who built them. Produces structured JSON + Markdown reports with a standardized rubric. Evaluates architecture, correctness, testing, authorship cohesiveness, ghost code, and more. Optionally ingests tmux session logs for process evidence and interrogates agents about specific problems found. Use when asked to "audit", "grade", "evaluate", "review code quality", "rate this project", "score the agents", or "assess the codebase". Especially useful after Marcus multi-agent experiments to grade agent output and track quality over time.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate synthetic code context for LLM coding tasks. Automatically use this before making code changes, fixing bugs, refactoring, or answering questions about the codebase. Provides execution paths, relevant files, symbols, tests, and code snippets.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Manual-only skill for minimizing total codebase size. Only activate when explicitly requested by user. Measures success by final code amount, not effort. Bias toward deletion.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.