1.6천건 · 관련도순
Benchmark CodeGraph retrieval quality on a real codebase by comparing agent behavior with vs without CodeGraph. Use when the user runs /agent-eval or asks to test, benchmark, audit, or validate a codegraph version (the local dev build or a published npm version) against a language's repo.
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Add a new competitor to the AI Visibility Tool Directory — researches the tool, generates data, takes a screenshot, and inserts into the codebase
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Explain code with visual diagrams and analogies. Use when explaining how code works, teaching about a codebase, or when the user asks "how does this work?"
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Build one release phase against the specs — plan it on an interactive board (endpoints, components, resources, with what already exists read from the codebase), let the user adjust, then build node by node while the board shows live progress. Use when the user wants to build a phase, implement the phase-1 slice, execute the plan from a story map or spec, or watch a build happen visually.
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Runs a multi-phase quality audit of a codebase: derives behavioral requirements from code and docs, generates spec-traced tests, runs a three-pass code review and independent spec audits, and reconciles every bug with regression tests. Use for a deep quality assessment of a project, not a quick PR review.
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Run Ruff checks with optional scope and rule overrides, apply safe and unsafe autofixes iteratively, review each change, and resolve remaining findings with targeted edits or user decisions. Use when cleaning up Ruff lint findings across a Python codebase.
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Debugs OpenAI Codex CLI and app behavior by navigating the codex-rs codebase (core, tui, exec, cli, app-server): config parsing, tool invocations, prompts, and MCP wiring. Use when Codex behaves unexpectedly or a Codex runtime bug needs tracing to source.
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Maps the files relevant to a task before any edit: files to modify, dependencies that may need updates, test files, reference patterns to follow, and risks such as API, migration, or config changes. Use at the start of a non-trivial change in an unfamiliar codebase.
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Makes the agent list the files it needs to see before answering a question about the codebase, instead of guessing. Use when a question depends on code the agent has not read yet, or when earlier answers were confidently wrong about the code.
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Performs security-focused differential review of code changes. Adapts analysis depth to codebase size, uses git blame for context, calculates blast radius by counting callers, checks test coverage of modified code, and generates a markdown report. Use when reviewing a PR, commit, or diff for security vulnerabilities, checking whether a change re-introduces a previously fixed bug, asking what else a change could break, or finding which modified code has no test covering it.
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Use when starting a coding session, setting up a project for AI-assisted development, switching between parts of a codebase, or when agent output quality degrades — hallucinated APIs, wrong patterns, ignored project conventions. Structures what the agent loads and when.
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Generates a complete AGENTS.md at the repository root following the agents.md format: setup, build, test, style, and PR conventions coding agents need. Use when a repo has no AGENTS.md, when onboarding coding agents to a codebase, or when the existing agent instructions are stale.
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Apply GDPR-compliant engineering practices across your codebase. Use this skill whenever you are designing APIs, writing data models, building authentication flows, implementing logging, handling user data, writing retention/deletion jobs, designing cloud infrastructure, or reviewing pull requests for privacy compliance. Trigger this skill for any task involving personal data, user accounts, cookies, analytics, emails, audit logs, encryption, pseudonymization, anonymization, data exports, breach response, CI/CD pipelines that process real data, or any question framed as "is this GDPR-compliant?". Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35.
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Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
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Measure and audit C/C++ code size, namespace distribution, function complexity, nesting, recurrence, and namespace coupling in the EMULATORS framework. Use when Codex needs codebase statistics, complexity baselines, namespace comparisons, duplication indicators, hotspots, architectural growth analysis, or before/after complexity reports while excluding third-party code.
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Core technical, safety, and maintainability rules for codebase edits. Trigger when: - Writing code, refactoring, modifying files, or creating unit tests. - Proposing commits, reviewing git history, or auditing code quality. - Modifying files in any programming language (e.g. *.go, *.rs, *.py, *.ts, *.js, *.c, *.cpp). - Prompt contains keywords: refactor, build, compile, lint, test, git, commit, rule priority, type safety, error handling, defensive programming.
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Maintain and update the persistent project chronicle (docs/chronicle.md). Trigger when: - The human requests a history summary or chronicle update. - Starting work on a new codebase and needing context on its evolution. - Prompt contains keywords: /chronicle, chronicle, project history, git log summary, history summary.
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SOP for the automated refinement loop (/refine) workflow. Trigger when: - Exhaustively auditing and optimizing pre-existing codebase artifacts. - Applying iterative contraction mapping to reduce code entropy. - Prompt contains: /refine, refinement loop, contraction mapping, fixed-point sweep.
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The crouton mental model for someone who has never seen this repo — what "layer" means (two different things), the two-halves rule, the three app.config registries, team-scoped multi-tenancy, manifest-driven field types, the numbered invariants with the incidents behind them, and the known-weak points. Use when orienting in this codebase for the first time, when a question is shaped like "how does crouton work / why is it built this way / where does X live", when deciding whether a change belongs in a package or a generated layer, when confused by an extends chain or by croutonApps/croutonCollections/croutonLayoutBlocks, or before proposing any structural change. Trigger phrases: "what is a crouton layer", "package vs generated layer", "how does multi-tenancy work here", "why is there a stub component", "where do field types come from", "what invariants must hold".
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Find and fix WCAG 2.2 accessibility issues. Two modes — report (sweep a codebase or page, produce a prioritized written report, no edits) and fix (audit→edit→verify loop on a target). Prefers direct-CDP live-DOM auditing; falls back to a browser-MCP composition or HTML-string audits.
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Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
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Run the Tamanu release-branch CUTOFF checks when preparing to cut a new release/X.YY branch: confirm the previous release's hotfixes are merged to main, reconcile the commits since the previous release against the "Merged to main" card list from Linear, and scan for data/reporting/schema changes. Use when the user is cutting a Tamanu release and wants to verify the codebase is ready, wants to check main against the previous release branch, or wants to reconcile commits with a Linear "Merged to main" column (they should paste the card list). Handles Tamanu's train-release model (release/X.YY branches vs vX.YY.Z patch tags) and the cherry-pick model where the same logical fix has different SHAs on different branches. Purely git-based — no live database or Canopy access needed. Not for verifying an existing release branch's hotfix integrity (see pre-release-checks) or for deciding which tests to run (see scope-tamanu-release-tests).
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Integrate Honcho memory and social cognition into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, implementing the dialectic chat endpoint for AI agents, or wiring Honcho into bot frameworks (nanobot, openclaw, picoclaw, etc).
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Delegate coding tasks to Codex, Claude Code, or Pi agents via background process. Use when: (1) building/creating new features or apps, (2) reviewing PRs (spawn in temp dir), (3) refactoring large codebases, (4) iterative coding that needs file exploration. NOT for: simple one-liner fixes (just edit), reading code (use read tool), thread-bound ACP harness requests in chat (for example spawn/run Codex or Claude Code in a Discord thread; use sessions_spawn with runtime:"acp"), or any work in ~/clawd workspace (never spawn agents here). Claude Code: use --print --permission-mode bypassPermissions (no PTY). Codex/Pi/OpenCode: pty:true required.
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