本文へ移動
cccskills
無料GitHub で公開

project-cleanup-audit

Audit and safely clean local project repositories or workspaces with excessive generated files, old project residue, dirty Git status, stale worktrees, build caches, duplicate nested projects, confusing roots, or agent context/token waste. Use when the user asks to organize a repo, remove useless files, classify valid project files, clean untracked/deleted files, reduce disk usage, migrate from an old repo layout to a canonical project, or prepare a repository for future Codex/agent work.

インストール方法を見る

含まれるファイル(10)

  • SKILL.md6.7 KB
  • agents/openai.yaml251 B
  • evals/evals.json2.5 KB
  • evals/files/dirty-repo/processes.json238 B
  • evals/files/dirty-repo/status.txt37 B
  • evals/files/ignored-paths/.gitignore37 B
  • evals/files/ignored-paths/.runtime/state.db70 B
  • evals/files/ignored-paths/test_dataset/user.csv29 B
  • references/classification.md4.0 KB
  • scripts/audit_repo_cleanup.ps115.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Project Cleanup Audit

Operating Rules

Use this skill as an evidence-first cleanup workflow for local repositories. Prioritize preserving user work over reclaiming disk.

  • Do not delete, reset, or overwrite until the target paths and rationale are explicit.
  • If evidence is insufficient, provide only known facts, candidate hypotheses, validation paths, and temporary mitigations. Mark unverified conclusions clearly.
  • Keep installed applications, user data directories, credentials, and system-wide caches out of scope unless the user explicitly includes them.
  • Before structural cleanup, create a backup outside the workspace containing Git status, remotes, worktrees, patches, and copies of non-generated untracked files.
  • Stop only development processes whose command line clearly points inside the workspace. Do not stop installed app processes just because the product name matches.
  • Never run whole-repo git clean -fdX unless every ignored dry-run path has been classified as safe generated residue. If the dry-run includes datasets, release artifacts, local validation output, .runtime, .codex, .codegraph, or other agent context, delete only explicit pathspecs or resolved literal paths.
  • Do not recursively print or quote sensitive local data directories while auditing. Prefer aggregate sizes, file counts, top-level names, and short non-sensitive samples.

Quick Audit

Run the bundled read-only audit first when PowerShell is available:

powershell -ExecutionPolicy Bypass -File "<skill>/scripts/audit_repo_cleanup.ps1" -Root . -Top 25

Use -IncludeProcesses when deletion is blocked by locked files or old dev servers:

powershell -ExecutionPolicy Bypass -File "<skill>/scripts/audit_repo_cleanup.ps1" -Root . -Top 25 -IncludeProcesses

Use -HashReleaseArtifacts only when checking whether local release packages match already-published assets. This can be slow on GB-scale archives:

powershell -ExecutionPolicy Bypass -File "<skill>/scripts/audit_repo_cleanup.ps1" -Root . -Top 25 -HashReleaseArtifacts

Read references/classification.md before classifying ambiguous files, local models, installers, datasets, dirty worktrees, or nested legacy projects.

When the audit reports Review Required Ignored Paths, treat those entries as a hard stop for whole-repo ignored cleanup. Classify them path by path before any delete.

Workflow

  1. Restate the cleanup target and boundaries.

    • Identify the intended canonical project, e.g. remote URL, default branch, active package manifest, app name, or repo root.
    • State explicitly what will not be touched, such as installed software, user profile data, or external archives.
  2. Gather read-only facts.

    • Record git rev-parse --show-toplevel, branch, HEAD, remotes, git status --short, worktrees, and top-level sizes.
    • Compare the current root with the canonical upstream when relevant.
    • Use dry runs: git clean -ndX for ignored files and git clean -nd for untracked files.
    • Inspect the audit's review_required_ignored_paths before considering any ignored cleanup.
    • For release-heavy repositories, inspect release_artifacts. If available, compare package names, sizes, and SHA256 values against the release host before deleting versioned archives.
    • Inspect local_context_paths before deleting .codex, .codegraph, .gstack, .venv, .pytest_cache, or __pycache__.
    • Inspect local_data_evidence_paths by aggregate counts and extensions; do not recursively print sensitive document names or contents.
    • Inspect package/build manifests: package.json, Cargo.toml, pyproject.toml, go.mod, pom.xml, *.sln, etc.
  3. Classify paths.

    • delete after confirmation: ignored/rebuildable build outputs, dependency installs, logs, test browser profiles, diagnostics.
    • backup/archive first: untracked source, dirty worktrees, nested old projects, release packages not confirmed uploaded.
    • keep: source, manifests, lockfiles, CI, docs, migrations, public assets, test fixtures.
    • needs explicit user confirmation: local models, datasets, databases, installers, credentials, external user data.
    • For build/, classify tracked packaging configuration separately from ignored build/work, build/runtime, and generated metadata.
  4. Back up before structural changes.

    • Save status/remotes/worktrees/logs to a backup directory outside the workspace.
    • Save git diff --binary patches for dirty tracked files.
    • Copy non-generated untracked files, excluding clearly rebuildable caches such as node_modules, target, dist, .next, and browser output.
  5. Execute the smallest safe cleanup.

    • Prefer exact path deletion or git clean -fdX -- <pathspec...> for ignored generated files after reviewing git clean -ndX.
    • Use whole-repo git clean -fdX only when the ignored dry-run has no review-required paths and all entries are safe generated residue.
    • Use git worktree remove for registered worktrees; do not raw-delete registered worktrees first.
    • Use native shell deletion with literal paths and resolved workspace-bound checks.
    • On Windows, verify resolved absolute paths stay inside the workspace before recursive deletion.
  6. Handle canonical root migration only after backup.

    • If the current directory is an old repo shell and the real product repo is another remote/branch/layout, switch or clone to the canonical root only after preserving dirty work.
    • Do not force-delete unmerged local branches unless the user explicitly accepts losing that history.
    • Prune stale remote refs and tags only after the remote is correct.
  7. Verify.

    • Show final git status, branch/upstream, remotes, worktrees, top-level tree, and disk usage.
    • Run a minimal dependency or build dry-run when reasonable, e.g. npm ci --dry-run --ignore-scripts.
    • Report any remaining locked or intentionally retained residue with reason and size.

Output Format

When reporting results, keep the distinction clear:

  • Known facts
  • Cleanup actions taken
  • Backups created
  • Remaining risks or unverified assumptions
  • Recommended next action

Never present a destructive cleanup as "fixed" if only an audit was completed.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括:AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**:本 skill 走批量 SQL · 离线大数据(PostgreSQL);v2 走实时 API · 单查询。两者互补:单条 NCT 实时状态用 v2,百万级历史挖掘用本 skill。支持云端公共 PostgreSQL(aact-db.ctti-clinicaltrials.org · 零部署)和每日 dump 本地还原(高性能 · 离线)两种连接方式,自动检测优先用本地。跨平台(macOS/Linux/Windows)参数化 SQL 防注入,read-only 强制保护。

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据库进行大批量临床试验历史分析与数据挖掘。触发场景包括:AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、clinical trials data warehouse、疾病领域全景分析、设计相似试验匹配、跨年度试验趋势聚合。本 skill 通过 SQL 接口处理百万级试验记录,支持云端公共 PostgreSQL 服务(aact-db.ctti-clinicaltrials.org)和每日 dump 本地还原两种连接方式,自动检测并优先使用本地高性能模式。

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

Use when work produces a deliverable, factual claim, dataset, code change, decision, publication, deployment, automation, or irreversible action whose failure would matter; when the user asks for a quality gate, acceptance criteria, QA, completeness, validation, audit, evidence, preflight, release readiness, or a definition of done; or before claiming completion on medium- or high-risk work. Automatically decide whether a formal gate is warranted, derive task-specific pass/fail criteria, gather evidence, and block unsupported completion. Make sure to use this skill even when the user does not say "quality gate" if consequential work needs acceptance criteria or completion evidence. Skip formal gating for trivial, reversible, low-impact requests.

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

Create a changeset file in .changeset/ that describes a user-facing change for the release notes

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

Add a new competitor to the AI Visibility Tool Directory — researches the tool, generates data, takes a screenshot, and inserts into the codebase

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

add-lang

無料

Add tree-sitter language support to codegraph end-to-end — wire the grammar + extractor, write tests, then benchmark extraction quality and retrieval value on 3 popular real-world repos. Use when the user runs /add-lang <language> or asks to add/support a new language (e.g. Lua, Elixir, Zig, OCaml) in codegraph.

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

EthanYoQ のスキルをすべて見る

このスキルの問題を報告する