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v202604

cc-agent-audit

@cablate⭐ 15 stars

Claude Code 專案配置審計。觸發:review/優化 CLAUDE.md、skills、settings、定期清洗累積內容、新專案上線前檢查。

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You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.

1. Native installer

This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.

Do not guess an installer command or replace an existing version without reviewing the diff.

2. Complete package recommended

Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.

No complete ProSkills package is published for this listing yet.

3. Prompt-only

Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.

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No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.

—/10

// RATINGS

⭐GitHub Stars
⭐⭐ 15 on GitHubGitHub ↗

Growing

🟢ProSkills Score
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Not yet listed on ClawHub or SkillsMP

// README

A batteries-included working environment for Claude Code. Dispatch agents, skills, and statusline — all extracted from daily production use.

What's Inside

ai-toolkit/
├── agents/                  # SOP agents (symlink to ~/.claude/agents/)
│   ├── analyst.md           # Architecture / Planning / Audit
│   ├── investigator.md      # Search / Explore / Debug / External research
│   ├── builder.md           # Code implementation / Testing
│   ├── reviewer.md          # Code review / Dead code cleanup
│   ├── doc-sync.md          # Doc init / Doc sync
│   └── agent-factory.md     # Design and generate new agents
├── skills/                  # Skills (symlink to ~/.claude/skills/)
│   ├── handoff/             # Session handoff
│   ├── thorough/            # Relentless delivery mode
│   ├── vector-memory/       # Persistent vector memory usage guide
│   ├── project-docs/        # Project documentation structure
│   ├── agentskill-expertise/ # Skill design knowledge base
│   ├── collaboration-style/ # AI-human collaboration framework
│   └── self-growth/         # Continuous learning framework
├── domain-skills/           # Domain-specific skill sets
│   ├── darkseoking/         # SEO & Threads algorithm (3 skills)
│   └── claude-code/         # Claude Code reverse engineering (6 skills)
├── mcp.example.json         # MCP server config template
└── statusline/              # Cost & context monitoring

Agents

SOP-style prompts for Claude Code's Agent tool. When /thorough dispatches parallel subagents, prompt quality determines output quality — these agents provide step-based workflows with hard thresholds, classification heuristics, and structured output formats.

AgentModelWhen to use
analystsonnet"design this", "plan the implementation", "audit codebase health"
investigatorhaiku"find all usages of X", "how does this work", "why does this fail"
buildersonnet"implement this", "modify the handler", "write tests for X"
reviewersonnet"review this code", "find dead code", "clean up unused exports"
doc-synchaiku"set up project docs", "sync docs after changes"
agent-factoryopus"create a new agent", "improve this agent's prompt"

Each agent auto-detects its mode from dispatch context. One agent, multiple workflows.

Design Principles

  1. Zero concept explanation — All operational instructions. Claude already knows what CQRS is.
  2. Step-based SOP — "Do X, then Y, if Z threshold → action." Not "You are an expert at..."
  3. Hard rules as threshold + trigger — >50 lines → flag, >4 nesting levels → flag. Not "keep functions small."
  4. Classification heuristics — AUTO-FIX / ASK / CRITICAL with concrete criteria. Not checklists.
  5. Structured output — Every agent ends with a report template. Consistent, parseable.

Skills

SkillDescription
/handoffSession handoff — compress context into a structured prompt for seamless continuation
/thoroughRelentless delivery mode — exhaust all options, cost-aware model selection, verify before done
/vector-memoryPersistent vector memory via LanceDB — store facts, decisions, lessons across sessions
/project-docsProject documentation structure — standard proj-[name]/ layout with ADRs, stories, and operations guides
/agentskill-expertiseAgent Skill design knowledge base — mechanisms, philosophy, patterns, pitfalls
/collaboration-styleAI-human collaboration norms — friction cases, coding style, behavioral guidelines
/self-growthContinuous learning framework — learn from work, organize knowledge, build feedback loops

Domain Skills

Deep skill sets built around specific topics or practitioners' methodologies. Unlike generic skills, these encode domain expertise with layered architecture (knowledge → operations → prediction).

DomainSkillsDescription
darkseoking3SEO & Threads algorithm — mindset (8 mental models), post optimizer (pre-publish checklist), post predictor (V2 dual-stage Views×ER)
claude-code6Claude Code reverse engineering — prompt craft, cost engineering, harness patterns, security, agent design, agent audit

Each domain has its own README with setup instructions and architecture overview.

MCP Servers

Example configuration for the MCP servers used in this toolkit.

mcp.example.json — copy to your project as .mcp.json and fill in your API keys.

ServerWhat it does
@cablate/memory-lancedb-mcpPersistent vector memory with hybrid search (semantic + keyword)
SerenaSemantic code intelligence — symbol search, references, refactoring

Statusline

Cost and context monitoring for Claude Code. Two-line display with context alerts and plan usage tracking.

 Normal (< 60% context):
┌──────────────────────────────────────────────────────────────────┐
│ Claude Opus 4  | [=======--------------] 45.2K/200.0K 22.6%    │
│ 5h: 12.3% (4h 22m) | 7d: 8.1% (6d 3h)                        │
└──────────────────────────────────────────────────────────────────┘

 Warning (>= 60% context):
┌──────────────────────────────────────────────────────────────────┐
│ Claude Sonnet 4 | concise | [============--------] 130.5K/200.0K 65.3%  /handoff soon │
│ 5h: 45.0% (2h 10m) | 7d: 22.4% (5d 1h)                       │
└──────────────────────────────────────────────────────────────────┘

 Critical (>= 80% context):
┌──────────────────────────────────────────────────────────────────┐
│ Claude Opus 4  | [==================--] 310.0K/200.0K 95.0%  !! HANDOFF NOW !! │
│ 5h: 78.2% (1h 05m) | 7d: 51.3% (3d 12h)                      │
│ !! DO NOT close/resume -- use /handoff first, or waste 6%+ of 5h tokens !! │
└──────────────────────────────────────────────────────────────────┘

Line 1 — Model name, output style (if not default), context progress bar with K-precision token counts, usage %, and alerts at 150K/200K/300K thresholds.

Line 2 — 5-hour and 7-day plan usage rates with reset countdowns. Fetched from Claude API (cached 5min) or inline rate_limits (v2.1.80+).

Line 3 — Appears at 250K+ tokens. Hard warning against closing/resuming without handoff.

statusline/statusline.ps1

// ~/.claude/settings.json
{ "status_line_command": "powershell -NoProfile -File C:/Users/YOU/.claude/statusline.ps1" }

License

MIT

// HOW IT'S BUILT

KEY FILES

domain-skills/claude-code/cc-agent-audit/SKILL.mdREADME.md

// REPO STATS

15 stars