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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.

Agent	Model	When to use
analyst	sonnet	"design this", "plan the implementation", "audit codebase health"
investigator	haiku	"find all usages of X", "how does this work", "why does this fail"
builder	sonnet	"implement this", "modify the handler", "write tests for X"
reviewer	sonnet	"review this code", "find dead code", "clean up unused exports"
doc-sync	haiku	"set up project docs", "sync docs after changes"
agent-factory	opus	"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

Skill	Description
/handoff	Session handoff — compress context into a structured prompt for seamless continuation
/thorough	Relentless delivery mode — exhaust all options, cost-aware model selection, verify before done
/vector-memory	Persistent vector memory via LanceDB — store facts, decisions, lessons across sessions
/project-docs	Project documentation structure — standard proj-[name]/ layout with ADRs, stories, and operations guides
/agentskill-expertise	Agent Skill design knowledge base — mechanisms, philosophy, patterns, pitfalls
/collaboration-style	AI-human collaboration norms — friction cases, coding style, behavioral guidelines
/self-growth	Continuous 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).

Domain	Skills	Description
darkseoking	3	SEO & Threads algorithm — mindset (8 mental models), post optimizer (pre-publish checklist), post predictor (V2 dual-stage Views×ER)
claude-code	6	Claude 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.

Server	What it does
@cablate/memory-lancedb-mcp	Persistent vector memory with hybrid search (semantic + keyword)
Serena	Semantic 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