Claude Code Toolkit — AI Agent Reference

For LLMs operating in this repository. Not Claude Code specific — any AI agent benefits from this.

Inventory date: 2026-04-01. Counts verified against filesystem.

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Repository Map

claude-code-toolkit/
  agents/                  # Domain expert markdown files + INDEX.json
  skills/                  # Skill directories, each with SKILL.md
  hooks/                   # Python event hooks + lib/ shared modules
  scripts/                 # Deterministic CLI tools (Python + shell)
  commands/                # Slash-menu command definitions
  adr/                     # Architecture Decision Records
  docs/                    # Documentation (you are here)
  evals/                   # Evaluation test suites
  templates/               # Scaffolding templates (e.g. reddit/)
  services/                # Service configurations
  tests/                   # Cross-component test suites
  .claude/                 # Repo-local settings (gitignored)
  .local/                  # User overlay directory (gitignored)
  .local.example/          # Overlay templates shipped with repo

Component Inventory

Type	Location	What It Is
Agent	`agents/*.md`	Domain expert. Markdown file with YAML frontmatter. Gets loaded as system prompt when routed to.
Skill	`skills/*/SKILL.md`	Workflow methodology. Phased instructions with gates. Paired with an agent at routing time.
Hook	`hooks/*.py`	Python script triggered by Claude Code lifecycle events. Reads JSON from stdin, outputs JSON to stdout.
Script	`scripts/.py`, `scripts/.sh`	Deterministic CLI tool. No LLM judgment -- pure computation, file ops, API calls.
Command	`commands/*.md`	Slash-menu entry point. Maps `/command-name` to a skill invocation.

Commands

Command	File
`/do`	`commands/do.md`
`/install`	`commands/install.md`
`/pr-review`	`commands/pr-review.md`
`/reddit-moderate`	`commands/reddit-moderate.md`
`/retro`	`commands/retro.md`
`/system-upgrade`	`commands/system-upgrade.md`
`/create-pipeline`	`commands/create-pipeline.md`
`/github-profile-rules`	`commands/github-profile-rules.md`
`/perses-onboard`	`commands/perses-onboard.md`
`/github-notifications`	`commands/github-notifications.md`
`/generate-claudemd`	`commands/generate-claudemd.md`

Architecture

Router --> Agent --> Skill --> Script. The /do skill classifies every request by complexity (Trivial, Simple, Medium, Complex), selects a domain agent from agents/INDEX.json, pairs it with a skill, stacks enhancements (anti-rationalization, TDD, verification), and dispatches. Agents do the thinking. Scripts do the computation. Hooks fire at lifecycle boundaries to inject context, capture learnings, and enforce gates. Everything flows through /do -- it's the single entry point.

Entry Point: `/do`

1. Parse request -> classify complexity (Trivial | Simple | Medium | Complex)
2. Check force-route triggers -> if match, invoke that skill immediately
3. Look up agent in agents/INDEX.json -> fallback to static routing table
4. Pair agent with skill (domain default or task-verb override)
5. Dispatch: agent executes with skill methodology loaded as instructions

Trivial = reading a file the user named by exact path. Everything else routes through an agent.

Hook System

Event Types

Event	When It Fires	stdin Schema	Hook Can Block?
`SessionStart`	Session begins	`{}` (no event data)	No
`UserPromptSubmit`	Before processing user message	`{"type": "UserPromptSubmit", "prompt": "..."}`	No
`PreToolUse`	Before a tool executes	`{"type": "PreToolUse", "tool_name": "...", "tool_input": {...}}`	Yes (exit 2)
`PostToolUse`	After a tool executes	`{"type": "PostToolUse", "tool_name": "...", "tool_input": {...}, "tool_result": {"output": "..."}}`	No
`PreCompact`	Before context compression	`{"type": "PreCompact"}`	No
`PostCompact`	After context compression	`{"type": "PostCompact"}`	No
`TaskCompleted`	Task/subagent finishes	`{"type": "TaskCompleted", ...}`	No
`SubagentStop`	Subagent session ends	`{"type": "SubagentStop", "agent_type": "...", ...}`	Yes (exit 2)
`Stop`	Session ends	`{"type": "Stop"}`	No
`StopFailure`	Session ends due to failure	`{"type": "StopFailure", ...}`	No

Field name difference: Both PreToolUse and PostToolUse use tool_name (not tool). PostToolUse adds tool_result with an output field.

Blocking: Exit code 2 from a PreToolUse or SubagentStop hook prevents the action. Exit 0 = allow. All other events ignore exit codes.

Hook Output Format

{
  "hookSpecificOutput": {
    "hookEventName": "PostToolUse",
    "additionalContext": "injected into system context",
    "userMessage": "displayed verbatim to user"
  }
}

Hook-to-Event Mapping

Hook	Event	Purpose
`sync-to-user-claude.py`	SessionStart	Sync repo to `~/.claude/`
`session-context.py`	SessionStart	Load learned patterns from learning.db
`cross-repo-agents.py`	SessionStart	Discover `.claude/agents/` in other repos
`fish-shell-detector.py`	SessionStart	Detect fish shell, inject bash workarounds
`skill-evaluator.py`	UserPromptSubmit	Inject agent/skill routing hints
`auto-plan-detector.py`	UserPromptSubmit	Inject `<auto-plan-required>` for complex tasks
`session-context.py` (dream payload)	SessionStart	Inject pre-built dream payload (LLM-curated by nightly auto-dream, ADR-147)
`instruction-reminder.py`	UserPromptSubmit	Re-inject CLAUDE.md instructions periodically
`capability-catalog-injector.py`	UserPromptSubmit	Inject available capabilities list
`operator-context-detector.py`	SessionStart	Detect operator context in prompts
`perses-mcp-injector.py`	UserPromptSubmit	Inject Perses MCP context
`pipeline-context-detector.py`	UserPromptSubmit	Detect pipeline state
`confidence-decay.py`	Stop	Decay low-use learning.db entries
`pretool-unified-gate.py`	PreToolUse	Consolidated gate: submission, ADR, attribution blocking
`pretool-learning-injector.py`	PreToolUse	Inject relevant learnings before tool execution
`perses-lint-gate.py`	PreToolUse	Block Perses writes that fail linting
`posttool-lint-hint.py`	PostToolUse	Suggest lint fixes after Write/Edit
`error-learner.py`	PostToolUse	Detect errors, record to learning.db
`review-capture.py`	PostToolUse	Capture review agent findings
`retro-graduation-gate.py`	PostToolUse	Check if PR includes retro graduation
`routing-gap-recorder.py`	PostToolUse	Record when /do finds no matching agent
`usage-tracker.py`	PostToolUse	Track skill/agent invocation counts
`agent-grade-on-change.py`	PostToolUse	Re-grade agent after edits
`sapcc-go-detector.py`	SessionStart	Detect SAP CC Go patterns
`user-correction-capture.py`	PostToolUse	Capture when user corrects output
`precompact-archive.py`	PreCompact	Archive learnings before context compression
`postcompact-handler.py`	PostCompact	Re-inject plan context after context compaction
`task-completed-learner.py`	TaskCompleted	Record subagent completion metadata
`subagent-completion-guard.py`	SubagentStop	Block main-branch commits, enforce read-only reviewers
`session-learning-recorder.py`	Stop	Warn on sessions with no learnings
`session-summary.py`	Stop	Persist session metrics to learning.db
`stop-failure-handler.py`	StopFailure	Record session failure for pattern analysis

Sync Lifecycle

hooks/sync-to-user-claude.py fires on SessionStart when cwd is this repo. It copies the repo into ~/.claude/ so that Claude Code instances in other repos get access to agents, skills, hooks, and scripts.

What gets synced:

Source	Destination	Sync Mode
`agents/`	`~/.claude/agents/`	File-by-file copy, stale files removed
`skills/`	`~/.claude/skills/`	File-by-file copy, stale files removed
`hooks/`	`~/.claude/hooks/`	File-by-file copy, stale files removed
`scripts/`	`~/.claude/scripts/`	File-by-file copy, stale files removed
`commands/`	`~/.claude/commands/`	Additive only (never removes)
`.claude/settings.json` hooks	`~/.claude/settings.json` hooks	Replace hook section entirely
`.mcp.json`	`~/.mcp.json`	Merge servers (don't overwrite existing)

Unchanged files are skipped via content comparison.

Settings Architecture

File	Location	Tracked?	Purpose
`settings.json`	`~/.claude/settings.json`	No (runtime)	Hook registrations, permissions. Repo hooks replace on sync.
`settings.local.json`	`.claude/settings.local.json`	No (gitignored)	Repo-local overrides (e.g. MCP tool permissions)
`CLAUDE.md`	Repo root	Yes	Soul document -- global instructions for all sessions

The .claude/ directory inside the repo is gitignored. Only settings.local.json lives there. The ~/.claude/ directory is the runtime home -- hooks, agents, skills all resolve from there when working in external repos.

Key Files

Path	Purpose
`CLAUDE.md`	Soul document -- global instructions for all sessions
`agents/INDEX.json`	Auto-generated agent registry (triggers, pairs_with, complexity)
`skills/INDEX.json`	Auto-generated skill registry
`install.sh`	One-shot installer: `--symlink` or `--copy` to `~/.claude/`
`scripts/learning-db.py`	CLI for learning.db: learn, record, query, stats, graduate, prune
`scripts/generate-agent-index.py`	Regenerates `agents/INDEX.json` from agent frontmatter
`scripts/generate-skill-index.py`	Regenerates `skills/INDEX.json` from skill frontmatter
`scripts/classify-repo.py`	Classifies repos as protected-org or standard
`scripts/scan-ai-patterns.py`	Scans docs for AI writing patterns against `scripts/data/banned-patterns.json`
`scripts/feature-state.py`	Feature lifecycle state machine (`.feature/` directory)
`scripts/adr-query.py`	ADR session management: register, query, check compliance
`scripts/score-component.py`	Deterministic health scorer for agents and skills (9 checks, 100-point rubric)
`scripts/lockfile.py`	PID-based concurrent access protection (acquire, release, status)
`scripts/manifest.py`	Snapshot/undo/verify for system upgrades with SHA-256 checksums
`scripts/task-status.py`	Pipeline progress tracking (start, update, done, show, clear)
`hooks/lib/learning_db_v2.py`	Core learning database module -- all hooks import from here
`hooks/lib/hook_utils.py`	Shared hook utilities: JSON output, fallbacks, frontmatter parsing
`hooks/lib/quality_gate.py`	Quality gate enforcement helpers
`AGENT_TEMPLATE_V2.md`	Template for creating new agents
`skills/shared-patterns/`	Shared anti-rationalization, gate enforcement, verification patterns

Learning System

Database: ~/.claude/learning/learning.db (SQLite, WAL mode, FTS5 full-text search)

Schema (core table)

CREATE TABLE learnings (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    topic TEXT NOT NULL,
    key TEXT NOT NULL,
    value TEXT NOT NULL,
    category TEXT NOT NULL,        -- error|pivot|review|design|debug|gotcha|effectiveness
    confidence REAL DEFAULT 0.5,
    tags TEXT,
    source TEXT NOT NULL,
    source_detail TEXT,
    project_path TEXT,
    session_id TEXT,
    observation_count INTEGER DEFAULT 1,
    success_count INTEGER DEFAULT 0,
    failure_count INTEGER DEFAULT 0,
    first_seen TEXT,
    last_seen TEXT,
    graduated_to TEXT,             -- target file when promoted to agent/skill
    error_signature TEXT,
    error_type TEXT,
    fix_type TEXT,                 -- auto|manual|skill
    fix_action TEXT,
    UNIQUE(topic, key)
);

FTS5 virtual table learnings_fts indexes topic, key, value, tags for fast keyword search.

CLI

# Record a learning scoped to a skill
python3 scripts/learning-db.py learn --skill go-patterns "table tests need t.Parallel()"

# Record scoped to an agent
python3 scripts/learning-db.py learn --agent golang-general-engineer "always check go.sum"

# Query learnings
python3 scripts/learning-db.py query --topic debugging --min-confidence 0.6

# Stats
python3 scripts/learning-db.py stats

# Graduate a learning into an agent or skill file
python3 scripts/learning-db.py graduate TOPIC KEY TARGET

# Prune low-confidence old entries
python3 scripts/learning-db.py prune --below-confidence 0.3 --older-than 90

# ROI report — cohort comparison of sessions with/without retro knowledge
python3 scripts/learning-db.py roi [--json]

# Show stale entries (low confidence, old, not graduated)
python3 scripts/learning-db.py stale [--min-age-days 30]

# Archive stale entries
python3 scripts/learning-db.py stale-prune --dry-run
python3 scripts/learning-db.py stale-prune --confirm

Lifecycle

Record: hooks capture errors, review findings, session patterns automatically
Boost/Decay: confidence adjusts -- success boosts, failure decays, inactivity decays
Inject: session-context.py injects the pre-built dream payload at SessionStart (LLM-curated by nightly auto-dream cycle, ADR-147). Falls back to direct learning.db queries for high-confidence patterns if no fresh payload exists.
Graduate: high-confidence entries get promoted into agent/skill files via learning-db.py graduate

Frontmatter Schema

Agent Frontmatter

---
name: {domain}-{function}-engineer       # kebab-case, ends with -engineer
version: 2.0.0
description: |
  Use this agent when [trigger conditions].
color: blue | green | orange | red | purple
hooks:
  PostToolUse:
    - type: command
      command: "python3 -c \"...\""
      timeout: 3000
routing:
  triggers:
    - keyword1
    - keyword2
  pairs_with:
    - related-skill
  complexity: Simple | Medium | Medium-Complex | Complex
  category: language | infrastructure | review | meta
---

Skill Frontmatter

---
name: skill-name                          # kebab-case
description: |
  What this skill does and when to use it.
version: 1.0.0
user-invocable: true | false              # true = appears in /slash menu
context: fork                             # optional: run in isolated sub-agent
agent: golang-general-engineer            # optional: declare executor agent
model: sonnet | haiku                     # optional: model preference (`/do` is the router exception)
allowed-tools:
  - Read
  - Write
  - Bash
  - Grep
  - Glob
  - Edit
  - Skill
  - Task
  - Agent
routing:
  triggers:
    - keyword1
  pairs_with:
    - related-skill
  complexity: Simple | Medium | Complex
  category: process | content | pipeline | validation
---

Key difference: user-invocable: true makes a skill appear in the slash command menu. Most skills are false -- they're invoked by the router, not directly by users.

Force-Route Triggers

These are mandatory. When triggers match, the skill fires before any other routing.

Skill	Triggers
`go-patterns`	Go test, *_test.go, table-driven, goroutine, channel, sync.Mutex, error handling, fmt.Errorf, review Go, anti-pattern, sapcc, make check
`python-quality-gate`	Python quality, ruff check, bandit scan, mypy check, python lint, check python
`create-voice`	create voice, new voice, build voice, voice from samples, calibrate voice
`voice-writer`	write article, blog post, write in voice, generate voice content
`feature-lifecycle`	design feature, plan feature, implement feature, validate feature, release feature, feature pipeline, full feature lifecycle
`system-upgrade`	upgrade agents, system upgrade, claude update, upgrade skills
`de-ai-pipeline`	de-ai docs, clean ai patterns, fix ai writing, scan and fix docs
`pr-workflow`	push changes, push my changes, create PR, open PR, PR status, sync to GitHub, check CI, CI status, did CI pass, generate branch name, name branch, commit, commit this, commit changes, stage and commit, codex review, second opinion
`fast`	quick fix, typo fix, one-line change, trivial fix, rename variable
`quick`	quick task, small change, ad hoc task, add a flag, small refactor
`perses`	perses, perses dashboard, perses plugin, perses lint, perses deploy, Grafana to Perses
`install`	install toolkit, verify installation, health check toolkit

Pipeline Registry

Pipeline skills have explicit phases with gates between them.

Workflow Skill	Phases
`system-upgrade`	CHANGELOG -> AUDIT -> PLAN -> IMPLEMENT -> VALIDATE -> DEPLOY
`research-pipeline`	SCOPE -> GATHER -> SYNTHESIZE -> VALIDATE -> DELIVER
`workflow`	Multi-phase workflows with references for specific types (review, debug, refactor, deploy, create, research)
`voice-writer`	LOAD -> GROUND -> GENERATE -> VALIDATE -> REFINE -> JOY-CHECK -> OUTPUT -> CLEANUP
`github-profile-rules`	PROFILE-SCAN -> CODE-ANALYSIS -> REVIEW-MINING -> PATTERN-SYNTHESIS -> RULES-GENERATION -> VALIDATION -> OUTPUT

Old pipeline names (explore-pipeline, research-to-article, pr-pipeline, doc-pipeline, workflow-orchestrator, de-ai-pipeline) are now reference files within skills/workflow/references/.

Anti-AI Writing System

The toolkit includes deterministic tooling for detecting and removing AI writing patterns.

Components

Component	Type	What It Does
`scripts/scan-ai-patterns.py`	Script	Regex scan against `scripts/data/banned-patterns.json` (105 patterns, 5 categories)
`skills/anti-ai-editor/`	Skill	Targeted revision: scan for patterns, propose minimal fixes, preserve meaning
`skills/workflow/references/de-ai-pipeline.md`	Skill	Full loop: SCAN -> FIX -> VERIFY, max 3 iterations
`scripts/voice-analyzer.py`	Script	Extract metrics from writing samples
`scripts/voice-validator.py`	Script	Validate content against voice profiles

Wabi-Sabi Principle

Natural imperfections are features. Sterile grammatical perfection is an AI tell.

Do: sentence fragments, run-on sentences, contractions, dashes instead of semicolons, varied sentence length, thoughts that trail off

Don't: "delve", "leverage", "comprehensive", "robust", "streamline", "empower", "harness", "foster", "spearheaded", "synergy"

Scan Command

python3 scripts/scan-ai-patterns.py                      # scan all docs
python3 scripts/scan-ai-patterns.py docs/some-file.md    # scan one file
python3 scripts/scan-ai-patterns.py --errors-only         # suppress warnings
python3 scripts/scan-ai-patterns.py --json                # machine-readable

Exit 0 = clean. Exit 1 = patterns found.

Agent Categories

Category	Examples
Language	golang-general-engineer, golang-general-engineer-compact, python-general-engineer, python-openstack-engineer, typescript-frontend-engineer, typescript-debugging-engineer, nodejs-api-engineer, kotlin-general-engineer, swift-general-engineer, php-general-engineer
Infrastructure	kubernetes-helm-engineer, ansible-automation-engineer, prometheus-grafana-engineer, rabbitmq-messaging-engineer, opensearch-elasticsearch-engineer
Review	reviewer-code, reviewer-system, reviewer-domain, reviewer-perspectives
Data	database-engineer, sqlite-peewee-engineer, data-engineer
Content	technical-documentation-engineer, technical-journalist-writer, github-profile-rules-engineer
Meta	system-upgrade-engineer, toolkit-governance-engineer, hook-development-engineer, pipeline-orchestrator-engineer, project-coordinator-engineer, testing-automation-engineer, mcp-local-docs-engineer
Perses	perses-engineer
UI/Perf	ui-design-engineer, performance-optimization-engineer, react-portfolio-engineer, nextjs-ecommerce-engineer
Research	research-coordinator-engineer, research-subagent-executor

MCP Integrations

MCP Server	Auto-Use Triggers	Tools
gopls	Go workspace, .go files, go.mod	`go_workspace`, `go_file_context`, `go_diagnostics`, `go_symbol_references`, `go_package_api`, `go_vulncheck`, `go_rename_symbol`, `go_search`
Context7	Library docs, API reference, "how do I use X"	`resolve-library-id`, `query-docs`
Playwright	Automated browser validation, screenshot tests	`browser_navigate`, `browser_snapshot`, `browser_take_screenshot`, etc.
Chrome DevTools	Live browser debugging, lighthouse audits	`list_pages`, `navigate_page`, `take_screenshot`, `lighthouse_audit`

MCP instructions are injected into the main session only. Subagents must use ToolSearch() to discover deferred MCP tools.

Quick Reference: Routing a Request

User says "fix the failing Go tests"

1. /do classifies: Simple (code change)
2. Force-route check: "Go test" matches -> go-patterns (MANDATORY)
3. Agent: golang-general-engineer
4. Skill: go-patterns (force-routed, loads testing reference)
5. Enhancements: anti-rationalization-testing auto-injected
6. Plan: task_plan.md created (Simple+ complexity)
7. Dispatch: agent executes with go-patterns testing methodology
8. Learn: record outcome to learning.db

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Claude Code Toolkit — AI Agent Reference

Repository Map

Component Inventory

Commands

Architecture

Entry Point: `/do`

Hook System

Event Types

Hook Output Format

Hook-to-Event Mapping

Sync Lifecycle

Settings Architecture

Key Files

Learning System

Schema (core table)

CLI

Lifecycle

Frontmatter Schema

Agent Frontmatter

Skill Frontmatter

Force-Route Triggers

Pipeline Registry

Anti-AI Writing System

Components

Wabi-Sabi Principle

Scan Command

Agent Categories

MCP Integrations

Quick Reference: Routing a Request

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for-claude-code.md

Latest commit

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File metadata and controls

Claude Code Toolkit — AI Agent Reference

Repository Map

Component Inventory

Commands

Architecture

Entry Point: /do

Hook System

Event Types

Hook Output Format

Hook-to-Event Mapping

Sync Lifecycle

Settings Architecture

Key Files

Learning System

Schema (core table)

CLI

Lifecycle

Frontmatter Schema

Agent Frontmatter

Skill Frontmatter

Force-Route Triggers

Pipeline Registry

Anti-AI Writing System

Components

Wabi-Sabi Principle

Scan Command

Agent Categories

MCP Integrations

Quick Reference: Routing a Request

Entry Point: `/do`