by notque
AI Agent System - Creator of the /do router
# Add to your Claude Code skills
git clone https://github.com/notque/claude-code-toolkitGuides for using ai agents skills like claude-code-toolkit.
Last scanned: 5/27/2026
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}claude-code-toolkit is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by notque. AI Agent System - Creator of the /do router. It has 353 GitHub stars.
Yes. claude-code-toolkit passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/notque/claude-code-toolkit" and add it to your Claude Code skills directory (see the Installation section above).
claude-code-toolkit is primarily written in Python. It is open-source under notque on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh claude-code-toolkit against similar tools.
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Claude Code Toolkit is a complete agent-driven workflow system for Claude Code and Codex. It gives Claude and Codex domain-specific expertise, step-by-step workflows, and automated quality gates. The result is Claude working like a team of Go, Python, Kubernetes, review, and content specialists instead of one generalist.
It starts from the moment you type a request. You don't pick agents, configure workflows, or learn any internal concepts. You just say what you want done.
/do debug this Go test
In Claude Code, the smart router command is /do. In Codex, use $do.
A router reads your intent and selects a Go specialist agent paired with a systematic debugging methodology. The agent creates a branch, gathers evidence before guessing, runs through phased diagnosis, applies a fix, executes tests, reviews its own work, and presents a PR. You describe the problem. The system handles everything else.
This works because the toolkit separates what you know from what the system knows. Agents carry domain expertise (Go idioms, Python conventions, Kubernetes patterns). Skills enforce process methodology (TDD cycles, debugging phases, review waves). Hooks automate quality gates that fire on lifecycle events. Scripts handle deterministic operations where you want predictable output, not LLM judgment. The router ties it all together, classifying requests, selecting the right combination, and dispatching.
The result: consistent, domain-specific output across Go, Python, TypeScript, infrastructure, content, and more. No configuration required. A first-time user and a power user get the same quality results. The power user just understands why.
A game built entirely by Claude Code using these agents, skills, and pipelines. Every step from design through implementation.
Requires Claude Code installed and working (claude --version should print a version number). Codex CLI is also supported: the installer mirrors toolkit skills into ~/.codex/skills and toolkit agents into ~/.codex/agents so Codex can use the same skill and agent library (codex --version should print a version number if you want Codex support too).
git clone https://github.com/notque/claude-code-toolkit.git ~/claude-code-toolkit
cd ~/claude-code-toolkit
./install.sh --symlink
The installer links agents, skills, hooks, commands, and scripts into ~/.claude/, where Claude Code loads extensions from. It also mirrors skills into ~/.codex/skills and agents into ~/.codex/agents for Codex. Use --symlink to get updates via git pull, or run without it for a stable copy.
Verify the install with:
python3 ~/.claude/scripts/install-doctor.py check
python3 ~/.claude/scripts/install-doctor.py inventory
If you update the repo later and want Codex to see newly added skills, rerun ./install.sh --symlink from the repo root.
Command entry points:
/do$doDetailed setup: docs/start-here.md
The toolkit mirrors agents, skills, and a curated subset of hooks into ~/.codex/ so they work with the OpenAI Codex CLI alongside Claude Code. The mirror runs automatically on every install.sh; no flags required.
What mirrors
agents/ (and private-agents/ if present) is copied or symlinked to ~/.codex/agents/.skills/, private-skills/, and private-voices/*/skill/ goes to ~/.codex/skills/.~/.codex/hooks/ with a matching generated ~/.codex/hooks.json. The allowlist covers SessionStart injectors (KAIROS briefing, operator context, team config, rules distill), a Stop recorder (session learning), and a PostToolUse Bash scanner. See scripts/codex-hooks-allowlist.txt.install.sh sets [features] codex_hooks = true in ~/.codex/config.toml using a TOML merge that preserves existing sections.What does not mirror (yet)
Bash tool today, so any hook guarding Write or Edit would register but never run.PreCompact, SubagentStop, Notification, or SessionEnd events; hooks on those events stay Claude Code only.install.sh warns if the installed version is below that, and prints a neutral note if the codex binary is not found.Opting out
There is no opt-out flag. The mirror is harmless when Codex CLI is not installed: ~/.codex/ entries sit unused until you install Codex. To skip the hooks portion, delete ~/.codex/hooks/ and ~/.codex/hooks.json after install; the toolkit does not recreate them on normal use.
Reference: adr/182-codex-hooks-mirror.md. Upstream Phase 2 tracker: openai/codex#16732.
The toolkit already supplies routing (/do), domain knowledge (agents), methodology (skills), and enforcement (hooks, CLAUDE.md). The shipped Claude Code system prompt, which is several thousand tokens, largely duplicates that structure for toolkit users. Override it to shrink per-request token cost:
claude --system-prompt "."
The . is just a non-empty placeholder (some shells reject an empty string). This is a token-economy and architectural-fit pattern, not a claim about output quality.
Trade-off: overriding the default strips Claude Code's built-in tool-use instructions and style guidance. That context comes back through the toolkit's own agents, skills, hooks, and CLAUDE.md, which is why the pattern fits here. On a bare Claude Code install without the toolkit, use --append-system-prompt "..." instead so the default guidance stays in place.
Routing. You type a request. The router entry point is /do in Claude Code and $do in Codex. It classifies intent, selects a domain agent and a workflow skill, and dispatches. No menus, no configuration.
Planning. For non-trivial work, the system creates a plan before touching code. Plans have phases, gates, and saved artifacts at each step.
Execution. A domain-specific agent handles the work using its skill's methodology. Go work gets Go idioms. Python work gets Python conventions. Reviews get multi-wave specialist panels.
Quality gates. Hooks fire automatically: anti-rationalization checks on code modifications, error learning after failures, context injection at session start. Quality is structural, not advisory.
Verification. Tests run. Deterministic scripts validate what LLM judgment cannot. The system does not claim completion without evidence.
Delivery. Changes land on a feature branch. PRs include lint checks, test runs, and review gates. Nothing merges without CI passing.
Agents carry domain-specific expertise. Not thin wrappers that say "you are an expert," but concrete knowledge: version-specific idiom tables, anti-pattern catalogs with detection commands, error-to-fix mappings from real incidents.
Software Engineering
Code Review
Frontend & Creative
Infrastructure
Skills enforce methodology. They are phased workflows with gates that prevent skipping steps.
Development Workflows: test-driven development, systematic debugging, feature lifecycle, subagent-driven development, pair programming
Code Quality: parallel code review (3 simultaneous reviewers), systematic code review, code cleanup, universal quality gates, linting
Content & Research: voice-validated writing, research pipelines, content calendars, SEO optimization, topic brainstorming, content repurposing
Operations: PR workflow with quality gates, git commit flow, GitHub Actions checks, cron job auditing, service health checks, Kubernetes debugging
Meta: skill evaluation, agent comparison, A/B testing, toolkit evolution, reference enrichment, routing table management
Event-driven automation that fires on session start, before/after tool use, at compaction, and on stop. Error learning, context injection, quality enforcement, and anti-rationalization all run automatically.
Deterministic Python utilities for mechanical operations: INDEX generation, learning database management, voice validat