Waza
🥷 Engineering habits you already know, turned into skills AI assistants like Claude Code, Codex and Cursor can run.
- 7.4k stars
- 423 forks
- 0 open issues
- Language: Python
- License: MIT
- updated
Install
git clone https://github.com/tw93/Waza && cp Waza/*.md ~/.claude/agents/- Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).
- Start a new Claude Code session to load the agents.
- Delegate work to them with the Task/Agent tool or by name.
Editor's note
Waza is a collection of eight structured subagent skills for Claude Code and other AI coding agents, each encoding a specific engineering workflow as a slash command or named invocation. The eight skills cover distinct phases of engineering work: `/think` pressure-tests a problem before any code is written, `/design` drives frontend UI iteration including screenshot-based aesthetic refinement, `/check` reviews diffs and handles release follow-through with evidence, `/hunt` requires root cause confirmation before applying any bug fix, `/write` rewrites prose in natural Chinese and English, `/learn` runs a six-phase research workflow from collection through self-review, `/read` routes URLs and PDFs through platform-specific logic to produce summaries or Markdown output, and `/health` audits agent configuration and AI maintainability with a budget-aware summary pass. Installation works via `npx skills add tw93/Waza` for Claude Code, Codex, OpenCode, and other agents, or through the Claude Code plugin marketplace on v2.1.143 and newer. Engineers who want consistent, opinionated workflows rather than ad-hoc prompting across multiple projects are the primary audience.
8 items in this repository
Reviews code diffs, PRs, issue queues, release readiness, commits, pushes, publishing, and project audits. Use when users ask in any language for code review, issue or PR triage, release gates, publishing follow-through, or project audits. Not for debugging root causes or prose review.
Produces distinctive, production-grade UI for pages, components, visual interfaces, typography, and screenshot-driven polish. Use when users ask in any language for UI, page, component, frontend, typography, screenshot-grounded visual polish, or complaints that a screen looks unclear, ugly, inconsistent, or visually wrong. Not for backend logic or data pipelines.
Runs a budget-aware agent-assisted engineering health audit for instruction/config drift, hooks/MCP, verifier surfaces, and AI maintainability. Use when users ask in any language to audit Claude, Codex, Pi, agent instructions, MCP or hooks, verifier coverage, or AI-maintainability drift. Not for debugging application code or reviewing PRs.
Finds root cause before applying fixes for errors, crashes, regressions, failing tests, broken behavior, and screenshot-reported defects. Use when users report in any language errors, crashes, broken behavior, regressions, failing tests, screenshot evidence, or something that used to work and now fails. Not for code review or new features.
Runs a six-phase research workflow that turns unfamiliar domains, source bundles, or collected material into publish-ready output. Use when users ask in any language to research, study, deep-dive, compile sources, synthesize unfamiliar material, or turn a source bundle into a coherent reference. Not for quick lookups or single-file reads.
Reads URLs and PDFs by fetching source content, defaulting to concise summaries for plain read requests and clean Markdown when asked to convert, save, quote, cite, or feed downstream work. Use when users ask in any language to read, fetch, check, summarize, quote, cite, convert, or save a URL or PDF. Not for local text files already in the repo.
Turns rough ideas into approved, decision-complete plans with validated structure before coding. Use when users ask in any language for planning, architecture, design direction, feasibility, value judgment, or whether a feature is worth doing before implementation. Not for bug fixes or small edits.
Rewrites and polishes prose in Chinese or English, removes AI-like wording, and reviews product localization copy while preserving intent for drafts, docs, release notes, launch copy, and social posts. Use when users ask in any language to draft, rewrite, proofread, localize, polish release notes, remove AI-like wording, or prepare launch and social copy. Not for code comments, commit messages, or inline docs.
Subagents overview
What people ask about Waza
What is tw93/Waza?
tw93/Waza is subagents for the Claude AI ecosystem. 🥷 Engineering habits you already know, turned into skills AI assistants like Claude Code, Codex and Cursor can run. It has 7.4k GitHub stars and its last recorded update is dated 2026-10-18.
How do I install Waza?
You can install Waza by cloning the repository (https://github.com/tw93/Waza) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is tw93/Waza safe to use?
Our security agent has analyzed tw93/Waza and assigned a Trust Score of 97/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains tw93/Waza?
tw93/Waza is maintained by tw93. The last recorded GitHub activity is dated 2026-10-18, with 0 open issues.
Are there alternatives to Waza?
Yes. On ClaudeWave you can browse similar subagents at /categories/agents, sorted by popularity or recent activity.