Waza is a collection of installed skills that turn familiar engineering practices into repeatable playbooks for coding agents. It helps agents plan work, design interfaces, review changes, debug problems, edit prose, research unfamiliar subjects, read URLs or PDFs, and check their own health. The catalogue entries are Waza’s skills and supporting agents or instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tw93/Waza --skill healthgit clone --depth 1 https://github.com/tw93/WazaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tw93/waza/health)<a href="https://agentmods.dev/skills/tw93/waza/health"><img src="https://agentmods.dev/badge/skills/tw93/waza/health/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tw93/waza/health"><img src="https://agentmods.dev/badge/skills/tw93/waza/health.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 5 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- high Memory Poisoning · line 31 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- high Tool Misuse · line 120 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- high YARA Match · line 120 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- medium Excessive Agency · line 122 Skill grants unrestricted tool access without appropriate constraints. An agent with unfettered tool access can perform arbitrary actions including file modification, network requests, and code execution.Fix: Restrict tool access to only the tools required for the skill's stated purpose. Use an explicit allowlist rather than granting blanket access.
- medium Excessive Agency · line 214 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.04268 |
| Opus 5 | $0.00036 | $0.02134 |
| Sonnet 5 | $0.00014 | $0.00854 |
| Haiku 4.5 | $0.00007 | $0.00427 |
Grade A, and why
health scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Health: Agent-Assisted Engineering Health
Prefix your first line with 🥷 inline, not as its own paragraph.
Audit the current project's agent setup and AI coding maintainability against this framework:
agent config → instruction surfaces → tools/runtime → verifiers → maintainability
Find violations. Identify the misaligned layer. Calibrate to evidence and risk, not repository size.
Outcome Contract
- Outcome: a budget-aware health report that separates agent configuration risk from AI maintainability risk.
- Done when: each finding names the misaligned layer, the concrete evidence, and a copy-pasteable action or diagnostic command.
- Evidence: collected health script output, tracked project instructions, runtime config summaries, verifier logs, hooks/MCP surfaces, and read-only live probes when needed.
- Output: prioritized findings with status, impact, and next action, or a clear clean bill with residual risk.
Two lanes share one report:
- Agent config health: Codex/Claude/Pi instruction drift, permissions, hooks, MCP, skills, and memory supply chain.
- AI maintainability health: non-obvious constraint reachability, risk-backed hotspot ownership, verifier coverage, generated-artifact checks, and stale or misleading durable docs.
Output language: Check in order: (1) project agent instructions (AGENTS.md before runtime-specific files); (2) global agent instructions; (3) user's recent language; (4) English.
Budget posture: Start with the summary audit. Escalate automatically when the user asks for a deep, full, complete, thorough, "深入", "完整", "彻底", or "继续跑完" audit, when the user explicitly mentions AI coding code rot, Codex/Claude config drift, unclear context, missing verification, verifier output that points at stale paths, or "代码变烂", when current project instructions or remembered user preference says to run deep health checks by default, or when the summary pass exposes a critical ambiguity that cannot be resolved locally. Inventory counts never trigger escalation on their own. Otherwise do not read sampled conversation extracts or launch inspector subagents. Tell the user before escalating because deep health audits can consume significant token quota.
What ships with it
20 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/inspector-context.md 12 KB
- agents/inspector-control.md 5.7 KB
- agents/inspector-maintainability.md 3.6 KB
- references/durable-context.md 2.0 KB
- references/long-running-agents.md 1.8 KB
- references/maintainability-findings.md 8.9 KB
- scripts/block-pipe-to-shell.py 11 KB runs code
- scripts/check_agent_context.py 49 KB runs code
- scripts/check_doc_refs.py 6.8 KB runs code
- scripts/check_maintainability.py 43 KB runs code
- scripts/check_verifier_output.py 3.5 KB runs code
- scripts/check-agent-context.sh 2.6 KB runs code
- scripts/check-doc-refs.sh 2.6 KB runs code
- scripts/check-maintainability.sh 2.6 KB runs code
- scripts/check-verifier-output.sh 2.6 KB runs code
- scripts/collect-data.sh 39 KB runs code
- scripts/conversation_audit.py 34 KB runs code
- scripts/read_skill_description.py 1.7 KB runs code
- scripts/run-health.ps1 14 KB runs code
- scripts/scan_skill_security.py 19 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed ddf50c9e2362
- 6d ago Changed b3fd725bdde8
- 7d ago Changed · -11 lines e504a761d9ba
- 10d ago First seen · 226 lines · 71 tokens per session scan A d31d7bd0eed3
health is a skill published in the GitHub repository tw93/Waza (6,988 stars, last pushed 3d ago), licensed MIT. It adds 71 tokens to every session and 4,268 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
testing
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ui-ux-and-feedback
Use when designing, auditing, or generating a HUD, menu system, or feedback vocabulary for any genre — information hierarchy, diegetic vs. non-diegetic classification, cognitive load budgeting, feedback channel design, menu flow, input modality, and accessibility. Also use to diagnose a UI that is cluttered, that…
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Finds root cause of errors, crashes, unexpected behavior, and failing tests before applying any fix. Not for code review or new features.
playwright-e2e-testing
Production-grade Playwright end-to-end testing skill for AI coding agents. Provides specialized guidance for writing, debugging, and maintaining Playwright tests in TypeScript, JavaScript, and Python. Covers the full testing lifecycle: test structure and architecture (Page Object Model, fixtures, custom matchers)…
playwright-best-practices
Use when writing Playwright tests, fixing flaky tests, debugging failures, implementing Page Object Model, configuring CI/CD, optimizing performance, mocking APIs, handling authentication or OAuth, testing accessibility (axe-core), file uploads/downloads, date/time mocking, WebSockets, geolocation, permissions…
playwright-best-practices
Use when writing Playwright tests, fixing flaky tests, debugging failures, implementing Page Object Model, configuring CI/CD, optimizing performance, mocking APIs, handling authentication or OAuth, testing accessibility (axe-core), file uploads/downloads, date/time mocking, WebSockets, geolocation, permissions…