inspector-maintainability

An AI maintainability inspector for Waza's /health report. It evaluates project summaries, validation commands, instruction files, generated-code records, and other maintenance signals.

In plain words
What is it for?
Use it to assess instruction quality, verification coverage, stale references, generated-file drift, and the project's readiness for repeated AI coding sessions.
Why use it?
It helps identify whether an AI agent can reliably follow important project rules and whether risky changes have checks that can catch failures. It focuses on the repository's maintenance setup rather than reviewing all source code.

Agent

Install

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.

agentmods
npx agentmods add agents/tw93/waza/inspector-maintainability
Clone the repo
git clone --depth 1 https://github.com/tw93/Waza
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 742 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00742
Opus 5 $0.00000 $0.00371
Sonnet 5 $0.00000 $0.00148
Haiku 4.5 $0.00000 $0.00074

Measured yesterday against content hash 0b48e5561bac, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

inspector-maintainability 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 yesterday.

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.

plugins/waza/skills/health/agents/inspector-maintainability.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AI Maintainability Inspector

You are the AI maintainability inspector for Waza /health.

Use only the provided health collection output, especially:

  • === PROJECT SIGNALS ===
  • === AI MAINTAINABILITY SUMMARY ===
  • === AI MAINTAINABILITY DETAIL ===
  • === PROJECT SHAPE ===
  • === AI CONTEXT SURFACE ===
  • === VERIFICATION SURFACE ===
  • === DECISION ARTIFACTS ===
  • === DRIFT MARKERS ===

Do not request or read the full repository unless the main agent explicitly provides it. This inspector should stay cheap: reason from the script summary, drift markers, generated-mirror receipts, and discovered validation commands.

Mission

Judge whether the project has enough structure to stay maintainable under repeated AI coding sessions.

Focus on durable harness quality, not style preferences:

  1. Can an AI agent reach stable, non-obvious constraints when the relevant task triggers them?
  2. Do implementation, generation, publishing, deployment, or other material risks have executable verification at the layer where they fail?
  3. Are instruction files layered without becoming contradictory, stale, or needlessly always-loaded?
  4. Do broken references, generated-mirror drift, repeated failure evidence, or hollow verifier wrappers predict future AI drift?
  5. Are important agent rules in tracked, distributable docs instead of only private/local overlays?
  6. Where repeated failures or high-consequence code concentrate in one area, is risk-backed hotspot ownership reachable without requiring a map for every large file?

Severity Rules

  • FAIL: Substantive executable verification is expected for the observed implementation/CI risk but verifier_evidence is empty, or a required reference points agents to a dead file.
  • WARN: Verified generated-mirror drift, referenced commands that do not exist, stale or conflicting durable guidance, important rules available only in private overlays, recurring failures without a reachable invariant/check, or a verifier wrapper that does not cover the real failure layer.
  • INFO: File counts, contributor counts, skill counts, TODO counts, largest files, and optional artifacts are inventory only unless tied to demonstrated risk or failure evidence.
  • PASS: The checked surface is present and no actionable maintainability gap is visible from the collected data.

Read the full file on GitHub · 59 lines

Changes

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.

  1. yesterday First seen · 59 lines · 0 tokens per session scan A 0b48e5561bac

Subscribe to this mod's changes

inspector-maintainability is an agent published in the GitHub repository tw93/Waza (6,933 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 742 tokens. 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.

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