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 agentmods add skills/danielvm-git/bigpowers/inspect-qualitynpx skills add danielvm-git/bigpowers --skill inspect-qualitygit clone --depth 1 https://github.com/danielvm-git/bigpowersWhat 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 | $0.00064 | $0.01152 |
| Opus 5 | $0.00032 | $0.00576 |
| Sonnet 5 | $0.00013 | $0.00230 |
| Haiku 4.5 | $0.00006 | $0.00115 |
Grade A, and why
inspect-quality 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 2d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inspect Quality
HARD GATE — HARD GATE — Quality metrics (coverage, lint, cyclomatic complexity, security scans) must be monitored. If a metric degrades, surface it as a blocker. Do NOT accept regressions.
Run an interactive QA session. The user describes problems they're encountering. You clarify, explore the codebase for context, and log each issue to specs/bugs/registry.yaml with a structured, durable format.
For each issue the user raises
1. Listen and lightly clarify
Let the user describe the problem in their own words. Ask at most 2–3 short clarifying questions focused on:
- What they expected vs what actually happened
- Steps to reproduce (if not obvious)
- Whether it's consistent or intermittent
Do NOT over-interview. If the description is clear enough to log, move on.
2. Explore the codebase in the background
Kick off an Agent (subagent_type=Explore) to understand the relevant area. The goal is NOT to find a fix — it's to:
- Learn the domain language used in that area (check
specs/UBIQUITOUS_LANGUAGE_LATEST.mdif present) - Understand what the feature is supposed to do
- Identify the user-facing behavior boundary
3. Assess scope: single issue or breakdown?
Break down when:
- The fix spans multiple independent areas
- There are clearly separable concerns that could be worked on in parallel
- The user describes something with multiple distinct failure modes
Keep as a single issue when:
- It's one behavior that's wrong in one place
- The symptoms are all caused by the same root behavior
4. Log to specs/bugs/registry.yaml
Append the issue to specs/bugs/registry.yaml. Create the specs/bugs/ directory if it doesn't exist.
registry.yaml format
The file maintains a Markdown table with the following columns (derived from structured audit practice):
| Field | Description |
|---|---|
bug_id |
BUG-YYYY-MM-DDTHHMMSS |
date |
YYYY-MM-DD |
severity |
critical / high / medium / low |
priority |
p0 / p1 / p2 / p3 |
scope |
kebab-case area (e.g. auth, checkout) |
what_happened |
actual behavior (user-facing terms) |
what_expected |
expected behavior |
steps_to_reproduce |
numbered steps |
root_cause |
one-line hypothesis |
files_changed |
filled in after fix |
approach |
filled in after fix |
risk_level |
low / medium / high |
new_tests |
count (filled in after fix) |
type_check |
pass / fail (filled in after fix) |
lint |
pass / fail (filled in after fix) |
commit_type |
fix / fix! / feat (filled in after fix) |
release_type |
patch / minor / major (filled in after fix) |
commit_message |
Conventional Commits message (filled in after fix) |
follow_ups |
semicolon-separated follow-up items |
file |
path to detailed specs/bugs/BUG-*.md (filled in by investigate-bug) |
status |
open / in-progress / fixed / wont-fix |
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.
- 2d ago First seen · 107 lines · 64 tokens per session scan A 6d060771c770
inspect-quality is a skill published in the GitHub repository danielvm-git/bigpowers (156 stars, last pushed 25d ago), licensed MIT. It adds 64 tokens to every session and 1,152 once invoked, about $0.0003 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.
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