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/darkroomengineering/cc-settings/auditnpx skills add darkroomengineering/cc-settings --skill auditgit clone --depth 1 https://github.com/darkroomengineering/cc-settingsWhat 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 | $0.00132 | $0.12398 |
| Opus 5 | $0.00066 | $0.06199 |
| Sonnet 5 | $0.00026 | $0.02480 |
| Haiku 4.5 | $0.00013 | $0.01240 |
Grade A, and why
audit scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Empirical only.** Source reading generates *hypotheses*, never findings. A hypothesis becomes a finding only when a measurement confirms it — a profile, a benchmark, a timed curl, analyzer output, a Lighthouse run. How it starts
The opening of the file, as written. The whole thing — 605 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit
Standalone Codex host branch
Claude frontmatter, TLDR, agent teams, dynamic workflows, codex-verifier, and
codex-run.ts do not apply in standalone Codex. Keep source inspection
read-only. Map with rg --files, rg -n, direct import/caller searches, focused
file reads, and the repo's own diagnostics; never claim TLDR ran. Use Context7
only when the user configured that MCP. Otherwise inspect pinned manifests and
lockfiles, consult official package documentation through native browsing when
available, and label currency or API claims unverified when neither source is
reachable. This package does not auto-run unpinned registry MCP packages.
For fan-out, create each new reader with spawn_agent, continue a live reader
with send_message, trigger another turn for an idle existing reader with
followup_task, wait with wait_agent, and stop its current turn with
interrupt_agent only when necessary. Only read-only reviewers
may overlap. Writers share the working tree unless the live host explicitly
offers isolation, so the main session writes the final report after readers
finish; any implementer and test-writer phases must be serialized with
non-overlapping ownership.
One skill, eight whole-repo audit modes. Seven of them share a skeleton: read the surface in full (never sample), hunt with explicit categories, and ship a prioritized, executable report or plan set. Six families of question:
- Codebase — one merged audit, two lenses on the same read. The structure lens (ported from Cursor's internal
thermo-nuclear-code-quality-reviewskill, reported by Eric Zakariasson as Cursor's most-used internal skill; formerly this skill's standalone Maintainability mode) asks should this code exist? — 1k-line sprawl, thin wrappers, code-judo deletions, dependency freshness via context7. The behavior lens (adapted from the fable audit goal-spec trio, gistdiegomarino/04970a2b8d9cc419de3ba05b9a03db5a; formerly the separate Codebase mode) asks does it do what it promises? — correctness, incoherences, affordance gaps. Merged August 2026: both modes fanned the same whole-repo readers over the same files and shipped near-identical reports, so they now run as one pass with two hunt lists. The July 2026 cc-settings audit ran the behavior lens and produced 28 findings, ~all confirmed and fixed. - Docs and Process — from the same fable audit trio. Truth and structure of the docs (docs), walkable end-to-end journeys (process). The mechanics that made the July 2026 audit work (stable IDs, CONFIRMED/PLAUSIBLE, concrete failure scenarios, design tensions vs line findings, open questions for the maintainer) are the contract for these modes.
- Performance — asks where is time actually going, measured? Empirical-only: a finding does not exist until a number confirms it. Covers client runtime (via the same Lighthouse protocol
/lighthouseuses), bundle and build, server and data, and code-level hot paths, adapting to what the repo actually is (web app vs CLI vs library). - Threat-Model — adapted from openai/skills
security-threat-model(Apache-2.0). Asks what can go wrong, and who would exploit it? — trust boundaries, attacker capability, abuse paths tied to attacker goals, mitigations mapped to components. - Motion — adapted from emilkowalski/skills
improve-animations(MIT). Asks where does animation work have the highest leverage? — purpose/frequency, easing/duration, physicality/origin, interruptibility, performance, accessibility, cohesion, and missed opportunities, turned into self-contained implementation plans rather than a findings report. - SEO — distilled from shipped Darkroom work (satus PRs #348/#405/#413 and darkroomengineering/website PRs #40/#65, which converged independently on the same architecture). Asks will this site be found, ranked, and cited? — canonical integrity, sitemap reachability, per-content metadata, structured data, and the AEO surfaces (llms.txt, named AI crawlers, machine-view routes) that answer engines read.
What ships with it
6 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.
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 · 605 lines · 132 tokens per session scan A 84a751aa49fa
audit is a skill published in the GitHub repository darkroomengineering/cc-settings (42 stars, last pushed 4d ago), licensed MIT. It adds 132 tokens to every session and 12,398 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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