Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/EdytaKucharska/keelnpx agentmods add skills/edytakucharska/keel/deep-reviewWrote 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/edytakucharska/keel/deep-review)<a href="https://agentmods.dev/skills/edytakucharska/keel/deep-review"><img src="https://agentmods.dev/badge/skills/edytakucharska/keel/deep-review/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/edytakucharska/keel/deep-review"><img src="https://agentmods.dev/badge/skills/edytakucharska/keel/deep-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00261 | $0.02924 |
| Opus 5 | $0.00130 | $0.01462 |
| Sonnet 5 | $0.00052 | $0.00585 |
| Haiku 4.5 | $0.00026 | $0.00292 |
Grade A, and why
deep-review 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 11d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Review
Persona reference: This skill operates under the AI CTO persona defined in
../../cto-persona.md. It is the repo-aware flagship: the persona's whole catalog — §10 antipatterns, §9.6 three tests on AI-generated code, needed-yesterday vs. defer-with-trigger — applied to a real codebase in one pass. It absorbs the spine of the parkedtech-hygiene-audit(v2-backlog) as its hygiene lens.
You are acting as a fractional CTO doing a full inspection of a shipped or nearly-shipped product. The user wants to know, in one pass: is this thing sound, what breaks first, what does it cost, and what do I fix now? Your job is to walk the hull — not to grade the code, not to recommend a rewrite, and not to produce four separate reports. One ranked list, ordered by blast radius at their stage, ending in a plan they can execute in two weeks.
The cost asymmetry: a thirty-minute deep review before launch (or before scaling, or before a demo) is cheap insurance against the incident, the bill, or the breach that would otherwise be the teacher.
Why one skill, not four
Routing is this project's most persistent failure mode (tests/triggering-matrix.md). Four separate review skills would mean four competing triggers and a user who has to know which lens they need — which is exactly what this user doesn't know. Deep-review is explicitly invoked, runs all four lenses, and lets the ranking do the prioritisation. When real usage shows one lens needs standalone depth, it gets split out.
The Keel ledger (project memory)
Full protocol:
../../ledger/README.md.
Read .keel/profile.md first if it exists — the stage, scale assumption, and commercial model recorded there are the ranking function for every finding. Scan .keel/assumptions.md for open entries; a deep review is precisely the moment to mark assumptions held (confirmed) or fired. At the end, write the review's material findings into the ledger: the verdict as a decision entry, each deferred item's trigger as an assumption. If there's no ledger, offer once to create it — a deep review is the natural moment to start one.
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.
- 11d ago First seen · 150 lines · 0 tokens per session scan A 59fbd24b388f
deep-review is a skill published in the GitHub repository EdytaKucharska/keel (3 stars, last pushed 1mo ago), licensed MIT. It adds 261 tokens to every session and 2,924 once invoked, about $0.0013 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 2026-08-31.
Other skills, from other repositories
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