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/jstoup111/ai-conductor/code-reviewnpx skills add jstoup111/ai-conductor --skill code-reviewgit clone --depth 1 https://github.com/jstoup111/ai-conductorWrote 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/jstoup111/ai-conductor/code-review)<a href="https://agentmods.dev/skills/jstoup111/ai-conductor/code-review"><img src="https://agentmods.dev/badge/skills/jstoup111/ai-conductor/code-review.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.01823 |
| Opus 5 | $0.00016 | $0.00911 |
| Sonnet 5 | $0.00006 | $0.00365 |
| Haiku 4.5 | $0.00003 | $0.00182 |
Grade A, and why
code-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 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Implements the generator/evaluator separation pattern. The evaluator gets a fresh context reset (no shared state with the generator) and is prompted for calibrated skepticism — finding real issues, not rubber-stamping work.
Correctness gate: a review finding is a claim, and a false-confident one either blocks good
work or waves through a bug. Per the /verify-claims protocol, the evaluator attaches a grounded
confidence % and its basis to each finding (verified — reproduced/traced in the code — vs
inferred), never asserts a defect it has not verified, and flags a low-confidence finding as
tentative rather than as a hard defect.
Provider-native delegation
Dispatch the evaluator through the selected host's available subagent facility. Preserve the fresh context boundary, skeptical review, verdict output, and blocking gates regardless of host. Claude delegation: Claude uses the Agent tool; the Claude model choices below apply only to that facility. A Codex-selected run uses its available subagent facility and configured Codex provider policy, without translating Claude model names.
Practices
1. Prepare Review Context
Gather what the evaluator needs:
- Git diff of changes — for batch reviews, scope to the current batch only (commits since
last batch boundary via
git diff <batch-start-commit>..HEAD), not the full branch diff. For the final batch, add a lightweight integration check (full branch diff stat summary) but do NOT re-review earlier batches line by line — they already passed their own evaluator gate. - The story/acceptance criteria being implemented (from
.docs/stories/) - The implementation plan task (from
.docs/plans/) - The relevant affected-test result set
- Tech-context review checklist if loaded in session
- A focused current-HEAD pattern basis, when the task supplies one: current-checkout paths for the relevant target and exemplar, stable symbol or role hints, the semantic traits to preserve or change, and allowed variation. It complements the task criteria and affected-test results; it does not expand review context to the full plan, unrelated stories, or history.
What ships with it
1 file 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.
- 4d ago First seen · 182 lines · 32 tokens per session scan A db3b5d54ca0a
code-review is a skill published in the GitHub repository jstoup111/ai-conductor (7 stars, last pushed yesterday), licensed Apache-2.0. It adds 32 tokens to every session and 1,823 once invoked, about $0.0002 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.
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