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 skills add BlackBeltTechnology/pi-agent-dashboard --skill review-codegit clone --depth 1 https://github.com/BlackBeltTechnology/pi-agent-dashboardWrote 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/blackbelttechnology/pi-agent-dashboard/review-code)<a href="https://agentmods.dev/skills/blackbelttechnology/pi-agent-dashboard/review-code"><img src="https://agentmods.dev/badge/skills/blackbelttechnology/pi-agent-dashboard/review-code/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/blackbelttechnology/pi-agent-dashboard/review-code"><img src="https://agentmods.dev/badge/skills/blackbelttechnology/pi-agent-dashboard/review-code.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00096 | $0.02768 |
| Opus 5.5 | $0.00038 | $0.01107 |
| Sonnet 5.5 | $0.00019 | $0.00554 |
| Haiku 4.5 | $0.00010 | $0.00277 |
Grade A, and why
review-code 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 19d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Code
Overview
Code review is the discipline of judging whether a change improves the health of the codebase — not whether it is perfect. An undirected reviewer does one of two failure modes: it rubber-stamps (misses real defects) or it nit-blocks (treats every preference as mandatory and never lets the change land). This skill prevents both by giving the review a governing principle, a fixed set of dimensions to inspect in value order, a parseable severity taxonomy, and a loop with an explicit stop condition.
This is the inline development-loop reviewer — it runs after you write a non-trivial change and before you commit. It is engine-agnostic: the reviewer can be a model (invoked via a role alias), a human, or a cloud tool. Because a model-backed reviewer has effectively unlimited throughput, it is the right engine for the inner loop — run it on every non-trivial change without spending a rate-limited cloud quota.
The cloud PR gate (CodeRabbit, via the rabbit-code-review skill) is a separate, later gate reserved for the pull request — do not spend it inside the inner loop. This skill covers everything up to the commit; the ship gate covers the PR.
Distilled from Google's Engineering Practices ("The Standard of Code Review", "What to look for"), the Conventional Comments spec, and the local severity→fix loop.
When to Use
- After writing a non-trivial change, before committing it (the inner loop)
- On an explicit request: "review this", "critique this diff", "is this change sound"
- Reviewing a subagent's or another author's diff before integrating it
- As a checkpoint in an implementation loop, once a task's code is written
When NOT to use:
- Shipping a PR — that is the cloud gate (
rabbit-code-review), run once, at the PR - A one-line or mechanical change (rename, import tweak) — review overhead > benefit
- In-flight, before the code exists — that is
doubt-driven-review(per-decision, not per-diff) - Diagnosing a failure — that is
systematic-debugging
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.
- 19d ago Changed · +27 lines 4dbba59187c7
- 1mo ago First seen · 137 lines · 96 tokens per session scan A 224c9245062f
review-code is a skill published in the GitHub repository BlackBeltTechnology/pi-agent-dashboard (325 stars, last pushed today), licensed MIT. It adds 96 tokens to every session and 2,768 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
goga-review-plan
Verify execution plan completeness and correctness.
goga-accept-manifest-review
Verify each Cell's CODEMANIFEST against the implementation.
goga-change-manifest-reconciler
Reconciliation of CODEMANIFEST specifications with implementation.
goga-change-validator
Final end-to-end validation of the completed change.
goga-accept-report
Generate the final acceptance report with verdict.
goga-change-drift-analyzer
Semantic drift detection after implementation.