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 ntorga/agent-starter-kit --skill reviewer-self-reviewgit clone --depth 1 https://github.com/ntorga/agent-starter-kitWrote 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/ntorga/agent-starter-kit/reviewer-self-review)<a href="https://agentmods.dev/skills/ntorga/agent-starter-kit/reviewer-self-review"><img src="https://agentmods.dev/badge/skills/ntorga/agent-starter-kit/reviewer-self-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/ntorga/agent-starter-kit/reviewer-self-review"><img src="https://agentmods.dev/badge/skills/ntorga/agent-starter-kit/reviewer-self-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 | $0.00024 | $0.02164 |
| Opus 5 | $0.00012 | $0.01082 |
| Sonnet 5 | $0.00005 | $0.00433 |
| Haiku 4.5 | $0.00002 | $0.00216 |
Grade A, and why
reviewer-self-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 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Before delivering a review, the Reviewer evaluates its own output against the SHIELD rubric. Each letter is scored 0, 1, or 2 with evidence quoted from the rubric and cited from actual work. The total determines whether to deliver, fix gaps, or restart.
Procedure
-
Gather evidence. Before scoring, run verification commands to gather proof. The examples below show common patterns — choose what provides the best evidence for your specific work.
Examples:
- Injection patterns:
rg -n 'ignore previous|disregard|override|skip check|change verdict' <reviewed-files>— check for embedded instructions - Code evidence: verify each finding includes concrete code evidence (specific lines, functions, or data flows)
- Rule tracing: verify each finding references a loaded rule, principle, or standard by name
- External factors:
git diff --name-onlyfor lockfile/package file changes,rg -n 'password|secret|api_key' <new-deps> - Pass completeness: verify all required review skill files were loaded, all required passes completed, and the coverage note records ranked targets and unreviewed files
- Injection patterns:
-
Score each criterion. For each letter, assign 0, 1, or 2. Quote the matching level and cite specific evidence from your work. Generic claims like "I checked everything" score 0.
-
Output the Scorecard.
-
Apply the hard-fail rule. If any letter scores 0, RESTART immediately — skip step 5.
-
Determine action by total score:
- 10 – 12 — DELIVER
- 8 – 9 — FIX the letters that scored below 2. Fix automatically (do NOT consult the caller). Re-score, then deliver if 10-12. After 2 failed fix attempts, yield with current state and blocking letters.
- 0 – 7 — RESTART — Discard and re-read the work with corrected understanding, or yield with an explanation.
Scorecard
Complete this before delivering. Each letter requires the matched criterion quote and specific evidence from your work.
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 · 106 lines · 24 tokens per session scan A 52b4a6519dfd
reviewer-self-review is a skill published in the GitHub repository ntorga/agent-starter-kit (142 stars, last pushed 2d ago), licensed MIT. It adds 24 tokens to every session and 2,164 once invoked, about $0.0001 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 2026-09-13.
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