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/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/auto-review-loopWrote 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/raja21068/autoresearch/auto-review-loop)<a href="https://agentmods.dev/skills/raja21068/autoresearch/auto-review-loop"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/auto-review-loop/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/raja21068/autoresearch/auto-review-loop"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/auto-review-loop.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 | $0.00057 | $0.05042 |
| Opus 5 | $0.00028 | $0.02521 |
| Sonnet 5 | $0.00011 | $0.01008 |
| Haiku 4.5 | $0.00006 | $0.00504 |
Grade A, and why
auto-review-loop 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 5d 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.
- **Anti-hallucination citations**: When adding references during fixes, NEVER fabricate BibTeX. Use the same DBLP → CrossRef → `[VERIFY]` chain as `/paper-write`: (1) `curl -s "https://dblp.org/search/publ/api?q=TITLE&f How it starts
The opening of the file, as written. The whole thing — 462 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Review Loop: Autonomous Research Improvement
Autonomously iterate: review → implement fixes → re-review, until the external reviewer gives a positive assessment or MAX_ROUNDS is reached.
Context: $ARGUMENTS
Constants
- MAX_ROUNDS = 4
- POSITIVE_THRESHOLD: score >= 6/10, or verdict contains "accept", "sufficient", "ready for submission"
- REVIEW_DOC:
review-stage/AUTO_REVIEW.md(cumulative log) (fall back to./AUTO_REVIEW.mdfor legacy projects) - REVIEWER_MODEL =
gpt-5.4— Model used via Codex MCP. Must be an OpenAI model (e.g.,gpt-5.4,o3,gpt-4o) - REVIEWER_BACKEND =
codex— Default: Codex MCP (xhigh). Override with— reviewer: oracle-profor GPT-5.4 Pro via Oracle MCP. Seeshared-references/reviewer-routing.md. - OUTPUT_DIR =
review-stage/— All review-stage outputs go here. Create the directory if it doesn't exist. - HUMAN_CHECKPOINT = false — When
true, pause after each round's review (Phase B) and present the score + weaknesses to the user. Wait for user input before proceeding to Phase C. The user can: approve the suggested fixes, provide custom modification instructions, skip specific fixes, or stop the loop early. Whenfalse(default), the loop runs fully autonomously. - COMPACT = false — When
true, (1) readEXPERIMENT_LOG.mdandfindings.mdinstead of parsing full logs on session recovery, (2) append key findings tofindings.mdafter each round. - REVIEWER_DIFFICULTY = medium — Controls how adversarial the reviewer is. Three levels:
medium(default): Current behavior — MCP-based review, Claude controls what context GPT sees.hard: Adds Reviewer Memory (GPT tracks its own suspicions across rounds) + Debate Protocol (Claude can rebut, GPT rules).nightmare: Everything inhard+ GPT reads the repo directly viacodex exec(Claude cannot filter what GPT sees) + Adversarial Verification (GPT independently checks if code matches claims).
💡 Override:
/auto-review-loop "topic" — compact: true, human checkpoint: true, difficulty: hard
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.
- 5d ago First seen · 462 lines · 57 tokens per session scan A 24beb955482c
auto-review-loop is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 57 tokens to every session and 5,042 once invoked, about $0.0003 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 2026-09-03.
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