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-paper-improvement-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-paper-improvement-loop)<a href="https://agentmods.dev/skills/raja21068/autoresearch/auto-paper-improvement-loop"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/auto-paper-improvement-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-paper-improvement-loop"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/auto-paper-improvement-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 | $0.00067 | $0.08805 |
| Opus 5 | $0.00034 | $0.04403 |
| Sonnet 5 | $0.00013 | $0.01761 |
| Haiku 4.5 | $0.00007 | $0.00881 |
Grade A, and why
auto-paper-improvement-loop 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 6d 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.
This is a copy
91% identical to auto-paper-improvement-loop — 115 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 625 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Paper Improvement Loop: Review → Fix → Recompile
Autonomously improve the paper at: $ARGUMENTS
Context
This skill is designed to run after Workflow 3 (/paper-plan → /paper-figure → /paper-write → /paper-compile). It takes a compiled paper and iteratively improves it through external LLM review.
Unlike /auto-review-loop (which iterates on research — running experiments, collecting data, rewriting narrative), this skill iterates on paper writing quality — fixing theoretical inconsistencies, softening overclaims, adding missing content, and improving presentation.
Constants
- MAX_ROUNDS = 2 — Two rounds of review→fix→recompile. Empirically, Round 1 catches structural issues (4→6/10), Round 2 catches remaining presentation issues (6→7/10). Diminishing returns beyond 2 rounds for writing-only improvements.
- REVIEWER_MODEL =
gpt-5.4— Model used via Codex MCP for paper review. - REVIEWER_BIAS_GUARD = true — When
true, every review round uses a freshmcp__codex__codexthread with no prior review context. Never usemcp__codex__codex-replyfor review rounds. Set tofalseonly for deliberate debugging of the legacy behavior. Empirical evidence: running the same paper withcodex-reply+ "since last round we did X" prompts inflated scores from real 3/10 → fake 8/10 across multiple rounds; switching to fresh threads recovered the true 3/10 assessment. - REVIEW_LOG =
PAPER_IMPROVEMENT_LOG.md— Cumulative log of all rounds, stored in paper directory. - HUMAN_CHECKPOINT = false — When
true, pause after each round's review and present score + weaknesses to the user. The user can approve fixes, provide custom modification instructions, skip specific fixes, or stop early. Whenfalse(default), runs fully autonomously. - EDIT_WHITELIST =
null— Optional path to a YAML/JSON whitelist file constraining which paths and operations the fix-implementation step may touch. Whennull(default), all edits proceed unconstrained. When set via— edit-whitelist <path>(also accepts— edit_whitelist <path>), the loop loads the file at startup and consults it before each edit; rejected edits are logged toPAPER_IMPROVEMENT_LOG.mdrather than silently dropped. See "Optional: Edit Whitelist" below.
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.
- 6d ago First seen · 625 lines · 67 tokens per session scan A a0cf1672a579
auto-paper-improvement-loop is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 67 tokens to every session and 8,805 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to auto-paper-improvement-loop, differing in 115 lines, and is treated as a copy.
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