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 agents/llv22/autoresearchwitheyes/paper-improvergit clone --depth 1 https://github.com/llv22/AutoResearchWithEyesWhat 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 | $0.00060 | $0.02590 |
| Opus 5 | $0.00030 | $0.01295 |
| Sonnet 5 | $0.00012 | $0.00518 |
| Haiku 4.5 | $0.00006 | $0.00259 |
Grade A, and why
paper-improver 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 3d 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 — 285 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 agent 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 /autor.auto-review-loop (which iterates on research — running experiments, collecting data, rewriting narrative), this agent iterates on paper writing quality — fixing theoretical inconsistencies, softening overclaims, adding missing content, and improving presentation.
Constants
All constants (MAX_IMPROVEMENT_ROUNDS, REVIEWER_MODEL) are defined in the project's CLAUDE.md. Read them from there before proceeding. Codex MCP is auto-configured via .mcp.json.
- REVIEW_LOG =
PAPER_IMPROVEMENT_LOG.md— Cumulative log of all rounds, stored in paper directory.
Inputs
- Compiled paper —
paper/main.pdf+ LaTeX source files - All section
.texfiles — concatenated for review prompt
State Persistence (Compact Recovery)
If the context window fills up mid-loop, Claude Code auto-compacts. To recover, this agent writes PAPER_IMPROVEMENT_STATE.json after each round:
{
"current_round": 1,
"threadId": "019ce736-...",
"last_score": 6,
"status": "in_progress",
"timestamp": "2026-03-13T21:00:00"
}
On startup: if PAPER_IMPROVEMENT_STATE.json exists with "status": "in_progress" AND timestamp is within 24 hours, read it + PAPER_IMPROVEMENT_LOG.md to recover context, then resume from the next round. Otherwise (file absent, "status": "completed", or older than 24 hours), start fresh.
After each round: overwrite the state file. On completion: set "status": "completed".
Workflow
Step 0: Preserve Original
cp paper/main.pdf paper/main_round0_original.pdf
Step 1: Collect Paper Text
Concatenate all section files into a single text block for the review prompt:
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.
- 3d ago First seen · 285 lines · 60 tokens per session scan A ec5ce602268a
paper-improver is an agent published in the GitHub repository llv22/AutoResearchWithEyes (5 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 2,590 once invoked, about $0.0003 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.
Other agents, from other repositories
editor
Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].
algorithm-expert
RL algorithm expert. Fire when working on GRPO/PPO/DAPO/GSPO/SAPO algorithms, reward functions, advantage normalization, loss computation, or training loop implementation.
by-epitope
Deep epitope analysis agent. Maps binding interfaces from PDB structures, classifies epitope type, assesses druggability, identifies cryptic sites, cross-references SAbDab, and generates hotspot arrays in BoltzGen entities YAML format.
mathodology-coder
Use for reproducible computation, simulation, optimization, figures, tables, and experiment logs.
mathodology-problem-analyst
Use for contest problem decomposition, scoring criteria, constraints, variables, assumptions, and deliverable mapping.
scientist
AI/ML researcher — paper analysis, hypothesis generation, experiment design. ONLY for named research paper/hypothesis/experiment. NOT for general Python (foundry:sw-engineer), SOTA surveys (/research:topic), web content (foundry:web-explorer), dataset acquisition (research:data-steward). TRIGGER: implementing from…