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 raja21068/AutoResearch --skill content-refinement-agentgit clone --depth 1 https://github.com/raja21068/AutoResearchWrote 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/content-refinement-agent)<a href="https://agentmods.dev/skills/raja21068/autoresearch/content-refinement-agent"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/content-refinement-agent.svg" alt="Measured on agentmods" 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 | $0.00104 | $0.02512 |
| Opus 5 | $0.00052 | $0.01256 |
| Sonnet 5 | $0.00021 | $0.00502 |
| Haiku 4.5 | $0.00010 | $0.00251 |
Grade A, and why
content-refinement-agent 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.
This is a copy
97% identical to content-refinement-agent — 1 line 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Refinement Agent (Step 5)
Faithful implementation of the Content Refinement Agent from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, §4 Step 5, App. F.1 pp. 49–51).
Cost: ~5–7 LLM calls (App. B), typically ~3 refinement iterations, each consisting of one reviewer call and one revision call.
The paper highlights this step as one of the largest contributors to overall quality: refinement alone accounts for +19% (CVPR) and +22% (ICLR) absolute acceptance-rate improvement (Fig. 4). Get this step right.
Inputs
workspace/drafts/paper.tex— output of Step 4workspace/inputs/conference_guidelines.mdworkspace/inputs/experimental_log.md— used as ground truth for the hallucination checkworkspace/citation_pool.json/workspace/refs.bib— the allowed bibliography
Outputs
workspace/refinement/iter1/,iter2/,iter3/— per-iteration snapshots containingpaper.tex,paper.pdf,review.json,score.jsonworkspace/refinement/worklog.json— append-only history of decisionsworkspace/final/paper.texandworkspace/final/paper.pdf— copy of the best accepted snapshot
The refinement loop
prev_score = score(paper.tex) # baseline from initial draft
snapshot iter0/
for iter in 1..ITER_CAP (default 3):
1. simulate_review(paper.tex) → review.json
(uses `references/reviewer-rubric.md` rubric)
2. apply_revision(paper.tex, review.json) → new_paper.tex
(uses verbatim Refinement Agent prompt at `references/prompt.md`)
3. snapshot iter<N>/ with new_paper.tex, review.json
latexmk -pdf new_paper.tex → iter<N>/paper.pdf
4. score(new_paper.tex) → curr_score
5. decide via score_delta.py:
- if curr.overall > prev.overall: ACCEPT
- elif curr.overall == prev.overall and net_subaxis ≥0: ACCEPT
- else: REVERT
6. apply_worklog.py to append the decision
7. if REVERT or no actionable weaknesses or iter == ITER_CAP: HALT
paper.tex ← new_paper.tex (only on ACCEPT)
prev_score ← curr_score
cp <best iter>/paper.tex → workspace/final/paper.tex
What ships with it
7 files 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.
- 3d ago First seen · 256 lines · 104 tokens per session scan A 53a45f94ec05
content-refinement-agent is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 104 tokens to every session and 2,512 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to content-refinement-agent, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…
paper-autoraters
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the…
paper-writing-bench
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a…