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 skills/leonardodalinky/scider/content-refinement-agentnpx skills add leonardodalinky/SciDER --skill content-refinement-agentgit clone --depth 1 https://github.com/leonardodalinky/SciDERWrote 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/leonardodalinky/scider/content-refinement-agent)<a href="https://agentmods.dev/skills/leonardodalinky/scider/content-refinement-agent"><img src="https://agentmods.dev/badge/skills/leonardodalinky/scider/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 | $0.02518 |
| Opus 5 | $0.00052 | $0.01259 |
| Sonnet 5 | $0.00021 | $0.00504 |
| Haiku 4.5 | $0.00010 | $0.00252 |
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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- content-refinement-agent — 97% identical, 1 lines differ
How it starts
The opening of the file, as written. The whole thing — 257 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.
- 6d ago First seen · 257 lines · 104 tokens per session scan A 7bd827987dcb
content-refinement-agent is a skill published in the GitHub repository leonardodalinky/SciDER (88 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 104 tokens to every session and 2,518 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
drawio-reconstruction
Reconstructs reference images into high-fidelity, editable Draw.io files with rendered previews: native Draw.io elements carry text and structure, SVG covers simple icons that match the reference, and cropped or transparent PNGs preserve complex visuals. Use when the user wants a diagram image, research figure…
idea-evaluator
Evaluates a preliminary research idea against a five-dimension framework (Higher, Faster, Stronger, Cheaper, Broader) plus idea-lifecycle and student-capability matching, paradigm-shift probing, and a fatal-flaws audit. Returns a reviewer-style verdict; non-STEM ideas route to substitute frameworks. Use when the user…
paper-polish
Polishes existing academic prose while preserving the author's meaning: grammar and flow repair, tone calibration against evidence strength, AI-tone removal, and Chinese-to-English rewriting at submission quality. Never fabricates data, citations, or claims, and flags any edit that could change scientific meaning. Use…
paper-writer
Drafts publishable paper prose from the author's own materials, from a single paragraph to a full manuscript, across STEM and non-STEM fields. Every factual claim traces to user input, verified retrieval, or field common knowledge; citations pass an independent verification ladder; delivery is clean prose with zero…
pre-submission-reviewer
Runs a pre-submission review of a technical paper across five dimensions: macro logic, writing details, English grammar, LaTeX formatting, and figure quality. Uses a reviewer-style severity taxonomy (CRITICAL / MAJOR / MINOR) and flags banned AI-tone vocabulary and em-dash misuse. Use when the user asks to 'review…
benchmark-paper-template
Structures Benchmark and Evaluation papers using the five-pillar framework (Research Gap, Construction Pipeline, Evaluation Framework, Empirical Findings, optional Companion Method). Returns a completeness audit, a six-part Introduction logic chain, a Section 2-7 skeleton, and a pre-submission checklist. Use when…