Borrowing it
Nothing to install: this file belongs to Ingar30/reviewer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Ingar30/reviewer/main/.agents/skills/paper-reviewer/SKILL.mdgit clone --depth 1 https://github.com/Ingar30/reviewerWrote 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/ingar30/reviewer/paper-reviewer)<a href="https://agentmods.dev/skills/ingar30/reviewer/paper-reviewer"><img src="https://agentmods.dev/badge/skills/ingar30/reviewer/paper-reviewer/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/ingar30/reviewer/paper-reviewer"><img src="https://agentmods.dev/badge/skills/ingar30/reviewer/paper-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.01582 |
| Opus 5.5 | $0.00013 | $0.00633 |
| Sonnet 5.5 | $0.00007 | $0.00316 |
| Haiku 4.5 | $0.00003 | $0.00158 |
Grade A, and why
paper-reviewer 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 14d 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:
- broken-tab — 88% identical, 87 lines differ
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Reviewer Skill
This skill runs a reproducible multi-agent paper-review workflow for academic PDFs.
Use this skill when
- the input is an academic paper PDF
- the task is review, auditing, or verification
- the user wants a structured report
- the task involves literature claims, references, numeric checks, or internal cross-references
Do not use this skill when
- the user wants only a summary
- the user wants only proofreading
- the user wants only a rewrite
- parsed artifacts already exist and the request is unrelated to the review pipeline
Default input convention
- If the user names a bare PDF filename, first look for it under
inputs/. - If the user gives a repo-relative path, use it.
- If the user gives an absolute path, use it as provided.
Workflow
For fresh runs, use scripts/review_paper.py as the primary entry point.
The pipeline stages are:
- Resolve the input PDF path.
- Derive
paper_idfrom the filename stem unless explicitly provided. - Preprocess the PDF into
work/<paper_id>/parsed/. - Render run-specific prompts into
work/<paper_id>/prompts/. - Launch preflight reviewers from
config/reviewers.json. - Validate preflight JSON and stop on blocking parser-quality failures.
- Route substantive reviewers around parser-quality warnings using the deterministic artifacts and parser-quality JSON.
- Run the conservative applicability router and record its complete decision in
work/<paper_id>/selection/reviewer_selection.json. Mixed, unknown, or lower-confidence classifications automatically expand to every conditional specialist. - Write the active run roster and selection provenance to
work/<paper_id>/selection/selected_reviewers.json. - Rerender prompts using the selected reviewer roster and parser-quality guidance.
- Launch the 8 universal review-stage reviewers and every applicable conditional specialist. The full roster contains 19 substantive reviewers.
- Validate each reviewer JSON output under
work/<paper_id>/reviews/. - Conservatively normalize reviewer outputs into a precision-first, lossless
work/<paper_id>/editor/normalized_bundle.json. Preserve every source finding's details and do not merge findings merely because they share a quote or path. - Build
work/<paper_id>/editor/editor_input.mdfrom the deterministic editor brief, the lossless bundle, and a compact provenance index. Validate the source reviewer JSON files, but do not duplicate them in the editor input or truncate evidence. - Run the editor to author
work/<paper_id>/editor/editor_decisions.json; validate dispositions and deterministically render its prose tooutputs/<paper_id>/report.md. - Smoke-check the final report with
scripts/check_final_report.py --bundle work/<paper_id>/editor/normalized_bundle.json.
What ships with it
1 file 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.
- 14d ago Changed e14327743d1a
- 1mo ago Changed e14a7fbea606
- 2mo ago Changed · -1 lines 5f8bcd013f0e
- 2mo ago First seen · 87 lines · 33 tokens per session scan A 33883e939f32
paper-reviewer is a skill published in the GitHub repository Ingar30/reviewer (230 stars, last pushed 8d ago), licensed MIT. It adds 33 tokens to every session and 1,582 once invoked, about $0.0001 per session on Opus 5.5. 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.
paper-reviewer compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| paper-reviewerIngar30/reviewer | Skill | 230 | 8d ago | MIT | A | 33 | Codex |
| compile-latexpedrohcgs/claude-code-my-workflow | Skill | 1.7k | 2d ago | MIT | A | 73 | Claude Code |
| overleafaloth/olcli | Skill | 243 | 4d ago | MIT | A | 62 | Claude Code, Codex |
| alterlab-markitdownAlterLab-IEU/AlterLab-Academic-Skills | Skill | 85 | yesterday | MIT | A | 204 | Claude Code |
| alterlab-open-notebookAlterLab-IEU/AlterLab-Academic-Skills | Skill | 85 | yesterday | MIT | A | 196 | Claude Code |
| alterlab-treatment-plansAlterLab-IEU/AlterLab-Academic-Skills | Skill | 85 | yesterday | MIT | A | 107 | Claude Code |
| latex-documentbrycewang-stanford/Auto-Empirical-Research-Skills | Skill | 4.9k | 4d ago | none | A | 950 | Claude Code, Codex |