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 pengyizhou/claude-plugins --skill reviewing-my-papergit clone --depth 1 https://github.com/pengyizhou/claude-pluginsWrote 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/pengyizhou/claude-plugins/reviewing-my-paper)<a href="https://agentmods.dev/skills/pengyizhou/claude-plugins/reviewing-my-paper"><img src="https://agentmods.dev/badge/skills/pengyizhou/claude-plugins/reviewing-my-paper/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/pengyizhou/claude-plugins/reviewing-my-paper"><img src="https://agentmods.dev/badge/skills/pengyizhou/claude-plugins/reviewing-my-paper.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 | $0.00045 | $0.02814 |
| Opus 5.5 | $0.00018 | $0.01126 |
| Sonnet 5.5 | $0.00009 | $0.00563 |
| Haiku 4.5 | $0.00005 | $0.00281 |
Grade A, and why
reviewing-my-paper 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 2mo 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reviewing My Paper
Overview
A multi-lens review pipeline for the user's own academic paper that ends in an annotated PDF (highlights + sticky notes), not just prose feedback. It layers several independent lenses and — critically — adversarially verifies its own findings before presenting them, because a single review pass overstates claims and misses whole categories (citation existence, English discourse flow, figure–text consistency, copy-edit).
Core principle: every finding must be located (page + anchor) and verified before it reaches the PDF. Findings you can't substantiate get dropped or downgraded.
When to Use
- "Review / critique / give feedback on my paper", "mark up my PDF", "proofread my manuscript".
- The artifact is the user's own paper (be a constructive reviewer, not a gatekeeper).
- NOT for reviewing someone else's paper for a venue (use
academic-research-skills:academic-paper-reviewerdirectly) or writing a new paper (academic-research-skills:academic-paper).
Pipeline
Run these in order. Lenses 3–7 are independent → dispatch them as parallel subagents, each writing its results to a file so the annotation step can consume them.
| # | Phase | How |
|---|---|---|
| 0 | Extract text | PyMuPDF (see Environment). Save to paper_text.txt with page markers. Large PDFs: don't try to Read all pages. |
| 1 | Field analysis | Identify discipline, method type, and target venue to calibrate standards. |
| 2 | Multi-perspective review | Invoke academic-research-skills:academic-paper-reviewer (EIC + 3 reviewers + Devil's Advocate + synthesis). |
| 3 | Citation hallucination check | Fan out parallel agents over every reference (~8 each). Each verifies existence + metadata via WebSearch against DOI / ACL Anthology / OpenReview / proceedings — never from memory. Classify VERIFIED / SUSPECT / NOT_FOUND with the 5-type taxonomy (TF/PAC/IH/PH/SH). Mirrors academic-research-skills:academic-pipeline integrity_verification_agent. |
| 4 | Argument / logical flow | Motivation→method→claim chain, claim-evidence alignment, overclaims, non-sequiturs. |
| 5 | Academic-English discourse & language precision | Two levels, both REQUIRED. Discourse: general→specific, topic-sentence-first paragraphs, given-before-new cohesion, CARS intro moves, section-opener signposting, cross-section consistency. Language precision (the highest-value differentiator — baseline reviewers and the discourse pass both skip it): every coined term/jargon defined on first use; one concept = one name across title/abstract/body/index terms; no mechanism restated in vaguer terms or 3+ jargon stacks; flag synonym churn, key-term over-repetition, vague hedges, and forward-references. Run the Language & Terminology Audit below — do not stop at discourse-level flow. |
| 6 | Figure & table review | Render every figure to an image and look — text extraction (Lens 0) cannot see figures, so they get silently skipped. Three layers: (a) mismatch — every number/label/unit/class-name/symbol in a figure matches the text, tables, and body notation; (b) claim justification — does the figure actually support the claim it is cited for (a motivation figure should embody the method's core idea; a results figure should show the effect/trend/significance the text asserts; a framework figure should match the pipeline step-for-step)?; (c) design & improvement cues — standalone readability, clutter/redundancy, print/grayscale legibility, missing dispersion/CIs/significance. Give concrete improvement cues, not just diagnoses. See Figure & Table Review. |
| 7 | Grammar / copy-edit | Genuine prose errors only. Tell the agent to ignore PDF-extraction artifacts (mid-word hyphenation, mangled math/symbols, spaced numerals like "15 696"). |
| 8 | Adversarial self-verification | Before finalizing: re-check substantive claims, especially external facts (SOTA numbers, prior-art priority, "missing baseline X"), with refute-style subagents. Downgrade or drop anything you can't substantiate. Check disclosed caveats before calling a number an "inconsistency". |
| 9 | Annotate the PDF | Build a findings JSON, run annotate_pdf.py (see Tool). Color = severity. |
| 10 | Deliver | Send the annotated PDF + a short summary. Offer a consolidated P0/P1/P2 revision plan. |
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.
- 2mo ago First seen · 110 lines · 45 tokens per session scan A 237a817ae1f9
reviewing-my-paper is a skill published in the GitHub repository pengyizhou/claude-plugins (5 stars, last pushed 4mo ago), licensed MIT. It adds 45 tokens to every session and 2,814 once invoked, about $0.0002 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 2026-08-31.
reviewing-my-paper compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| reviewing-my-paperpengyizhou/claude-plugins | Skill | 5 | 4mo ago | MIT | A | 45 | Claude Code |
| apify-youtube-transcript-scrapersickn33/agentic-awesome-skills | Skill | 49k | yesterday | MIT | A | 48 | Claude Code |
| foundry-hosted-agent-validationmicrosoft/agent-framework | Skill | 15k | yesterday | MIT | A | 82 | Claude Code, Codex |
| report-to-pdfasgeirtj/system_prompts_leaks | Skill | 71k | yesterday | CC0-1.0 | A | 17 | Claude Code, Codex |
| pdf-extract-create-workflowHKUDS/OpenSpace | Skill | 7.9k | yesterday | MIT | A | 22 | Claude Code, Codex |
| document-direct-pythonHKUDS/OpenSpace | Skill | 7.9k | yesterday | MIT | A | 20 | Claude Code, Codex |
| pdf-verification-cliHKUDS/OpenSpace | Skill | 7.9k | yesterday | MIT | A | 19 | Claude Code, Codex |