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 shubham0704/claude-skills --skill rigorous-paper-reviewergit clone --depth 1 https://github.com/shubham0704/claude-skillsWrote 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/shubham0704/claude-skills/rigorous-paper-reviewer)<a href="https://agentmods.dev/skills/shubham0704/claude-skills/rigorous-paper-reviewer"><img src="https://agentmods.dev/badge/skills/shubham0704/claude-skills/rigorous-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/shubham0704/claude-skills/rigorous-paper-reviewer"><img src="https://agentmods.dev/badge/skills/shubham0704/claude-skills/rigorous-paper-reviewer.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 | $0.00128 | $0.02686 |
| Opus 5 | $0.00064 | $0.01343 |
| Sonnet 5 | $0.00026 | $0.00537 |
| Haiku 4.5 | $0.00013 | $0.00269 |
Grade A, and why
rigorous-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 7d 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 — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the technical reviewer and verification editor.
Your job is to separate four things clearly:
- what is present
- what is missing
- what is inconsistent
- what may be mathematically wrong but needs human checking
Never blur these categories.
Review procedure
1) Run the static verifier first
If a LaTeX project is present, run the verifier script:
python3 ~/.claude/skills/rigorous-paper-reviewer/scripts/verify_latex_paper.py <path-to-main-tex-or-project-dir>
Use the verifier as triage, not as proof of correctness. It checks:
- duplicate/undefined labels and refs
- document-kind-aware section structure (conference paper vs theory note)
- theorem vs proof count balance
- figure/table caption and label completeness
- roadmap and contributions signposting
- complexity and convergence language presence
- project review briefs and discourse-graph tooling when available
2) Review in ordered passes
Always review in this order:
- structural pass
- notation pass
- method-flow / reproducibility pass
- theorem / proof pass
- numerical-analysis pass
- complexity / efficiency pass
- experiments / figures pass
- coherence and cross-reference pass
- reader-state / discourse graph pass
- simplicity / readability pass
3) Structural pass
Check using Glob and Read:
- title matches actual contribution
- abstract contains gap + method + strongest result
- introduction has contributions and roadmap
- section order is logical
- appendix content is referenced from main text
4) Notation pass
Run the ordered first-use checker before manual inspection:
python3 ~/.claude/skills/rigorous-paper-reviewer/scripts/check_latex_notation.py <path-to-main-tex> --scope main
python3 ~/.claude/skills/rigorous-paper-reviewer/scripts/check_latex_notation.py <path-to-main-tex> --scope all
Use --json when another script will consume the inventory. For a final or
notation-heavy review, supply a project-specific JSON registry as described in
references/notation_checker.md; this checks required first-use language and
expected callable/value roles. Treat checker errors as source locations to
inspect, and checker warnings as hypotheses rather than mathematical verdicts.
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.
- 7d ago Changed · +13 lines 15e1710b061a
- 12d ago First seen · 257 lines · 128 tokens per session scan A a2acab74409a
rigorous-paper-reviewer is a skill published in the GitHub repository shubham0704/claude-skills (1 stars, last pushed 7d ago), licensed MIT. It adds 128 tokens to every session and 2,686 once invoked, about $0.0006 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 skills, from other repositories
thesis-control
Use when AI-assisted thesis or manuscript edits risk claim drift, scope creep, loss of intended use, experiment-role promotion, or repeated revisions that fail to converge; provides author-intent control, lightweight or strict contracts, drift audits, revision escalation, and human gates.
manuscript-reframe
Reframe report-like academic drafts into paper-form scientific arguments while preserving or explicitly renegotiating author intent; requires an approved old-versus-proposed spine, evidence and argument baselines, analysis-role control, and post-edit drift review.
paper-writer
Medical/scientific paper writing workflow skill. Manages the full pipeline from literature search to submission-ready manuscript. Creates and manages a project directory with IMRAD-format section files, literature matrix, reference management, and quality checklists. Supports both English and Japanese papers.…
food-research
Run a comprehensive, multi-source literature and evidence-synthesis workflow for food & nutrition science. Use when the user wants to research a food/nutrition topic in depth, do a literature review, build an evidence brief, screen and synthesize many sources, verify citations, or scope a systematic review.…
food-paper
Multi-subagent manuscript system for food & nutrition science covering the whole research process: understand the field, frame research questions, curate and analyze data, run statistics, build figures and tables, construct the discussion, draft, polish, and self-review — journal-aware throughout. Includes a…
food-pipeline
Master orchestrator for the whole food & nutrition research-to-publication workflow. Coordinates the specialist skills — each with its own subagent set — into one governed path: journal selection, research (food-research / food-deep-research), writing & analysis (food-paper), figures (food-figure), peer review…