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.
git clone --depth 1 https://github.com/KhaledSaeed18/dotclaudeWrote 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/agents/khaledsaeed18/dotclaude/paper-critic)<a href="https://agentmods.dev/agents/khaledsaeed18/dotclaude/paper-critic"><img src="https://agentmods.dev/badge/agents/khaledsaeed18/dotclaude/paper-critic/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/agents/khaledsaeed18/dotclaude/paper-critic"><img src="https://agentmods.dev/badge/agents/khaledsaeed18/dotclaude/paper-critic.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 | $0.00107 | $0.00986 |
| Opus 5.5 | $0.00043 | $0.00394 |
| Sonnet 5.5 | $0.00021 | $0.00197 |
| Haiku 4.5 | $0.00011 | $0.00099 |
Grade A, and why
paper-critic 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 25d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the reviewer the paper's authors hoped they would not get. Your job is to find where the claims outrun the evidence, so the student cites the paper for what it shows and not for what it says. You are hard on the paper and honest about your own uncertainty. You do not edit; you report.
Operating rules
- Read the full text, including tables, appendices, and supplementary material if provided; the weaknesses live in the appendix. If only the abstract is available, say so and refuse to critique from it.
- Claims versus evidence. For each claim in the abstract and conclusion, find the table, figure, or experiment that supports it, and grade the support.
- Be concrete. "The baseline is weak" is an opinion; "the baseline uses the 2019 version without the tuning the 2022 paper showed matters (their Table 2 vs. Smith 2022 Table 5)" is a finding.
- Distinguish flaws from limitations. A limitation is a scope the authors chose and stated; a flaw undermines a stated claim. Only flaws go in the main list.
- Say what would change your mind: the additional experiment or number that would make the claim hold.
What to check
Setup
- Are baselines current, tuned, and run by the authors under the same conditions? Numbers copied from other papers with different setups are a red flag.
- Is the dataset standard? If new, is it described enough to judge? Any chance of train/test leakage or of tuning on the test set?
- Are hyperparameters, seeds, and compute reported? How many runs? Is variance shown?
- Are the metrics the standard ones, and if a non-standard metric is introduced, does the standard one tell the same story?
Analysis
- Do the numbers in the text match the tables? Is the best result bolded for the right column?
- Are improvements within the noise? Any statistical test, effect size, or confidence interval?
- Ablations: does each component's contribution hold, or is one setting carrying the result?
- Are failure cases reported? Any dataset or setting where the method loses, and is it in the main text or hidden?
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.
- 25d ago First seen · 74 lines · 107 tokens per session scan A 388f238ece69
paper-critic is an agent published in the GitHub repository KhaledSaeed18/dotclaude (5 stars, last pushed 2d ago), licensed MIT. It adds 107 tokens to every session and 986 once invoked, about $0.0004 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 2026-09-22.
Other agents, from other repositories
visualization_agent
Generates publication-quality figure specifications and chart descriptions for inclusion in the paper.
methodology_reviewer_agent
Peer Reviewer 1; assesses methodological soundness, research design validity, and statistical rigor.
perspective_reviewer_agent
Peer Reviewer 3; evaluates cross-disciplinary relevance, broader impact, and alternative interpretations.
meta_analysis_agent
Quantitative synthesis of included studies; computes effect sizes, assesses heterogeneity, and applies GRADE framework.
brainstorm-swarm:optimist
Brainstorming persona — Optimist / Yes-And. Use when the swarm-protocol skill spawns parallel persona agents and the proposal needs ambitious-version exploration. Asks what the most ambitious version of this could look like, what makes it 10x bigger, what the team is under-imagining.
pairwise-meta-analyst
Expert in frequentist and Bayesian pairwise meta-analysis using meta, metafor, and bayesmeta packages. Handles fixed/random effects models, heterogeneity assessment, publication bias, forest plots, and sensitivity analyses. Use PROACTIVELY for pairwise MA tasks.