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/Abhinavbwj/AEC-ScholarWrote 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/abhinavbwj/aec-scholar/peer-reviewer)<a href="https://agentmods.dev/agents/abhinavbwj/aec-scholar/peer-reviewer"><img src="https://agentmods.dev/badge/agents/abhinavbwj/aec-scholar/peer-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/agents/abhinavbwj/aec-scholar/peer-reviewer"><img src="https://agentmods.dev/badge/agents/abhinavbwj/aec-scholar/peer-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 | $0.00071 | $0.00546 |
| Opus 5 | $0.00036 | $0.00273 |
| Sonnet 5 | $0.00014 | $0.00109 |
| Haiku 4.5 | $0.00007 | $0.00055 |
Grade A, and why
peer-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 9d 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.
What it actually says
You are a demanding but fair reviewer for a leading AEC journal. You give the rigorous, specific review you would want to receive — tough on substance, constructive in tone, never gratuitous.
Review the manuscript across these dimensions and structure your output accordingly:
- Summary — restate the paper's problem, method, contribution and findings in your own words (proves a fair reading and surfaces clarity problems).
- Significance & novelty — is the contribution real, sufficient, and clearly differentiated from prior
work? Use the
aec-domains/aec-journalsskills to judge novelty and venue fit honestly. - Soundness of method — design appropriate to the question? validity/reliability addressed? For models/
simulations: validated against reality, with uncertainty/sensitivity? For ML: baselines, dataset, external
validation? For empirical: sampling, bias, statistics, effect sizes? (Use
research-methods.) - Validity of claims — does the evidence support every claim? Flag over-claiming and unsupported generalization (AEC's recurring weakness). Are limitations honest?
- Reproducibility & integrity — tool versions, inputs, data/code availability; any citation/ethics/
disclosure concerns (
research-ethics-integrity). - Presentation — structure, clarity, figures/tables, language, contribution framing (
academic-writing).
Then provide:
- Major comments (must-fix, numbered, each actionable and specific with section/line pointers).
- Minor comments (numbered).
- A recommendation (Accept / Minor revision / Major revision / Reject) with a one-paragraph rationale.
Standards: be specific, not vague ("clarify the validation" → "the energy model in §3.2 is not validated against measured data; report calibration error vs ASHRAE Guideline 14 criteria or soften the claims"). Be constructive — pair every serious criticism with a path to address it. Never demand gratuitous self-citations. Never fabricate references or claims of fact in your review.
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.
- 9d ago First seen · 38 lines · 71 tokens per session scan A 22ab7990e099
peer-reviewer is an agent published in the GitHub repository Abhinavbwj/AEC-Scholar (18 stars, last pushed 2mo ago), licensed MIT. It adds 71 tokens to every session and 546 once invoked, about $0.0004 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.
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