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/commands/abhinavbwj/aec-scholar/peer-review)<a href="https://agentmods.dev/commands/abhinavbwj/aec-scholar/peer-review"><img src="https://agentmods.dev/badge/commands/abhinavbwj/aec-scholar/peer-review/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/commands/abhinavbwj/aec-scholar/peer-review"><img src="https://agentmods.dev/badge/commands/abhinavbwj/aec-scholar/peer-review.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 | $0.00013 | $0.00425 |
| Opus 5 | $0.00006 | $0.00212 |
| Sonnet 5 | $0.00003 | $0.00085 |
| Haiku 4.5 | $0.00001 | $0.00042 |
Grade A, and why
peer-review 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
Run a pre-submission peer review of: $ARGUMENTS
Delegate to the peer-reviewer agent. Read the manuscript if a path is given.
Produce a full referee report structured as:
- Summary of the manuscript — problem, method, contribution, findings in your own words (demonstrates a fair reading and exposes clarity issues).
- Significance & novelty — is the contribution real, sufficient and well-differentiated? Is the venue a
fit (use
aec-journalsif a target is named)? - Soundness of method — appropriate design? validity/reliability? For models/simulations: validated vs
measured data, with uncertainty/sensitivity? For ML: baselines, dataset, external validation? For
empirical: sampling, bias, statistics, effect sizes? (
research-methods). - Validity of claims — evidence supports every claim? Flag over-claiming and over-generalization; are limitations honest?
- Reproducibility & integrity — tool versions, inputs, data/code availability; citation/ethics/
disclosure concerns (
research-ethics-integrity). - Presentation — structure, clarity, figures/tables, contribution framing.
Then:
- Major comments (numbered, specific, actionable, with section pointers and a path to fix each).
- Minor comments (numbered).
- Recommendation — Accept / Minor / Major / Reject, with rationale.
Be tough on substance and constructive in tone. Be specific, not vague. Never demand gratuitous self-citations or fabricate references. The goal is to find the problems a real reviewer would, so the author can fix them first. Offer to help address the major comments afterward.
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 · 34 lines · 13 tokens per session scan A da7120e179d1
peer-review is a command published in the GitHub repository Abhinavbwj/AEC-Scholar (18 stars, last pushed 2mo ago), licensed MIT. It adds 13 tokens to every session and 425 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.