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/plain-summary)<a href="https://agentmods.dev/commands/abhinavbwj/aec-scholar/plain-summary"><img src="https://agentmods.dev/badge/commands/abhinavbwj/aec-scholar/plain-summary.svg" alt="Measured on agentmods" 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 | $0.00015 | $0.00343 |
| Opus 5 | $0.00008 | $0.00171 |
| Sonnet 5 | $0.00003 | $0.00069 |
| Haiku 4.5 | $0.00002 | $0.00034 |
Grade A, and why
plain-summary 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.
What it actually says
Write a plain-language summary of: $ARGUMENTS
Use the academic-writer agent and academic-writing.
-
Identify the audience (
--audience, default: general public). Calibrate vocabulary and emphasis:- Public — why it matters in everyday terms, no jargon.
- Policy — implications for regulation, standards, decarbonization/safety/housing targets.
- Industry — practical application, adoption, ROI/benefit on real projects.
- Funder — significance, novelty and impact pathways.
-
Write the summary (typically 100–250 words; honor any limit the user gives):
- Open with the real-world problem and why it matters.
- Explain what the research did and found, without jargon or acronyms (translate "embodied carbon", "digital twin", "BIM" into plain terms; define if unavoidable).
- End with the implication / "so what" for that audience.
- Use concrete, honest framing — accurate to the actual findings, no hype or over-generalization.
-
Offer variants — a one-sentence version (for a tweet/abstract highlight), and a 3-bullet "key messages" version.
Keep it truthful: a lay summary simplifies, it does not exaggerate. Don't claim impact the study doesn't support.
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 First seen · 29 lines · 15 tokens per session scan A 987f6ba3226a
plain-summary is a command published in the GitHub repository Abhinavbwj/AEC-Scholar (18 stars, last pushed 2mo ago), licensed MIT. It adds 15 tokens to every session and 343 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
verify-math
Verify a self-authored mathematical result end to end by routing claims across adversarial review, numerical falsification, symbolic or CAS checks, and Lean, then aggregating one report. Use when a theorem, proposition, conjecture, or paper-wide mathematical argument needs the appropriate combination of verification…
replication-package
Scaffold or audit a social-science replication package at a target directory, and audit the manuscript and its archived research objects against FAIR principles.
diff
Quantitative volume comparison between a CadQuery model and a reference STEP file.
simulation-calibrator
Test and refine simulation accuracy with validation loops, bias detection, and continuous improvement frameworks.
arg-diagram
ARG academic-paper diagram mode — standalone structural & conceptual diagram generation.
graphite-morphology-classify
Classify graphite in a cast-iron micrograph per ASTM A247 / ISO 945-1, quantify nodularity, and read the matrix — the single most diagnostic observation in a cast-iron case.