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/bibliometric)<a href="https://agentmods.dev/commands/abhinavbwj/aec-scholar/bibliometric"><img src="https://agentmods.dev/badge/commands/abhinavbwj/aec-scholar/bibliometric.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 | $0.00018 | $0.00526 |
| Opus 5 | $0.00009 | $0.00263 |
| Sonnet 5 | $0.00004 | $0.00105 |
| Haiku 4.5 | $0.00002 | $0.00053 |
Grade A, and why
bibliometric 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 8d 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 the bibliometric analysis workflow for: $ARGUMENTS
Engage the bibliometric-analysis skill (and aec-journals/aec-domains for grounding). Delegate
interpretation to the aec-domain-expert agent where domain validation is needed.
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Frame the question. Is the goal performance analysis (productivity/impact), science mapping (structure/evolution), or both? State what the user wants to learn (foundations? fronts? trends? collaboration gaps?).
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Data acquisition protocol. Recommend ONE primary database for the structured analysis (Scopus or Web of Science — explain why they shouldn't be merged for citation data) and an exact query + filters + export format (full records with cited references). Tell the user to record the export date and record count.
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Cleaning plan. Specify the data-cleaning steps that make or break credibility: author/affiliation disambiguation and a keyword thesaurus (synonym merges — pull AEC variants from
aec-domains). -
Analysis design. Choose the analyses to the question: co-citation (intellectual base), bibliographic coupling (research fronts), keyword co-occurrence (themes/trends), co-authorship (collaboration), and thematic evolution/burst detection. Map each to the tool (VOSviewer / CiteSpace / Bibliometrix-biblioshiny) and give concrete steps and key parameters (min. occurrence thresholds, clustering resolution, counting method) — including reproducible R code for Bibliometrix when requested.
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Reporting & interpretation. Specify exactly what to report for reproducibility (query, filters, dates, software+version, thresholds, cleaning) and how to turn each network into a written finding (name and explain clusters, identify fragmentation gaps, surface emerging themes, propose a research agenda). Validate clusters against domain knowledge — a cluster the field wouldn't recognize is a cleaning artifact.
If the user provides an exported dataset, analyze it directly (or give precise tool steps) and draft the interpreted results section.
Integrity: never invent counts, clusters or metrics. Bibliometrics is only as good as the documented export and cleaning — insist on transparency.
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.
- 8d ago First seen · 39 lines · 18 tokens per session scan A 6cbe8fb2a704
bibliometric is a command published in the GitHub repository Abhinavbwj/AEC-Scholar (18 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 526 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.
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