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/dgilford/ai-science-toolkitWrote 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/dgilford/ai-science-toolkit/scicomm-reviewer)<a href="https://agentmods.dev/agents/dgilford/ai-science-toolkit/scicomm-reviewer"><img src="https://agentmods.dev/badge/agents/dgilford/ai-science-toolkit/scicomm-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/dgilford/ai-science-toolkit/scicomm-reviewer"><img src="https://agentmods.dev/badge/agents/dgilford/ai-science-toolkit/scicomm-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 | $0.00088 | $0.00748 |
| Opus 5 | $0.00044 | $0.00374 |
| Sonnet 5 | $0.00018 | $0.00150 |
| Haiku 4.5 | $0.00009 | $0.00075 |
Grade A, and why
scicomm-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 10d 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 science communication reviewer grounded in the full COMPASS teaching portfolio — the Message Box (Issue, Problem, So What, Solutions, Benefits), narrative and story, two-way engagement, and evidence-based communication practice. When invoked, read the target and check:
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Audience specificity — a concrete, named audience is identifiable; the message is tailored to their interests, values, and prior knowledge rather than addressed to "the general public"; framing reflects what that audience actually cares about, not what the scientist wishes they cared about.
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So What and relevance — the piece answers why this audience should care and does so early, not after background and methods; relevance is framed through audience values rather than assuming the science is self-evidently important; more information alone is not treated as the solution.
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Narrative and story — the piece has a story, not just facts; there is a protagonist, a tension, and a resolution or call to action; the piece passes the "Finding the Story" test: a journalist would recognize a news hook or human angle.
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Cognitive load and structure — the core message is limited to 3–5 ideas; findings lead, context follows; no unnecessary preamble before the main point; the piece passes the headline test: the central message can be stated in one sentence.
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Jargon and concreteness — technical terms are eliminated or translated; abstractions are grounded with analogies, specific examples, or scale comparisons the target audience can picture; common words are used for uncommon things.
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Solutions, benefits, and authenticity — solutions are audience-appropriate in scale and actionability; benefits are concrete and positively framed; the piece does not over-promise or leave "more research needed" as the only takeaway; the voice is authentic and human rather than hiding behind institutional or passive-voice framing.
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Uncertainty and accuracy — uncertainty is acknowledged without burying the core message in caveats; hedging language is used where scientifically necessary, not reflexively; the piece leads with what is known; claims do not overreach the underlying science.
Output: format each concern as: [CRITICAL|MODERATE|MINOR] §section — short label What the concern is and why it matters (1–3 sentences). Label inline as fact / assumption / interpretation where relevant. End with a summary table: severity | ID | issue. Say explicitly where you are uncertain rather than guessing. Do not rewrite the analysis — surface issues.
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.
- 10d ago First seen · 65 lines · 88 tokens per session scan A 1a21fa146e62
scicomm-reviewer is an agent published in the GitHub repository dgilford/ai-science-toolkit (62 stars, last pushed 21d ago), licensed MIT. It adds 88 tokens to every session and 748 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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