scicomm-reviewer

scicomm-reviewer is an agent for Claude Code from dgilford/ai-science-toolkit. It costs 88 tokens per session (748 once invoked), scanned A, original, MIT.

A review guide for public-facing science communication, such as articles, press releases, social posts, and talk summaries. It examines audience fit, relevance, story, evidence, and clarity.

In plain words
What is it for?
Reviewing science articles, press releases, public summaries, talks, and social posts for audience focus, narrative, engagement, and cognitive load.
Why use it?
It helps turn technical findings into communication that a specific audience can understand and care about. It can reveal missing context, weak relevance, or an unclear message.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the ai-science-toolkit plugin — 21 skills, 4 agents shipped together

Good fit Reviewing science articles, press releases, public summaries, talks, and social posts for audience focus, narrative, engagement, and cognitive load.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/dgilford/ai-science-toolkit/scicomm-reviewer
Install

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.

Clone the repo
git clone --depth 1 https://github.com/dgilford/ai-science-toolkit

Made for: Claude Code.

Or install ai-science-toolkit, the plugin that ships this one along with the rest of its 21 skills, 4 agents.

Wrote 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.

agentmods badge for scicomm-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/agents/dgilford/ai-science-toolkit/scicomm-reviewer/github.svg)](https://agentmods.dev/agents/dgilford/ai-science-toolkit/scicomm-reviewer)
Your own site
<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.

agentmods 80×15 button for scicomm-reviewer

Your own site · 80×15
<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>
Per session 88 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 748 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 10d ago against content hash 1a21fa146e62, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

agents/scicomm-reviewer.md · 65 lines

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:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

Changes

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.

  1. 10d ago First seen · 65 lines · 88 tokens per session scan A 1a21fa146e62

Subscribe to this mod's changes

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.

Related

Other agents, from other repositories

reproducibility-auditor

Reviews research workflows for reproducibility gaps — hidden dependencies, absolute paths, undocumented prerequisites, environment assumptions, and output traceability. Use when checking whether a project can be rerun by someone else or handed off cleanly. Read-only with respect to project files; writes its own report…

flonat/flonat-research · 291 tokens

artifact-coherence-auditor

Audits coherence between paper prose and replication outputs — catches hallucinated results, missing scripts, mismatched numbers, and unverifiable claims. Read-only with respect to project files; writes its own report at reviews/ /artifact-coherence-auditor/ .md. Complements code-paper-auditor (which maps numbers to…

flonat/flonat-research · 296 tokens

researcher

Use this agent when the user wants to operate as a Researcher — organize research, manage references, notes, and collaboration. Services: drive, docs, sheets, gmail. Context: User wants to organize research materials user: "Create a research notes doc and log my experiment data in the tracking sheet" assistant: "I'll…

fakoli/fakoli-plugins · 128 tokens

geospatial-analyst

Geospatial analysis agent. Handles coordinate systems, spatial relationships, distance calculations, clustering, interpolation, and accessibility analysis. Use when data has a geographic or spatial dimension.

ChrisGVE/localdata-mcp · 39 tokens

research-analyst

Academic research analysis agent. Ensures methodological rigor, proper assumption documentation, power analysis, and reproducible reporting. Use when the analysis must meet peer-review or regulatory standards.

ChrisGVE/localdata-mcp · 39 tokens

statistical-analyst

Statistical analysis agent. Runs hypothesis tests, ANOVA, effect sizes, sampling design, bootstrap estimation, and non-parametric tests with plain-language interpretation. Use when rigorous statistical testing or estimation is needed.

ChrisGVE/localdata-mcp · 47 tokens