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.
npx agentmods add commands/stellarshenson/claude-code-plugins/popular-sciencegit clone --depth 1 https://github.com/stellarshenson/claude-code-pluginsWrote 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/stellarshenson/claude-code-plugins/popular-science)<a href="https://agentmods.dev/commands/stellarshenson/claude-code-plugins/popular-science"><img src="https://agentmods.dev/badge/commands/stellarshenson/claude-code-plugins/popular-science.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 | $0.00048 | $0.00407 |
| Opus 5 | $0.00024 | $0.00204 |
| Sonnet 5 | $0.00010 | $0.00081 |
| Haiku 4.5 | $0.00005 | $0.00041 |
Grade A, and why
popular-science 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 4d 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
Popular science
Read the datascience:popular-science skill first - it is the single source of truth for the spine, the craft canon, the visual standard, the license-aware reference tool, and the workflow. Do NOT duplicate it here.
What to do
- Read the
datascience:popular-scienceskill and thereferences/craft-canon.mdit points to - Decide create vs update from the argument:
- Create - no target file yet: frame the single reader and the one takeaway, then follow the skill's workflow (frame → source → outline to the spine → draft → figures → self-critique → revise)
- Update - a target article exists: read it, apply the requested change (or, if none is named, run it through the
popular-scienceadversary and fix what the review surfaces), and re-confirm the spine end to end - hook lands, every claim carries source + number, the ending arcs back with conclusions + next steps
- Source every empirical claim via
datascience:papers; commission every figure viasvg-infographics:svg-designer; self-review viadevils-advocate:adversarial-reviewwith thepopular-scienceadversary - do not reinvent them here - No git commit / publish unless the user asks
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.
- 4d ago First seen · 19 lines · 48 tokens per session scan A 8d039b588f57
popular-science is a command published in the GitHub repository stellarshenson/claude-code-plugins (3 stars, last pushed 4d ago), licensed MIT. It adds 48 tokens to every session and 407 once invoked, about $0.0002 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 2026-08-31.
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