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 skills add ur-grue/autopunk-media-skills --skill data-footnote-writergit clone --depth 1 https://github.com/ur-grue/autopunk-media-skillsWrote 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/skills/ur-grue/autopunk-media-skills/data-footnote-writer)<a href="https://agentmods.dev/skills/ur-grue/autopunk-media-skills/data-footnote-writer"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/data-footnote-writer/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/skills/ur-grue/autopunk-media-skills/data-footnote-writer"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/data-footnote-writer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00046 | $0.01411 |
| Opus 5 | $0.00023 | $0.00705 |
| Sonnet 5 | $0.00009 | $0.00282 |
| Haiku 4.5 | $0.00005 | $0.00141 |
Grade A, and why
data-footnote-writer 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Footnote Writer
What This Skill Does
Writes precise, publication-standard footnotes for data claims in a finished article — citing sources accurately, explaining how figures were derived where necessary, and flagging any caveats that qualified readers need to know.
When To Use This Skill
- An article contains data claims that need formal sourcing in footnotes or endnotes
- An editor or fact-checker has asked for sources to be footnoted before publication
- You are writing a long-form data feature, annual report, or policy brief where footnoted sourcing is standard
- You want to ensure every quantitative claim in a piece is traceable to its source
What You Need To Provide
Required: The data claims from the article that need footnotes (the exact sentences or figures). The source for each claim — dataset name, institution, publication date, and URL or file reference. Whether the figure was taken directly from the source or derived through analysis. Optional: The publication format (academic, journalism, policy report) and its citation style if known; any methodological notes that should appear in the footnote rather than the body text.
How the Assistant Approaches This
- For each claim, identifies whether the figure is a direct quote from a source or a calculated/derived figure — and adjusts the footnote language accordingly ("Source: X" for direct quotes; "Calculated from X using [method]" for derived figures).
- Writes each footnote to the minimum length required for the claim to be reproduced or verified — no padding, no duplication of the body text.
- Numbers the footnotes in the order they appear in the article and formats them consistently.
Output Format
Numbered footnotes, one per data claim, formatted as: source information first, then derivation note if applicable, then caveat if applicable. Each footnote is one to three sentences. Consistent citation style throughout. At the end, a complete list of all sources cited.
Quality Criteria
- Every quantitative claim in the input has a corresponding footnote
- Footnotes distinguish between direct citations and derived figures
- Source information is complete: institution, publication name, date, and URL or file reference
- Derived figures include a brief description of the calculation method
- No footnote duplicates information already clear from the body text
- Footnotes are numbered in the order they appear in the article
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 76 lines · 46 tokens per session scan A c5fae1e7b1d4
data-footnote-writer is a skill published in the GitHub repository ur-grue/autopunk-media-skills (32 stars, last pushed 12d ago), licensed MIT. It adds 46 tokens to every session and 1,411 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 2026-08-30.
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