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-corrections-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-corrections-writer)<a href="https://agentmods.dev/skills/ur-grue/autopunk-media-skills/data-corrections-writer"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/data-corrections-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-corrections-writer"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/data-corrections-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 | $0.00045 | $0.01101 |
| Opus 5 | $0.00023 | $0.00550 |
| Sonnet 5 | $0.00009 | $0.00220 |
| Haiku 4.5 | $0.00005 | $0.00110 |
Grade A, and why
data-corrections-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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Corrections Writer
What This Skill Does
Writes a precise, publication-standard corrections notice for a data error published in an article — stating clearly what was wrong, what the correct figure is, and how the error affected the reported story.
When To Use This Skill
- A data error has been identified in a published article and needs a formal correction notice
- You need to word a correction that is honest about the scope of the error without overstating its impact on the story's conclusions
- A reader or subject has disputed a figure and you have confirmed the error
- An editor or legal team needs a corrections draft to review before publication
What You Need To Provide
Required: The original published figure (as it appeared in print or online). The correct figure. The article headline or title and publication date. A brief explanation of how the error occurred (if known — this can be "we do not know how this error occurred"). Optional: Whether the error affects the story's main conclusion (yes / no / partially); whether a related chart, table, or infographic also needs correction; the publication's standard corrections format or style.
How the Assistant Approaches This
- Drafts a correction that states the error precisely — the original figure, the correct figure, and where in the article it appeared — without defensive language or over-explanation.
- Assesses whether the correction should include a note on whether the story's core finding is affected, and drafts appropriate language: affirming the conclusion stands, or acknowledging that it changes.
- Produces the correction in the standard form: present tense, direct, no passive constructions that obscure responsibility.
Output Format
A corrections notice of two to five sentences. Standard structure: (1) The article title and date, (2) what was wrong and where it appeared, (3) what the correct figure is, (4) whether the error affects the article's conclusions. Optionally: a note on what caused the error if this is known and disclosable. Tone: direct, factual, no hedging. Plain language.
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 · 68 lines · 45 tokens per session scan A a207daa6045f
data-corrections-writer is a skill published in the GitHub repository ur-grue/autopunk-media-skills (32 stars, last pushed 11d ago), licensed MIT. It adds 45 tokens to every session and 1,101 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.
Other skills, from other repositories
secure-auth
Secure authentication patterns (OWASP, NIST). Use for login, registration, password reset, sessions, JWT, OAuth, MFA, passkeys.
visual-explainer
HTML explainers, diagrams, architecture, timelines, source maps, slide decks, comparison tables, recaps, plan and diff reviews.
foia-requests
FOIA and public records workflows. Use when drafting requests, tracking submissions, checking exemptions, or appealing denials.
academic-writing
Scholarly writing and research compliance. Use for CRediT, preregistration, Plan S, Nelson Memo, preprints, ORCID, LLM disclosure.
page-monitoring
Web page change detection, availability tracking, and RSS feed generation. Use to monitor changes, downtime, or make a feed.
web-archiving
Web archiving and retrieval via Wayback Machine and Archive.today. Use to preserve content, reach dead pages, or save evidence.