Borrowing it
Nothing to install: this file belongs to Alexander-M-Dickerson/ai-asset-pricing. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Alexander-M-Dickerson/ai-asset-pricing/main/.claude/skills/audit-captions/SKILL.mdgit clone --depth 1 https://github.com/Alexander-M-Dickerson/ai-asset-pricingWrote 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/alexander-m-dickerson/ai-asset-pricing/audit-captions)<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/audit-captions"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/audit-captions/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/alexander-m-dickerson/ai-asset-pricing/audit-captions"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/audit-captions.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 | $0.00019 | $0.02497 |
| Opus 5 | $0.00010 | $0.01248 |
| Sonnet 5 | $0.00004 | $0.00499 |
| Haiku 4.5 | $0.00002 | $0.00250 |
Grade A, and why
audit-captions 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Captions Skill
Systematic audit of all \caption{} blocks, tablenotes, and figure notes for cross-caption consistency in language, notation, terminology, number formatting, and structure.
Examples
/audit-captions-- audit all captions in main.tex/audit-captions tables-- audit only table captions/audit-captions figures-- audit only figure captions/audit-captions appendix-- audit captions in appendix sections only
Input
The user provides an optional scope filter. If omitted, all captions in main.tex are audited.
Workflow
Step 1: Load Context
- Read
.claude/rules/notation-protocol.mdfor mathematical notation conventions (if it exists) - Read
.claude/rules/academic-writing.mdfor terminology rules and banned words - Read
guidance/paper-context.mdfor canonical numbers, sample description, and project-specific terminology (if it exists) - Read
main.texin full
Step 2: Extract All Captions
For each \caption{...} in main.tex, extract:
- Line number in main.tex
- Environment type: table or figure
- Label: the
\label{...}associated with this float - Caption title: the text inside
\caption{...} - Caption notes: any footnote-size block that follows the caption. Common patterns:
- Tables:
\caption{Title.}followed by\begin{spacing}{1}{\footnotesize ... }\end{spacing} - Figures:
\caption{Title.}followed by\begin{justify}\begin{spacing}{1}\footnotesize{...}\end{spacing}\end{justify} - Or
\begin{tablenotes}...\end{tablenotes}
- Tables:
- Panel descriptions: any
\textbf{Panel A:}or\multicolumn{...}{...}{\textbf{Panel A:} ...}text inside the float
Build a numbered registry of all captions for cross-comparison. Record the section each caption belongs to (using %% BEGIN/END markers).
Step 3: Language Consistency
Compare the phrasing of recurring concepts across ALL captions. Flag inconsistencies in:
3a. Sample period description
- Extract every mention of the sample period
- Flag: different date formats for the same sample (e.g., "YYYY-YYYY" vs "YYYY-MM to YYYY-MM" vs "Month YYYY to Month YYYY")
- Flag: inconsistent use of observation counts (some captions include $T$=N, others omit it)
- Note: different sample start dates are legitimate when different datasets start at different points. Only flag genuinely inconsistent formatting, not substantively different samples.
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 · 220 lines · 19 tokens per session scan A 7fae5b9ad7c9
audit-captions is a skill published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 4mo ago), licensed MIT. It adds 19 tokens to every session and 2,497 once invoked, about $0.0001 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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