ai-asset-pricing: Skill for Claude Code

.claude/skills/audit-captions/SKILL.md

audit-captions is a skill for Claude Code from Alexander-M-Dickerson/ai-asset-pricing. It costs 19 tokens per session (2,497 once invoked), scanned A, original, MIT.

A systematic check of table and figure captions in an academic paper.

In plain words
What is it for?
Auditing captions, table notes, and figure notes in a LaTeX paper, either across the whole paper or within a selected scope.
Why use it?
Captions can become inconsistent in wording, notation, numbers, or formatting as a paper changes, which makes results harder to interpret.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

This is Alexander-M-Dickerson/ai-asset-pricing's own configuration. It tells Claude Code how to work on ai-asset-pricing itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ai-asset-pricing configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/Alexander-M-Dickerson/ai-asset-pricing/main/.claude/skills/audit-captions/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Alexander-M-Dickerson/ai-asset-pricing

Made for: Claude Code.

Wrote this? Show the measurements

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README.md
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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 audit-captions

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Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,497 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 $0.00019 $0.02497
Opus 5.5 $0.00008 $0.00999
Sonnet 5.5 $0.00004 $0.00499
Haiku 4.5 $0.00002 $0.00250

Measured 2mo ago against content hash 7fae5b9ad7c9, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

.claude/skills/audit-captions/SKILL.md · 220 lines

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

  1. Read .claude/rules/notation-protocol.md for mathematical notation conventions (if it exists)
  2. Read .claude/rules/academic-writing.md for terminology rules and banned words
  3. Read guidance/paper-context.md for canonical numbers, sample description, and project-specific terminology (if it exists)
  4. Read main.tex in 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}
  • 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.

Read the full file on GitHub · 220 lines

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. 2mo ago First seen · 220 lines · 19 tokens per session scan A 7fae5b9ad7c9

Subscribe to this mod's changes

audit-captions is a skill published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 6mo ago), licensed MIT. It adds 19 tokens to every session and 2,497 once invoked, about $0.0001 per session on Opus 5.5. 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

audit-captions compared with skills from other repositories

audit-captions next to 6 skills from other repositories
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audit-captionsAlexander-M-Dickerson/ai-asset-pricingSkill596mo agoMITA19Claude Code
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