agentmods.dev

ai-asset-pricing: Skill for Claude Code

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

audit-section is a skill for Claude Code from Alexander-M-Dickerson/ai-asset-pricing. It costs 19 tokens per session (714 once invoked), scanned A, original, MIT.

A review tool for one section of an academic paper. It checks writing style, factual claims, references, and the order of ideas.

In plain words
What is it for?
Use it to review sections such as the introduction, data, or results in a LaTeX paper. It can apply the project's writing and citation rules.
Why use it?
It helps find unclear wording, unsupported statements, citation problems, and gaps in reasoning before the paper is shared.

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 →

not rated 59repo 6mo ago A scan 19 tokens original MIT
View source ↗ Alexander-M-Dickerson/ai-asset-pricing
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-section/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

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.

agentmods badge for audit-section

README.md
[![agentmods](https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/audit-section/github.svg)](https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/audit-section)
Your own site
<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/audit-section"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/audit-section/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.

agentmods 80×15 button for audit-section

Your own site · 80×15
<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/audit-section"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/audit-section.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 714 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 $0.00714
Opus 5.5 $0.00008 $0.00286
Sonnet 5.5 $0.00004 $0.00143
Haiku 4.5 $0.00002 $0.00071

Measured 2mo ago against content hash e899215ce2ee, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

audit-section 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-section/SKILL.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Audit Section Skill

Comprehensive single-section audit combining style checking, factual verification, citation auditing, and logical flow analysis.

Examples

  • /audit-section introduction -- deep audit of the introduction
  • /audit-section data -- audit the data section
  • /audit-section results -- audit the results section

Workflow

Step 1: Load Context

  1. Read .claude/rules/academic-writing.md for style rules
  2. Read .claude/rules/banned-words.md for hard/soft bans
  3. Read .claude/rules/grammar-punctuation.md for grammar conventions
  4. Read .claude/rules/latex-citations.md for citation protocol
  5. If the project has guidance/paper-context.md, read it for correct claims and numbers
  6. Read the target section from main.tex (use /extract-section)

Step 2: Style Audit

Run the full /style-check analysis:

  • Banned words, throat-clearing, passive voice, superlatives, vague claims, self-praise
  • Structural AI tells: em-dashes, AI-marker words (per Kobak/Liang), naked "this", adverb openers, "Together, these results...", soft-ban counts
  • Hedge words & previewing: somewhat/quite/very/arguably/perhaps (Nikolov); "as we show below"/"Recall from" (Cochrane); nominalizations (Williams)
  • See banned-words.md and academic-writing.md for the full current lists

Step 3: Factual Accuracy

If the project has guidance/paper-context.md, cross-reference every quantitative claim:

  • Do numerical claims match the paper's canonical values?
  • Do table references match actual table content?
  • Flag any inconsistency between text claims and tables/figures

If no paper-context file exists, flag claims that cannot be verified.

Step 4: Citation Audit

For each citation in the section:

  • Verify key exists in .bib
  • Check citation supports the claim being made (not just existence but relevance)
  • Flag citations used out of context

Step 5: Logical Flow

  • Does the section follow a logical progression?
  • Are transitions between paragraphs smooth?
  • Is there redundancy (same point made twice)?
  • Does the opening paragraph set up what follows?
  • Does the section deliver on its implicit promise?

Read the full file on GitHub · 88 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 · 88 lines · 19 tokens per session scan A e899215ce2ee

Subscribe to this mod's changes

audit-section 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 714 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.

ScientificWriting and docs
Related

audit-section compared with skills from other repositories

audit-section next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
audit-sectionAlexander-M-Dickerson/ai-asset-pricingSkill596mo agoMITA19Claude Code
instrument-data-to-allotropeanthropics/knowledge-work-pluginsSkill35k2d agoApache-2.0A123Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA83Claude Code
matlabK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA44Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA71Claude Code, Codex
research-engineerdavila7/claude-code-templatesSkill33kyesterdayMITA43Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmedSkill5.8kyesterdayApache-2.0A205Claude Code