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-section/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-section)<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.
<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>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 | $0.00714 |
| Opus 5 | $0.00010 | $0.00357 |
| Sonnet 5 | $0.00004 | $0.00143 |
| Haiku 4.5 | $0.00002 | $0.00071 |
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 11d 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 — 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
- Read
.claude/rules/academic-writing.mdfor style rules - Read
.claude/rules/banned-words.mdfor hard/soft bans - Read
.claude/rules/grammar-punctuation.mdfor grammar conventions - Read
.claude/rules/latex-citations.mdfor citation protocol - If the project has
guidance/paper-context.md, read it for correct claims and numbers - 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.mdandacademic-writing.mdfor 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?
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.
- 11d ago First seen · 88 lines · 19 tokens per session scan A e899215ce2ee
audit-section 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 714 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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