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/edit-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/edit-section)<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/edit-section"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/edit-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/edit-section"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/edit-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 | $0.00015 | $0.00877 |
| Opus 5 | $0.00008 | $0.00439 |
| Sonnet 5 | $0.00003 | $0.00175 |
| Haiku 4.5 | $0.00002 | $0.00088 |
Grade A, and why
edit-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 13d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Edit Section Skill
When this skill is invoked, follow this structured workflow to revise an existing section of the paper.
Input
The user specifies which section to edit (file path or section key) and what kind of revision (style cleanup, content revision, restructuring, or specific feedback to address).
Workflow
Step 1: Read Current Text
- Read the target section from
main.tex - Note the current structure, length, and key claims
Step 2: Load Standards
- Read
.claude/rules/academic-writing.mdfor banned words, terminology, and style rules - Read
.claude/rules/banned-words.mdfor the full banned-word list - Read
guidance/paper-context.mdfor correct claims, numbers, and paper framing (if it exists)
Step 3: Diagnose Issues
Run through each check category:
A. Banned Words (read banned-words.md for the full current list; key examples: delve, crucial, comprehensive, utilize; AI markers: underscores, showcasing, pivotal, intricate, encompass, aligns with; previewing: "as we show below", "Recall from"; filler: of course, obviously, in other words)
B. Opening Quality -- Does sentence 1 state a concrete finding?
C. Voice and Tense -- Flag passive constructions
D. Quantitative Precision -- Numbers instead of adjectives; cross-check against paper-context.md if available
E. Terminology -- If guidance/paper-context.md defines a terminology table, check compliance. Otherwise flag any terms used inconsistently within the section.
F. Self-Praise -- Flag "striking", "important contribution", "novel"
G. Concision -- Cut repeated ideas, "in other words", sentences that don't earn their place
H. Em-Dashes -- No --- in prose (rewrite with commas, semicolons, colons, or parentheses)
I. Structural AI Tells -- Check for patterns from academic-writing.md: naked "this" without noun, "Importantly,"/"Notably,"/"Specifically," as sentence openers, "Together, these results..." openers (max 1/paper), "In this section, we..." throat-clearing, "This finding" repetition (max 1/paper), "Overall," as paragraph opener. Check soft-ban counts: "highlights" (max 2/paper), "insights" (max 1/paper)
J. Hedge Words -- Delete or quantify: somewhat, quite, very (intensifier), rather (hedge), arguably, perhaps. Replace with magnitudes. (See academic-writing.md "Kill Hedge Words")
K. Nominalizations -- Prefer verbs: "conduct an analysis" → "analyze", "provide evidence" → "show". (See academic-writing.md "Prefer Verbs over Nominalizations")
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.
- 13d ago First seen · 63 lines · 15 tokens per session scan A 308c05c79766
edit-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 15 tokens to every session and 877 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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