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/submission-prep/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/submission-prep)<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/submission-prep"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/submission-prep/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/submission-prep"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/submission-prep.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 | $0.00022 | $0.01200 |
| Opus 5 | $0.00011 | $0.00600 |
| Sonnet 5 | $0.00004 | $0.00240 |
| Haiku 4.5 | $0.00002 | $0.00120 |
Grade A, and why
submission-prep 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Submission Prep Skill
Run a pre-submission checklist tailored to the target journal. Catches common rejection-worthy issues before you submit.
Examples
/submission-prep-- run full checklist (uses target journal from project's CLAUDE.md)/submission-prep JF-- run checklist with JF-specific requirements/submission-prep --strict-- flag warnings as failures
Workflow
Step 1: Load Target Journal
Read the project's CLAUDE.md for the target journal. If not specified, ask the user. Common targets: JF, RFS, JFE, RoF, JFQA, MS.
Step 2: Run Checks
2a. Document Structure
- Abstract present and within word limit (100--150 words for JF/RFS/JFE)
- Title length reasonable (<15 words recommended)
- All expected body sections present (check project's
CLAUDE.mdfor section list) - Page count within typical range (40--60 pages including appendices for top-3)
2b. Content Completeness
- No
[HUMAN EDIT REQUIRED]tags remaining in any.texfile - No
TODO,FIXME,XXXcomments in.tex - No
\lipsumor placeholder text - No commented-out sections that should be removed or restored
2c. Terminology Compliance
- Project-specific terminology used consistently (check project's
CLAUDE.md) - No banned words from
academic-writing.mdSection 1
2d. Tables and Figures
- All tables have self-contained captions (sample period, units, variable definitions)
- All figures have self-contained captions with axis labels
- All tables and figures are referenced in the text (
\refcheck) - Numbers use 2--3 significant digits (not computer output)
- Tables use booktabs style (
\toprule/\midrule/\bottomrule, no vertical lines) - Figures use vector format (PDF) where possible
2e. Bibliography
- All
\cite{}keys resolve to.bibentries - No unused
.bibentries - No duplicate
.bibentries - Consistent BibTeX format (all
@articlehavejournal,year,volume,pages) - Proper nouns protected in titles:
{CAPM},{U.S.},{NYSE}, etc.
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 · 120 lines · 22 tokens per session scan A 2960e1e0e001
submission-prep is a skill published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 4mo ago), licensed MIT. It adds 22 tokens to every session and 1,200 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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