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-math/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-math)<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/audit-math"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/audit-math/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-math"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/audit-math.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 | $0.00018 | $0.02241 |
| Opus 5 | $0.00009 | $0.01120 |
| Sonnet 5 | $0.00004 | $0.00448 |
| Haiku 4.5 | $0.00002 | $0.00224 |
Grade A, and why
audit-math 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Math Skill
Structured adversarial audit of mathematical content: proofs, derivations, definitions, assumptions, and formal environments. Works on any section containing formal math.
Examples
/audit-math appendix-a-- full audit of Appendix A/audit-math methodology-- audit math content in the methodology section/audit-math 200-450-- audit specific line range in main.tex
Input
The user provides one of:
- A section key (e.g.,
appendix-a,methodology,results) - A line range (e.g.,
200-450) - If omitted, scans main.tex for sections containing formal environments and audits the first one found
Workflow
Step 1: Load Context
- Read
.claude/rules/notation-protocol.mdfor custom commands, core variables, subscript conventions, theorem environment numbering, and math writing rules (if it exists) - Read
guidance/paper-context.mdfor key labels, formal environment ordering, and canonical results (if it exists) - Read
.claude/rules/academic-writing.mdfor cross-reference style rules and terminology - Extract the target section from
main.texusing%% BEGIN/ENDmarkers (or line range)
Step 2: Inventory Formal Environments
Build a registry of every formal environment in the target section:
- Definitions (
\begin{definition}) - Assumptions (
\begin{assumption}) - Lemmas (
\begin{lemma}) - Propositions (
\begin{proposition}) - Corollaries (
\begin{corollary}) - Theorems (
\begin{theorem}) - Proofs (
\begin{proof}) - Remarks (
\begin{remark})
For each, record: label, name (from [...] bracket), line number, and which assumptions/results it references.
If notation-protocol.md registers environment numbering, cross-check against it. Flag any missing or extra environments, misordered labels, or mismatched names.
Step 3: Assumption Dependency Audit
For each lemma, proposition, corollary, and theorem:
- List the assumptions explicitly invoked in the statement (e.g., "Under Assumptions \ref{assum:iid} and \ref{assum:overlap}")
- List the assumptions actually used in the proof
- Flag hidden dependencies: assumptions used in the proof but not stated in the result
- Flag unused declarations: assumptions stated in the result but never invoked in the proof
- Flag implicit prerequisites: does the result depend on an earlier definition or lemma not cited?
- Check that assumption references use
\ref{assum:...}(not prose descriptions like "the independence assumption")
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 · 182 lines · 18 tokens per session scan A 0e8d231e1ff9
audit-math is a skill published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 4mo ago), licensed MIT. It adds 18 tokens to every session and 2,241 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…