Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add VeryMath/AI4Math-Writing --skill paper-skeleton-and-logical-architecturegit clone --depth 1 https://github.com/VeryMath/AI4Math-WritingWrote 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/verymath/ai4math-writing/paper-skeleton-and-logical-architecture)<a href="https://agentmods.dev/skills/verymath/ai4math-writing/paper-skeleton-and-logical-architecture"><img src="https://agentmods.dev/badge/skills/verymath/ai4math-writing/paper-skeleton-and-logical-architecture/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/verymath/ai4math-writing/paper-skeleton-and-logical-architecture"><img src="https://agentmods.dev/badge/skills/verymath/ai4math-writing/paper-skeleton-and-logical-architecture.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 | $0.00045 | $0.00483 |
| Opus 5 | $0.00023 | $0.00242 |
| Sonnet 5 | $0.00009 | $0.00097 |
| Haiku 4.5 | $0.00005 | $0.00048 |
Grade A, and why
paper-skeleton-and-logical-architecture 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Skeleton And Logical Architecture
Build the paper's mathematical architecture before polishing prose. The goal is to make definitions, results, dependencies, proof placeholders, examples, and section roles visible early enough to catch gaps.
Inputs
- Source packet: definitions, theorem statements, lemmas, proof sketches, experiment logs, figures, reading notes, and intended target venue.
- Optional existing outline, draft, reviewer comments, or page constraints.
If the source packet is missing, request it or produce only a checklist of materials needed for a skeleton.
Output Contract
Return:
- Paper skeleton: section order, purpose of each section, and expected inputs.
- Result dependency map: definitions, assumptions, lemmas, propositions, theorems, corollaries, algorithms, experiments, and figures.
- Gap list: missing definitions, missing proof sketches, unsupported examples, unplaced results, circular dependencies, and intro/abstract claims that do not match the technical core.
- Drafting plan: what can be written now, what needs proof/citation/experiment evidence first, and which subskill should run next.
Workflow
- Inventory source materials before writing.
- Identify the main result, supporting results, and reader prerequisites.
- Build a dependency map from definitions to claims and from claims to proof or empirical support.
- Assign each section a job: motivation, background, setup, main result, proof, examples, experiments, limitations, or conclusion.
- Mark placeholders explicitly; do not fill missing proofs, citations, or experiments with plausible prose.
- Route theorem-risk items to
proof-obligation-and-assumption-auditand support-risk items toclaim-evidence-ledger.
Review Rules
- Prefer a sparse skeleton over fluent prose when dependencies are unclear.
- Keep introduction, abstract, and conclusion claims weaker than or equal to the verified technical results.
- Do not add new mathematical claims to make the story smoother.
- Treat examples and experiments as structural evidence only after their source logs or statements are present.
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 · 55 lines · 45 tokens per session scan A 7f83d80b9877
paper-skeleton-and-logical-architecture is a skill published in the GitHub repository VeryMath/AI4Math-Writing (6 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 483 once invoked, about $0.0002 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 2026-08-31.
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