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 agentmods add skills/frenzymath/danus/write-papernpx skills add frenzymath/Danus --skill write-papergit clone --depth 1 https://github.com/frenzymath/DanusWrote 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/frenzymath/danus/write-paper)<a href="https://agentmods.dev/skills/frenzymath/danus/write-paper"><img src="https://agentmods.dev/badge/skills/frenzymath/danus/write-paper.svg" alt="Measured on agentmods" 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 | $0.00106 | $0.17578 |
| Opus 5 | $0.00053 | $0.08789 |
| Sonnet 5 | $0.00021 | $0.03516 |
| Haiku 4.5 | $0.00011 | $0.01758 |
Grade A, and why
write-paper 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 4d 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 — 1,087 lines — stays where its author put it; the contents beside it link to each section on GitHub.
write-paper — fact graph → publishable paper
You are the main agent. This skill turns a project's verified fact graph into
a publishable LaTeX paper in a configurable house style: a standalone
\documentclass{amsart} .tex with a real manual bibliography, compiled to a
clean PDF, ready for arXiv / an Overleaf (or other) LaTeX git repo. It is the
publication sibling of human-summary (a reader-facing progress report with no
bibliography).
The heavy LaTeX work is delegated to a local codex at extra-high (xhigh)
reasoning — the same codex machinery the workers and the verify service already
use. The writer,
auditor, verifier, and reviser roles are wrapped behind the write-paper MCP
service (tools paper_write / reference_audit / reference_verify / paper_revise):
you call them with structured args, the tool assembles each role's prompt
internally (so the style guide and fact-graph bytes never enter your context) and
drives the codex with each role isolated by construction. The reference chain is
auditor (offline, flags) → verifier (online, checks) → reviser (edits). You
orchestrate the stages, call these tools, gate on compilation, and surface the one
or two real decisions to the operator.
When to use
- A project's target theorem is established in the fact graph and the operator wants the paper.
- The operator asks to write, revise, audit references for, or push the paper for a project.
Do not use it for the progress report (human-summary) or the internal strategy
synthesis (elaboration).
Source of content: the fact graph (never your memory)
Build the mathematics from the project's verified facts
(<project>/fact_graph/facts/*.md) and the verbatim goal in
<project>/PROBLEM.md — exactly as human-summary does, but for publication:
- Each fact's
## statementis fully-quantified and self-contained → render into the paper's theorems/propositions (clean LaTeX, do not paraphrase loosely). Its## proofis the argument;## intuitionfeeds a proof sketch where useful. - Load-bearing facts first: high dependency depth (headline results) and high
in-degree (key lemmas).
predecessorsgive you the internalTheorem~\ref{}cross-reference structure with zero invention. - Citations come structured, from the source. Each fact's
external_refs(key / authors / title / arxiv / year / cited_for) records the published results its proof cited.driver/seed_ledger.pyaggregates them across the project into the startingREFERENCE_LEDGER.md. Do not re-mine citations from prose — that is the #1 failure mode (hallucinated references). - Preserve all mathematics. Invent nothing — no assumptions, lemmas,
citations, theorem labels, or definitions that are not in the fact graph. If a
step is unclear, flag
[GAP: ...], do not smooth it over.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 1,087 lines · 106 tokens per session scan A 5d7dbb9339aa
write-paper is a skill published in the GitHub repository frenzymath/Danus (387 stars, last pushed 7d ago), licensed Apache-2.0. It adds 106 tokens to every session and 17,578 once invoked, about $0.0005 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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