Skip to content
Agentmart
Submit a tool

publication

Build and review a Chinese XeLaTeX mathematical-modeling paper from a fixed selected result, evidence freeze, and formal execution manifest.

Skill by SoFarSoGoodya

IndexedMIT★ 2
Write a review

Chalk Labs

Need help deploying publication?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/sofarsogoodya-publication

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Build and review a Chinese XeLaTeX mathematical-modeling paper from a fixed selected result, evidence freeze, and formal execution manifest. Use for final paper narrative, result-backed figures, submission PDFs, or publication checks; do not use to change algorithms, samples, metrics, units, or human approval.

Use cases: research

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md and code. Checked Nov 1, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/publication

What's inside

Skill name
publication
Description
Build and review a Chinese XeLaTeX mathematical-modeling paper from a fixed selected result, evidence freeze, and formal execution manifest. Use for final paper narrative, result-backed figures, submission PDFs, or publication checks; do not use to change algorithms, samples, metrics, units, or human approval.
License
MIT
Instructions
28 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Publication

Read the selected-result record, evidence freeze, formal run manifest, current rule config,
and available human adoption facts before drafting. Stop with a science revision when the
run identity, metric value/definition/unit, sample set, aggregation, adopted claim, or rule
binding conflicts. Display-only changes such as final size, typography, color, marker,
linestyle, hatch, legend, and caption may proceed without recomputing a Metric.

Create a short `paper_plan.md` that states what each section answers, the frozen evidence it
uses, the mathematical or visual role, and the conclusion boundary. Write one coherent TeX
source rather than concatenating agent fragments. Repeated result values must come from
`generated/results.tex`, not manual transcription.

Use execution-owned data and scripts for data figures. Match final insertion width, provide a
non-color encoding, and draw intervals only when the run defines them. When only an original
real image exists without data or a script, disclose that it cannot be redrawn. Never present
a conceptual illustration as measured data.

Build the electronic target with XeLaTeX, then check citations, placeholders, anonymity,
critical numbers, page/size rules, metadata, embedded fonts, and the text layer. Rasterize
every page and inspect all of them. Compilation alone leaves `visual_review_pending`;
`review_ready` requires explicit page-complete visual notes and is not human approval.

Use `mathmodel_agent.publication.build_publication(case_root, request)` for the deterministic
pipeline. See `docs/modules/publication.md` for the request fields, rule configuration, CLI hook, and
exact commands.

From SoFarSoGoodya/mathmodel-weave, MIT.

Signals

Stars
2
Forks
0
Open issues
0
Last commit (this folder)
Oct 2, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from SoFarSoGoodya

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get publication running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying publication?

CallGet help
Cite this page

publication skill. Agent Mart. https://agentmart.dev/skills/sofarsogoodya-publication (last updated 2026-10-24)