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joshua-zyy/academic-paper-writer

An evidence-driven, section-by-section paper writing agent skill for the CS / AI / ML domain.

What is academic-paper-writer?

This skill is designed to assist AI agents in writing academic papers within the Computer Science, Artificial Intelligence, and Machine Learning fields. It follows an evidence-driven, progressive approach, structuring the paper section by section. The skill provides a set of predefined working modes, including drafting from data, generating blueprints, auditing existing figures, and suggesting revisions. It relies on a configuration manifest (`manifest.yaml`) and dynamically loads constraints from files like `stance.md` and `red-lines.md` to enforce strict rules regarding output, visual policies, and prohibited patterns. Notably, the skill explicitly restricts automatic generation of complex diagrams like architecture overviews, routing such requests to a manual figure blueprint mode. It is intended for use in AI agent workflows where a structured, rule-compliant paper generation pipeline is needed, ensuring reproducible and verification-ready outputs.

Works with~Claude Code~Codex CLI~Cursor
npx skills add joshua-zyy/academic-paper-writer

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Documentation

Academic Figure

CS/AI/ML academic-figure router. Experimental data figures are default delivered as editable Python/matplotlib SVGs and pass through the CS/AI/ML chart design gate. Model framework diagrams, architecture diagrams, overview figures, and complex mechanism diagrams are out of scope for automatic rendering by this skill; if the paper requires such figures, only human-drawing requirements, evidence checklists, caption drafts, or figure blueprint notes may be produced.

Router Protocol

  1. Read manifest.yaml. It declares always_load files, axes, and references.on_demand.
  2. Read every file listed under always_load. These are the skill's binding rules — not reference material.
  3. Apply the loaded material as constraints:
    • stance.md defines Python-only plotting, figure contract, visual policy, and scope.
    • red-lines.md defines absolute prohibitions. Do not negotiate these.
    • output-contract.md defines deliverables per mode.
    • anti-patterns.md defines known failure modes and their correct alternatives.
  4. Select exactly one mode from the manifest. If ambiguous, ask one concise clarification only when data source or target use is missing. Requests for model framework, architecture, overview, or mechanism diagrams use figure-blueprint only to produce manual_figure_needed notes; never render images, SVG, or prompts.
  5. Echo the selected mode to the user before executing.
  6. Reach for references/ only when the manifest's references.on_demand condition is satisfied.

Modes

ModeUse when
chart-from-dataData files or numeric results, needs publication plot with CS/AI/ML chart design gate
figure-blueprintWants figure suggestions for a paper section
figure-auditExisting figure reviewed for publication readiness
figure-revisionExisting figure needs revision

Agent Dispatch

agents/figure_agent.md is dispatched by the orchestrator at Step 6.4. The agent returns figure artifacts, scripts, SVG paths, and reports; it must not independently edit project source code or experimental data.

Completion Criteria

  • chart-from-data: Figure Contract, CS/AI/ML chart design gate, Python script, source data, editable SVG, QA report — all pass.
  • figure-audit: Every QA item has pass/fail status and concrete remediation.
  • figure-blueprint: Every suggested figure maps to a paper claim and data/evidence source.
  • figure-revision: Revised artifact or instructions, QA report, unchanged evidence traceability.

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