Outline Agent (Step 1)

Faithful implementation of the Outline Agent from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, App. F.1, pp. 40–44).

Cost: 1 LLM call.

Your task

Read four input files from the workspace and produce a single JSON object at

workspace/outline.json

with three top-level keys:

• plotting_plan

  — array of figure objects

• intro_related_work_plan

  — object with

  introduction_strategy

  and

  related_work_strategy

• section_plan

  — array of section objects, each with

  section_title

  and

  subsections[]

How to do it

1. Read the verbatim prompt at

   references/prompt.md

   . This is the exact Outline Agent system prompt from the paper. Use it as your system message.
2. Prepend the Anti-Leakage Prompt from

   ../paper-orchestra/references/anti-leakage-prompt.md

   .
3. Read the four input files:

   • workspace/inputs/idea.md

   • workspace/inputs/experimental_log.md

   • workspace/inputs/template.tex

   • workspace/inputs/conference_guidelines.md

4. Synthesize across all four — the global instruction in the prompt is "Do not analyze inputs in isolation. You must synthesize information across all provided documents for every step."
5. Emit a single JSON object following the schema in

   references/outline-schema.md

   . Cross-check against

   references/outline_schema.json

   (machine-readable).
6. Save to

   workspace/outline.json

   .
7. Validate:

   python skills/outline-agent/scripts/validate_outline.py workspace/outline.json
   

   If validation fails, fix the JSON and re-validate. Do not proceed to Step 2 or Step 3 with an invalid outline — every downstream agent depends on this schema.

Hard rules from the prompt (do not violate)

These are excerpted from

references/prompt.md

. The validator enforces them.

Plotting plan (Directive 1)

• plot_type

  MUST be exactly one of

  "plot"

  or

  "diagram"

  .

• data_source

  MUST be exactly one of

  "idea.md"

  ,

  "experimental_log.md"

  , or

  "both"

  .

• aspect_ratio

  MUST be exactly one of:

  "1:1"

  ,

  "1:4"

  ,

  "2:3"

  ,

  "3:2"

  ,

  "3:4"

  ,

  "4:1"

  ,

  "4:3"

  ,

  "4:5"

  ,

  "5:4"

  ,

  "9:16"

  ,

  "16:9"

  ,

  "21:9"

  .

• figure_id

  MUST be a semantically meaningful snake_case identifier (e.g.,

  fig_framework_overview

  ,

  fig_ablation_study_parameter_sensitivity

  ).

• figure_id

  MUST NOT contain the word

  "Figure"

  .

Intro / Related Work strategy (Directive 2)

• Strictly separate Introduction (macro-level context, 10-20 papers, foundational + survey + impact) from Related Work (micro-level technical baselines, 30-50 papers, divided into 2-4 methodology clusters that directly compete with or precede the proposed approach).
• For each Related Work cluster: provide

  methodology_cluster

  ,

  sota_investigation_mission

  ,

  limitation_hypothesis

  ,

  limitation_search_queries

  ,

  bridge_to_our_method

  .
• CRITICAL TIMELINE RULE: Do not instruct searches for any papers published after

  {cutoff_date}

  . Derive

  cutoff_date

  from

  conference_guidelines.md

  (e.g., "ICLR 2025 → cutoff October 2024", "CVPR 2025 → cutoff November 2024"). If unspecified, default to one month before today's date.

Section plan (Directive 3)

• Structural hierarchy: if Subsection X.1 is created, X.2 is mandatory. No orphaned subsections. Omit subsections entirely if a section does not require division.
• Content specificity: each

  content_bullets

  entry must reference source materials concretely. AVOID "Describe the model". REQUIRE "Formalize the Temporal-Aware Attention mechanism using Eq. 3 from idea.md."
• Mandatory citations: every dataset, optimizer, metric, and foundational architecture/model mentioned in

  idea.md

  or

  experimental_log.md

  MUST have a citation hint, no matter how ubiquitous (e.g., AdamW, ResNet, ImageNet, CLIP, Transformer, LLaMA, GPT, LLaVA).
• Citation hint format:
  • If you know the exact author and title:

    "Author (Exact Paper Title)"

  • Otherwise:

    "research paper or technical report introducing '[Exact Model/Dataset/Metric Name]'"

  • Do NOT guess or hallucinate authors.

Output

Exactly one file:

workspace/outline.json

. No prose, no code blocks, no markdown. The Section Writing Agent and Literature Review Agent will parse this JSON directly.

See

references/example-output.json

for a complete worked example from the paper (App. F.1, pp. 43–44).

Resources

• references/prompt.md

  — verbatim Outline Agent prompt from App. F.1

• references/outline-schema.md

  — prose explanation of the schema

• references/outline_schema.json

  — machine-readable JSON Schema

• references/example-output.json

  — example output from the paper

• references/allowed-values.md

  — enumerated allowed values for each enum field

• scripts/validate_outline.py

  — JSON Schema validator