outline-agent

name

outline-agent

description

Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimental_log.md, template.tex, conference_guidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator delegates Step 1 or when the user asks to "outline a paper from raw materials" or "generate the paper structure".

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