# Outline Agent > 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"… - **Type:** Skill - **Install:** `agentstack add skill-woodfishhhh-ez-math-model-outline-agent` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [woodfishhhh](https://agentstack.voostack.com/s/woodfishhhh) - **Installs:** 0 - **Category:** [Search](https://agentstack.voostack.com/c/search) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [woodfishhhh](https://github.com/woodfishhhh) - **Source:** https://github.com/woodfishhhh/EZ_math_model/tree/main/skills/ez-math-model/external/paper-orchestra/skills/outline-agent ## Install ```sh agentstack add skill-woodfishhhh-ez-math-model-outline-agent ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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**: ```bash 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 ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [woodfishhhh](https://github.com/woodfishhhh) - **Source:** [woodfishhhh/EZ_math_model](https://github.com/woodfishhhh/EZ_math_model) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-woodfishhhh-ez-math-model-outline-agent - Seller: https://agentstack.voostack.com/s/woodfishhhh - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.