Outline Agent logo

Outline Agent

Community
Ar9av
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" or "generate the paper structure".

Overview

PublisherAr9av
RepositoryPaperOrchestra
Skill nameoutline-agent
Stars
681
Forks
94
Bundled files
6
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Ar9av on GitHub. Read the source before you install it.

Installation

Install the Outline Agent AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Ar9av/PaperOrchestra/tree/main/skills/outline-agent
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Ar9av/PaperOrchestra.git /tmp/PaperOrchestra
mkdir -p .claude/skills
cp -r /tmp/PaperOrchestra/skills/outline-agent .claude/skills/outline-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Outline Agent in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Outline Agent on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Outline Agent is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

  8. Append §1 to research_brief.md (see skills/shared/research_brief_template.md):

    After outline.json passes validation, append the §1 section to workspace/research_brief.md (create the file if absent). Template:

    markdown
    ## §1 · Core Claim and Narrative
    _Written by: outline-agent, Step 1_
    
    **Core claim:** <one-sentence contribution>
    **Narrative tension:** <gap this paper resolves>
    **Key novelty framing:** <how the contribution is framed relative to prior work>
    **Outline decisions:**
    - Plotting plan: <N> figures
    - Related Work clusters: <names>
    - Section structure: <section titles>
    **Potential weaknesses flagged at outline stage:**
    - <any claim in idea.md that may be hard to support>

    This is a free-form prose append; no machine-readable schema required.

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
  • skills/shared/research_brief_template.md — NEW §1 schema; append after outline.json passes validation

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Outline Agent AI skill do?

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".

Why use Outline Agent on TypingMind?

Because you install it once and use it with any model. Outline Agent is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Outline Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Ar9av/PaperOrchestra/tree/main/skills/outline-agent. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Outline Agent?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Outline Agent?

As many as you like. As long as a model supports skills, you can use Outline Agent with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Outline Agent AI skill free?

It is published on GitHub by Ar9av. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇