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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".

インストール方法を見る

含まれるファイル(7)

  • SKILL.md6.2 KB
  • references/allowed-values.md2.2 KB
  • references/example-output.json4.8 KB
  • references/outline_schema.json4.0 KB
  • references/outline-schema.md4.2 KB
  • references/prompt.md6.7 KB
  • scripts/validate_outline.py5.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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.

  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:

    ## §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

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.

日本語の概要は準備中です。原文の説明を表示しています。

Ar9av/PaperOrchestra6812026年10月12日 更新

Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate concession-threshold guard that blocks acceptance on unresolved critical findings. Maintains a worklog and snapshots each iteration so revert is real, not symbolic. TRIGGER when the orchestrator delegates Step 5 or when the user asks to "refine the draft", "iterate on the paper", or "run peer review on this paper".

日本語の概要は準備中です。原文の説明を表示しています。

Ar9av/PaperOrchestra6812026年10月12日 更新

Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to flag hallucinated citations, build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".

日本語の概要は準備中です。原文の説明を表示しています。

Ar9av/PaperOrchestra6812026年10月12日 更新

Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the user asks to "score this paper draft", "evaluate against the benchmark", "compare two papers", or "run the autoraters".

日本語の概要は準備中です。原文の説明を表示しています。

Ar9av/PaperOrchestra6812026年10月12日 更新

Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper from my experiments", "turn this idea and these results into a paper", "generate a conference submission", "run paper-orchestra on X", or otherwise wants the end-to-end paper-writing pipeline. Coordinates the outline-agent, plotting-agent, literature-review-agent, section-writing-agent, and content-refinement-agent skills.

日本語の概要は準備中です。原文の説明を表示しています。

Ar9av/PaperOrchestra6812026年10月12日 更新

Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a benchmark case from this paper", "reverse-engineer raw materials", or "evaluate my pipeline against PaperWritingBench".

日本語の概要は準備中です。原文の説明を表示しています。

Ar9av/PaperOrchestra6812026年10月12日 更新

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