paperorchestra

PaperOrchestra

A pluggable skill pack that lets any coding agent in Claude Code, Cursor, Antigravity, Cline, Aider, OpenCode, etc. which can run the PaperOrchestra multi-agent pipeline for turning unstructured research materials into a submission-ready LaTeX paper.

Song, Y., Song, Y., Pfister, T., Yoon, J. PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing. arXiv:2604.05018, 2026. https://arxiv.org/pdf/2604.05018

Click to read the paper on arXiv

Why this exists

The paper defines a five-agent pipeline

• Outline
• Plotting
• Literature Review
• Section Writing
• Content Refinement

that substantially outperforms single-agent and tree-search baselines on the PaperWritingBench benchmark (50–68% absolute win margin on literature review quality; 14–38% on overall quality). The paper ships the exact prompts for every agent in Appendix F.

This repo turns those prompts, schemas, halt rules, and verification pipelines into a set of host-agent-executable skills. There are no API keys, no SDK dependencies, no embedded LLM calls. The skills are instruction documents plus deterministic helpers; your coding agent does all LLM reasoning and web search using its own tools.

How skills work here

Each skill is:

• SKILL.md — a dense instruction document the host agent reads and follows.
• references/ — reference material: verbatim paper prompts (Appendix F), JSON schemas, rubrics, halt rules, example outputs.
• scripts/ — purely deterministic local helpers: JSON schema validation, Levenshtein fuzzy matching, BibTeX formatting, dedup, LaTeX sanity checks, coverage gates. No network, no LLM, no API keys.

Everything else (LLM reasoning, web search, Semantic Scholar lookups, LaTeX compilation) is delegated to the host agent by instruction. See skills/paper-orchestra/references/host-integration.md for per-host invocation (Claude Code, Cursor, Antigravity, Cline, Aider).

The seven skills

Skill	Paper step	# LLM calls	Role
paper-orchestra	orchestrator	—	Top-level driver. Coordinates the other six.
outline-agent	Step 1	1	Idea + log + template + guidelines → structured outline JSON (plotting plan, lit review plan, section plan).
plotting-agent	Step 2	~20–30	Execute plotting plan; render plots & conceptual diagrams; optional VLM-critique refinement loop; caption everything.
literature-review-agent	Step 3	~20–30	Web-search candidates; Semantic Scholar verify (Levenshtein > 70, cutoff, dedup); draft Intro + Related Work with ≥90% citation integration.
section-writing-agent	Step 4	1	One single multimodal call: draft remaining sections, build tables from experimental log, splice figures.
content-refinement-agent	Step 5	~5–7	Simulated peer review; accept/revert per strict halt rules; safety constraints prevent gaming the evaluator.
paper-writing-bench	§3	—	Reverse-engineer raw materials (Sparse/Dense idea, experimental log) from an existing paper to build benchmark cases.
paper-autoraters	App. F.3	—	Run the paper's own autoraters: Citation F1 (P0/P1), LitReview quality (6-axis), SxS paper quality, SxS litreview quality.

Steps 2 and 3 run in parallel (see skills/paper-orchestra/references/pipeline.md).

agent-research-aggregator (optional)

A pre-pipeline skill that bridges the gap between scattered AI coding-agent history and the structured (idea.md, experimental_log.md) inputs that PaperOrchestra expects. If you have been running experiments through Claude Code, Cursor, Antigravity, or OpenClaw — but never wrote up a clean experiment log — this skill does that extraction for you.

It is optional. If workspace/inputs/idea.md and workspace/inputs/experimental_log.md already exist, the skill skips itself and the pipeline proceeds directly. It only runs when the inputs are missing or when you explicitly point an agent at a directory.

The simplest way to use it: just tell your agent the folder. If you have a directory (a project root, an agent cache, any folder with research notes), the aggregator figures out what's inside and structures it for PaperOrchestra. The first thing it does is aggregate — scanning, extracting, and synthesising — so even if the data is scattered across multiple files and formats, it produces clean, reviewable inputs before anything gets written.

Run it before paper-orchestra (or let paper-orchestra call it automatically when inputs are missing).

What it does

[.claude/]  [.cursor/]  [.antigravity/]  [.openclaw/]
      │            │              │               │
      └────────────┴──────────────┴───────────────┘
                        │
                Phase 1: Discovery  (deterministic)
                        │
                Phase 2: Extraction (LLM — per batch)
                        │
                Phase 3: Synthesis  (LLM — one call)
                        │
                Phase 4: Formatting (deterministic)
                        │
             ┌──────────┴──────────┐
      workspace/inputs/      workspace/ara/
        idea.md                aggregation_report.md
        experimental_log.md    discovered_logs.json
                               raw_experiments.json
                               synthesis.json

The four phases are:

Phase	Tool	What happens
1 Discovery	discover_logs.py	Walks --search-roots to catalog every relevant log file across all agent caches. Prints a summary for user review before anything is read.
2 Extraction	LLM (per ~50 KB batch)	Applies references/extraction-prompt.md to each batch; produces raw_experiments.json. PII is stripped; unverified numbers are flagged [UNVERIFIED].
3 Synthesis	LLM (one call)	Merges possibly-redundant experiment records into a single research narrative (synthesis.json). Detects multiple disconnected projects and pauses to ask the user.
4 Formatting	format_po_inputs.py	Converts synthesis.json into idea.md (Sparse Idea format, §3.1) and experimental_log.md (App. D.3), ready for paper-orchestra.

Integration

Install — no extra dependencies beyond the base requirements.txt.

Symlink the skill into your host's skill directory alongside the others:

ln -sf ~/paper-orchestra/skills/agent-research-aggregator \
       ~/.claude/skills/agent-research-aggregator

For Cursor / Antigravity / Cline / Aider, follow the same per-host instructions in skills/paper-orchestra/references/host-integration.md.

Invoke by telling your coding agent:

"Aggregate my agent logs for paper writing" — or — "Prepare PaperOrchestra inputs from my cache" — or — "Turn my agent logs into a paper"

The trigger phrases are listed in the description field of skills/agent-research-aggregator/SKILL.md.

Parameters

Flag	Default	Description
--search-roots	cwd, ~	Directories to scan for agent caches
--agents	all	Subset: claude,cursor,antigravity,openclaw
--workspace	./workspace	PaperOrchestra workspace root
--depth	4	Max scan depth (prevents runaway traversal)
--since	—	Only logs modified after this date (ISO 8601)

Example workflows

From Claude Code memory + CLAUDE.md only:

python skills/agent-research-aggregator/scripts/discover_logs.py \
    --search-roots . \
    --agents claude \
    --out workspace/ara/discovered_logs.json
# → finds .claude/projects/<hash>/memory/*.md and CLAUDE.md

From a Cursor project (chat history + rules):

python skills/agent-research-aggregator/scripts/discover_logs.py \
    --search-roots ~/my-project \
    --agents cursor \
    --out workspace/ara/discovered_logs.json
# → finds .cursor/chat/chatHistory.json and .cursorrules

From Antigravity worker logs, restricted to the last 60 days:

python skills/agent-research-aggregator/scripts/discover_logs.py \
    --search-roots ~/my-project \
    --agents antigravity \
    --since 2026-02-09 \
    --out workspace/ara/discovered_logs.json
# → finds .antigravity/workers/<id>/log.jsonl and output.md

From OpenClaw sessions + run metrics:

python skills/agent-research-aggregator/scripts/discover_logs.py \
    --search-roots ~/my-project \
    --agents openclaw \
    --out workspace/ara/discovered_logs.json
# → finds .openclaw/sessions/*/conversation.md and runs/*/metrics.json

Full run across all caches:

# Phase 1 — discovery
python skills/agent-research-aggregator/scripts/discover_logs.py \
    --search-roots . ~ --out workspace/ara/discovered_logs.json

# Phase 2 — LLM extraction (your agent handles this; validate afterward)
python skills/agent-research-aggregator/scripts/extract_experiments.py \
    --discovered workspace/ara/discovered_logs.json \
    --out workspace/ara/raw_experiments.json --validate-only

# Phase 3 — LLM synthesis (your agent handles this)

# Phase 4 — format + audit report
python skills/agent-research-aggregator/scripts/format_po_inputs.py \
    --synthesis workspace/ara/synthesis.json \
    --out workspace/inputs/ \
    --report workspace/ara/aggregation_report.md

After Phase 4, the workspace is ready for paper-orchestra. You still need to supply workspace/inputs/template.tex (your conference LaTeX template) and workspace/inputs/conference_guidelines.md (page limit, deadline, formatting rules).

Reference docs

• skills/agent-research-aggregator/SKILL.md — full phase-by-phase protocol
• skills/agent-research-aggregator/references/log-formats.md — per-agent cache layouts and file priorities
• skills/agent-research-aggregator/references/extraction-prompt.md — verbatim LLM extraction prompt
• skills/agent-research-aggregator/references/synthesis-prompt.md — verbatim LLM synthesis prompt

Install

git clone <this repo> ~/paper-orchestra
cd ~/paper-orchestra
pip install -r requirements.txt   # deterministic helpers only

Then symlink the skills you want into your host's skill directory:

# Claude Code
mkdir -p ~/.claude/skills
for s in paper-orchestra outline-agent plotting-agent literature-review-agent \
         section-writing-agent content-refinement-agent paper-writing-bench \
         paper-autoraters agent-research-aggregator; do
  ln -sf ~/paper-orchestra/skills/$s ~/.claude/skills/$s
done

# Or for ~/.all-skills/
mkdir -p ~/.all-skills
for s in paper-orchestra outline-agent plotting-agent literature-review-agent \
         section-writing-agent content-refinement-agent paper-writing-bench \
         paper-autoraters agent-research-aggregator; do
  ln -sf ~/paper-orchestra/skills/$s ~/.all-skills/$s
done

For Cursor / Antigravity / Cline / Aider, see skills/paper-orchestra/references/host-integration.md.

Optional integrations

The pipeline requires zero API keys to run under any host with a native web search tool. Two optional integrations improve throughput or coverage:

• Semantic Scholar API key — Phase 2 (citation verification) uses the public unauthenticated Semantic Scholar