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).