agent-research-aggregator
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 o
npx skills add Ar9av/PaperOrchestra --skill agent-research-aggregator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# agent-research-aggregator --- ## Should I run? (decision gate) Before starting Phase 1, check whether aggregation is actually needed: | Situation | Action | |---|---| | `workspace/inputs/idea.md` **and** `workspace/inputs/experimental_log.md` both exist and are non-empty | **Skip this skill entirely.** Proceed directly to `paper-orchestra`. | | Either file is missing or empty, **and** the user provided a directory path | **Run this skill** with that directory as `--search-roots`. | | Either file is missing or empty, **and** no directory was provided | Scan cwd and `~` by default; show the discovery summary to the user before continuing. | | The inputs exist but look thin (e.g. idea.md has < 5 lines, no numeric data in experimental_log.md) | **Ask the user** whether to supplement with aggregation or proceed as-is. | The skill is intentionally a pre-pass — it is cheap to skip and should only run when the structured inputs don't already exist. --- A pre-processing skill for PaperOrchestra (arXiv:2604.05018). Reads scattered experimentation artifacts from AI coding-agent cache directories and synthesizes them into the structured `(I, E)` input pair the PaperOrchestra pipeline expects
- Should I run? (decision gate)
- Inputs
- Phase 1 — Discovery (deterministic)
- Phase 1.5 — Project Selection (mandatory)
- Phase 2 — Extraction (LLM-assisted)
- Phase 3 — Synthesis (LLM-assisted)
- Phase 4 — Formatting (deterministic)
- workspace/inputs/idea.md (Sparse variant)
- workspace/inputs/experimentallog.md
- Phase 5 — Audit Report (deterministic)
- Handoff to PaperOrchestra
- Error handling
- Hard rules (never violate)
- Quick reference
python skills/agent-research-aggregator/scripts/discover_logs.py \ python skills/agent-research-aggregator/scripts/extract_experiments.py \ python skills/agent-research-aggregator/scripts/format_po_inputs.py \ Phase 1: discover all projects (exits with code 2 — project selection required) Phase 1.5: re-run with chosen project (exits 0)
What does the agent-research-aggregator skill do?
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 o
How do I install it?
Run `npx skills add Ar9av/PaperOrchestra --skill agent-research-aggregator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Ar9av/PaperOrchestra, a repository with 627 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.