--- name: agent-research-aggregator description: 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. --- # 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. ``` [.claude/] [.cursor/] [.antigravity/] [.openclaw/] │ │ │ │ └────────────┴──────────────┴───────────────┘ │ Phase 1: Discovery (discover_logs.py) │ discovered_logs.json │ Phase 2: Extraction (LLM call per log batch) │ raw_experiments.json │ Phase 3: Synthesis (LLM call — consolidate) │ synthesis.json │ Phase 4: Formatting (format_po_inputs.py) │ ┌────────────┴────────────┐ workspace/inputs/ workspace/ara/ idea.md aggregation_report.md experimental_log.md discovered_logs.json raw_experiments.json synthesis.json ``` The output drops directly into `workspace/inputs/` so the user can immediately run `paper-orchestra` on the same workspace. --- ## Inputs | Parameter | Required | Default | Description | |---|---|---|---| | `--search-roots` | no | cwd, `~` | Comma-separated directories to scan for agent caches | | `--agents` | no | all | Comma-separated subset: `claude,cursor,antigravity,openclaw` | | `--workspace` | no | `./workspace` | PaperOrchestra workspace root | | `--depth` | no | 4 | Max directory scan depth (prevents runaway scans on large home dirs) | | `--since` | no | none | Only include logs modified after this date (ISO 8601: `2025-01-01`) | The user specifies these when invoking the skill, or you may ask them for `--search-roots` if the current directory has no detectable agent caches. --- ## Phase 1 — Discovery (deterministic) Run the discovery script to catalog every relevant log file: ```bash python skills/agent-research-aggregator/scripts/discover_logs.py \ --search-roots \ --agents \ --depth \ --since \ --out workspace/ara/discovered_logs.json ``` The script prints a **discovery summary** to stdout — show it to the user before proceeding so they can confirm or prune the file list. Read `references/log-formats.md` to understand what each agent type stores. **If no logs are found at all:** stop and ask the user to specify `--search-roots` or point you at a directory that contains agent cache folders. **If the discovery summary shows irrelevant files:** ask the user whether to include or exclude them before continuing to Phase 2. Err on the side of inclusion — the extraction prompt is conservative. --- ## Phase 2 — Extraction (LLM-assisted) Process discovered logs in **batches** (group by agent type; keep batches under ~50 KB of raw text to stay within context limits): For each batch: 1. **Read** the log files in the batch (the script's `--list` output tells you which file paths to read). 2. **Apply the extraction prompt** from `references/extraction-prompt.md` as your system message. 3. **Pass the raw log text** as the user message. 4. **Collect the structured JSON** the LLM returns (see schema in the prompt). 5. **Append** to `workspace/ara/raw_experiments.json`. After all batches: ```bash python skills/agent-research-aggregator/scripts/extract_experiments.py \ --discovered workspace/ara/discovered_logs.json \ --out workspace/ara/raw_experiments.json \ --validate-only ``` Run this in `--validate-only` mode to check the combined JSON is well-formed and meets the minimum schema (`experiments` array non-empty, each entry has `hypothesis` or `method` or `results`). Fix any malformed entries before Phase 3. --- ## Phase 3 — Synthesis (LLM-assisted) Consolidate possibly-redundant experiment records from multiple agent caches into a single coherent research narrative. This is ONE LLM call. **System message:** Use `references/synthesis-prompt.md` verbatim. **User message:** ``` {contents of workspace/ara/raw_experiments.json} ``` The LLM must return a `synthesis.json` with keys: - `research_question` — the overarching question being investigated - `hypothesis` — the core proposed solution / claim - `method_summary` — how the approach works (concise, no data leakage) - `key_contributions` — 2–5 bullet strings - `experimental_setup` — datasets, metrics, baselines, implementation notes - `results_tables` — array of `{title, headers[], rows[]}` markdown-table objects - `qualitative_observations` — free-form text blocks (what worked, what didn't, failure modes, ablation insights) - `iteration_history` — ordered list of `{iteration_id, change_description, outcome}` entries if multiple iterations are detected - `open_questions` — questions that remain unanswered in the logs Save to `workspace/ara/synthesis.json`. **If the logs contain multiple disconnected projects** (detected when `research_question` count > 1 in synthesis), pause and ask the user which project to target for the paper, or whether to combine them. --- ## Phase 4 — Formatting (deterministic) Convert `synthesis.json` into PaperOrchestra input files: ```bash python skills/agent-research-aggregator/scripts/format_po_inputs.py \ --synthesis workspace/ara/synthesis.json \ --out workspace/inputs/ ``` This generates two files: ### `workspace/inputs/idea.md` (Sparse variant) Follows the PaperOrchestra Sparse Idea format (arXiv:2604.05018, §3.1): ```markdown # [Synthesized Research Title] ## Problem <2–4 sentence problem statement derived from research_question> ## Hypothesis ## Method ## Key Contributions ## Open Questions ``` ### `workspace/inputs/experimental_log.md` Follows the PaperOrchestra Experimental Log format (App. D.3): ```markdown ## 1. Experimental Setup ## 2. Raw Numeric Data ## 3. Qualitative Observations ### Iteration History ``` After running the script, **review both files** with the user: 1. Read `workspace/inputs/idea.md` aloud and ask: "Does this accurately capture your research question and method?" 2. Read the table headers from `workspace/inputs/experimental_log.md` and ask: "Are these the correct metrics and baselines?" Revise based on feedback before proceeding to PaperOrchestra. --- ## Phase 5 — Audit Report (deterministic) ```bash python skills/agent-research-aggregator/scripts/format_po_inputs.py \ --synthesis workspace/ara/synthesis.json \ --out workspace/inputs/ \ --report workspace/ara/aggregation_report.md ``` The `--report` flag makes the script also write `aggregation_report.md`, which contains: - Number of agent caches scanned, files read, batches processed - Per-agent breakdown (files found per agent type) - Experiment records extracted (count, date range) - Iterations detected (count, convergence direction) - Data quality warnings (gaps, low-confidence extractions, conflicting numbers) - Files written and their sizes Show the report to the user. If the data quality section lists warnings, discuss them before running paper-orchestra — garbage in, garbage out. --- ## Handoff to PaperOrchestra Once the user has confirmed `idea.md` and `experimental_log.md`, the workspace is ready for the paper-orchestra pipeline. You still need: | File | Status | Action | |---|---|---| | `workspace/inputs/idea.md` | ✓ generated | user review recommended | | `workspace/inputs/experimental_log.md` | ✓ generated | user review recommended | | `workspace/inputs/template.tex` | **MISSING** | ask user to provide their conference LaTeX template | | `workspace/inputs/conference_guidelines.md` | **MISSING** | ask user to provide (page limit, deadline, formatting rules) | Tell the user exactly which two files are still needed, then offer to run `paper-orchestra` once they supply them. --- ## Error handling | Situation | Action | |---|---| | Cache directory does not exist | Skip silently; note in report | | File is binary or non-text | Skip; note in report | | File > 200 KB | Truncate at 200 KB; note in report with path | | LLM extraction returns malformed JSON | Re-prompt once with the parse error appended; if still malformed, log the batch as `status: failed` and continue | | Synthesis returns > 1 `research_question` | Pause; ask user to select or merge | | `results_tables` is empty after synthesis | Warn the user — PaperOrchestra's section-writing agent needs numeric data | --- ## Hard rules (never violate) 1. **Never write to agent cache directories.** This skill is read-only on `.claude/`, `.cursor/`, `.antigravity/`, `.openclaw/`. 2. **Never include personal information** (emails, names, credentials, API keys) in generated `idea.md` or `experimental_log.md`. The extraction prompt instructs the LLM to strip PII; double-check before handoff. 3. **Never fabricate results.** If a metric appears in only one log with low confidence, mark it `[UNVERIFIED]` in the table rather than silently including it. 4. **Never proceed past Phase 1 without user confirmation** of the discovered file list if the scan found > 50 files. --- ## Quick reference ```bash # Full run (all phases, all agents, cwd + home) python skills/agent-research-aggregator/scripts/discover_logs.py \ --search-roots . ~ --out workspace/ara/discovered_logs.json # ... (Phase 2: LLM extraction calls, see above) ... 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 call, see above) ... python skills/agent-research-aggregator/scripts/format_po_inputs.py \ --synthesis workspace/ara/synthesis.json \ --out workspace/inputs/ \ --report workspace/ara/aggregation_report.md ```