agent-research-aggregator

# 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 <roots> \
    --agents <agents> \
    --depth <depth> \
    --since <since> \
    --out workspace/ara/discovered_logs.json
```

The script exits with code **2** when no `--project` filter is set (this is
expected on the first run). It prints a **"Projects found"** list to stdout —
show it to the user immediately.

**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.

---

## Phase 1.5 — Project Selection (mandatory)

**A paper can only be written from a single project. You must ask the user
which project to use before any LLM processing begins.**

1. Display the numbered project list from the discovery summary, e.g.:
   ```
   Projects found:
     [1] /home/alice/projects/my-rl-experiment  (42 files)
     [2] /home/alice/projects/llm-eval-suite    (17 files)
     [3] /home/alice/projects/old-demo          (3 files)
   ```
2. Ask: *"Which project should this paper be based on? Please choose a number
   or paste the project path."*
3. **Do not proceed to Phase 2 until the user has answered.**
4. Re-run discovery with the chosen project to filter the manifest:

```bash
python skills/agent-research-aggregator/scripts/discover_logs.py \
    --search-roots <roots> \
    --agents <agents> \
    --depth <depth> \
    --since <since> \
    --project "<chosen project path>" \
    --out workspace/ara/discovered_logs.json
```

This overwrites `discovered_logs.json` so only the selected project's files
remain. The script exits 0 on success.

**If the discovery finds only one project:** skip the question and inform the
user: *"Only one project found: `<path>`. Using it for the paper."* — then
re-run with `--project` automatically.

**If the discovery summary shows irrelevant files after filtering:** 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 \
    --dis