agent-research-aggregator

Should I run? (decision gate)

Before starting Phase 1, check whether aggregation is actually needed:

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

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:

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)

1. Ask: *"Which project should this paper be based on? Please choose a number

or paste the project path."*

1. Do not proceed to Phase 2 until the user has answered.
2. Re-run discovery with the chosen project to filter the manifest:

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).

1. Apply the extraction prompt from references/extraction-prompt.md as

your system message.

1. Pass the raw log text as the user message.
2. Collect the structured JSON the LLM returns (see schema in the prompt).
3. Append to workspace/ara/raw_experiments.json.

After all batches:

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:

<raw_experiments>
{contents of workspace/ara/raw_experiments.json}
</raw_experiments>

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.

Note: By this point, the user has already selected a single project in Phase 1.5. The synthesis should represent one coherent research thread. If the LLM still surfaces multiple disconnected research questions, flag this as a data quality warning in the audit report (Phase 5) but do not re-ask for project selection — that decision was made earlier.

Phase 4 — Formatting (deterministic)

Convert synthesis.json into PaperOrchestra input files:

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):

# [Synthesized Research Title]

## Problem
<2–4 sentence problem statement derived from research_question>

## Hypothesis
<hypothesis from synthesis>

## Method
<method_summary from synthesis>

## Key Contributions
<key_contributions as bullet list>

## Open Questions
<open_questions, if any>

workspace/inputs/experimental_log.md

Follows the PaperOrchestra Experimental Log format (App. D.3):

## 1. Experimental Setup
<experimental_setup from synthesis, formatted as prose + sub-bullets>

## 2. Raw Numeric Data
<results_tables converted to GitHub-Flavored Markdown tables>

## 3. Qualitative Observations
<qualitative_observations from synthesis>

### Iteration History
<iteration_history as an ordered narrative, if present>

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?"

1. 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)

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:

Tell the user exactly which two files are still needed, then offer to run paper-orchestra once they supply them.

Error handling

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

# Phase 1: discover all projects (exits with code 2 — project selection required)
python skills/agent-research-aggregator/scripts/discover_logs.py \
    --search-roots . ~ --out workspace/ara/discovered_logs.json

# Phase 1.5: re-run with chosen project (exits 0)
python skills/agent-research-aggregator/scripts/discover_logs.py \
    --search-roots . ~ \
    --project "/home/user/projects/my-chosen-project" \
    --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