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.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
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.mdandworkspace/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.
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.
Display the numbered project list from the discovery summary, e.g.:
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:
Read the log files in the batch (the script's --list output tells you
which file paths to read).
Apply the extraction prompt from references/extraction-prompt.md as
your system message.
Pass the raw log text as the user message.
Collect the structured JSON the LLM returns (see schema in the prompt).
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)
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:
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
<hypothesisfromsynthesis>
## Method
<method_summaryfromsynthesis>
## Key Contributions
<key_contributionsasbulletlist>
## Open Questions
<open_questions, if any>
workspace/inputs/experimental_log.md
Follows the PaperOrchestra Experimental Log format (App. D.3):
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
Log as data quality warning in audit report; do not re-ask for project (was selected in Phase 1.5)
results_tables is empty after synthesis
Warn the user — PaperOrchestra's section-writing agent needs numeric data
Hard rules (never violate)
Never write to agent cache directories. This skill is read-only on .claude/, .cursor/, .antigravity/, .openclaw/.
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.
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.
Never proceed past Phase 1 without user confirmation of the discovered file list if the scan found > 50 files.
