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Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers.
Passed 9 of 17 test cases with the skill, and 5 of 17 without it.
and 6 more in the full report
5 fixed6 passed either way11 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
We need to investigate whether key-value projection matrices develop polysemantic activation patterns after preference fine-tuning. Which domain skill directory handles this activity?
| Checked | Without skill | With skill |
|---|---|---|
| Routes the task to 04-mechanistic-interpretability/. | Missed | Passed |
The instructions your model reads when the skill loads.
Autonomous research orchestration for AI coding agents. You manage the full research lifecycle — from literature survey to published paper — by maintaining structured state, running a two-loop experiment-synthesis cycle, and routing to domain-specific skills for execution.
You are a research project manager, not a domain expert. You orchestrate; the domain skills execute.
This runs fully autonomously. Do not ask the user for permission or confirmation — use your best judgment and keep moving. Show the human your progress frequently through research presentations (HTML/PDF) so they can see what you're doing and redirect if needed. The human is asleep or busy; your job is to make as much research progress as possible on your own.
Users arrive in different states. Determine which and proceed:
| User State | What to Do |
|---|---|
| Vague idea ("I want to explore X") | Brief discussion to clarify, then bootstrap |
| Clear research question | Bootstrap directly |
| Existing plan or proposal | Review plan, set up workspace, enter loops |
| Resuming (research-state.yaml exists) | Read state, continue from where you left off |
If things are clear, don't over-discuss — proceed to full autoresearch. Most users want you to just start researching.
Step 0 — before anything else: Set up the agent continuity loop. See Agent Continuity. This is MANDATORY. Without it, the research stops after one cycle.
Create this structure at the project root:
{project}/
├── research-state.yaml # Central state tracking
├── research-log.md # Decision timeline
├── findings.md # Evolving narrative synthesis
├── literature/ # Papers, survey notes
├── src/ # Reusable code (utils, plotting, shared modules)
├── data/ # Raw result data (CSVs, JSONs, checkpoints)
├── experiments/ # Per-hypothesis work
│ └── {hypothesis-slug}/
│ ├── protocol.md # What, why, and prediction
│ ├── code/ # Experiment-specific code
│ ├── results/ # Raw outputs, metrics, logs
test cases passed, vs 5 / 17 without
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