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discover
Use when user wants to test multiple angles on an idea, run parallel hypothesis lanes, or explore a gap from different entry points.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Use when user wants to test multiple angles on an idea, run parallel hypothesis lanes, or explore a gap from different entry points.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use when user mentions autonomous iteration, metric-driven optimization, $research-loop plan, $research-loop debug, $research-loop fix, $research-loop security, $research-loop ship, $research-loop scenario, $research-loop predict, $research-loop learn, $research-loop reason, $research-loop probe, or mentions "research-loop" with a goal/metric. Autonomous Goal-directed Iteration — apply Karpathy's autoresearch principles: modify, verify, keep/discard, repeat. Supports bounded mode via Iterations: N inline config.
Mandatory activation layer — loads on any conversation start. Establishes skill-loading protocol, Red Flags, priority rules, and HARD-GATE enforcement for all research-loop skills.
Run a thorough, multi-phase deep research investigation on a topic with subagent dispatch, provenance tracking, and integrity verification.
Experiment sandbox execution for Research Loop. Supports four modes: local (venv), Docker (isolated containers), SSH remote (GPU compute on servers), and Colab (Google Drive bridge). Provides experiment harness templates, code validation, metric collection, deterministic seeding, and compute budget enforcement. Use before running experiments generated by the paper-pipeline.
Use when user wants to explore a topic, find papers, map a field, or understand the research landscape. Not for explaining concepts — use learn for that.
Publication-quality figure generation for research papers. Decision agent selects figure type (code plot vs architecture diagram). Generates Matplotlib/Seaborn code for quantitative figures with iterative improvement loop. Style-matches conference templates (NeurIPS, ICML, ICLR). Use when the paper-pipeline reaches the figure generation phase, or when a user requests figures for an existing draft.
| name | discover |
| description | Use when user wants to test multiple angles on an idea, run parallel hypothesis lanes, or explore a gap from different entry points. |
Read the active lab_notebook.md to understand what was already explored and which gap was selected.
If no session exists:
"Run
/explore <topic>first to build the foundation, then come back here."
Say:
"Spinning up 4 parallel research lanes for [topic]. Each explores a different angle. I'll apply Carlini gates between stages and kill weak lanes early."
Append to lab_notebook.md:
## Discovery Run
Date: <date>
Topic: <topic>
Gap pursuing: <selected gap from idea-selection>
### Lanes launched
- Lane 1: [angle description]
- Lane 2: [angle description]
- Lane 3: [angle description]
- Lane 4: [angle description]
Spawn all 4 simultaneously. Each prompt must be long and task-shaped — start with "You are a research search agent. Do not ask questions. Search and return structured results immediately."
Agent 1 prompt:
You are a research search agent. Do not ask questions. Search and return structured results immediately.
Task: Find the most incremental improvement on SOTA for [topic].
Search for 3 recent papers (2022–2025) that represent the current frontier.
Then identify: what is the single most direct gap left open by these papers?
Propose a hypothesis that closes it. Suggest a concrete experiment (what to change, what metric).
Estimate feasibility: can it be tested in under 1 week on 1 GPU?
Return: papers (title, year, 1-line contribution), gap, hypothesis, experiment, feasibility score 0–1.
Agent 2 prompt:
You are a research search agent. Do not ask questions. Search and return structured results immediately.
Task: Find a cross-field transfer opportunity for [topic] from an adjacent field.
Search for 3 papers from a neighboring discipline (e.g. neuroscience→ML, physics→biology, etc.) that contain ideas not yet applied to [topic].
Identify the most promising transfer. Propose a hypothesis. Suggest a concrete experiment.
Return: papers (title, year, 1-line contribution), gap, hypothesis, experiment, feasibility score 0–1.
Agent 3 prompt:
You are a research search agent. Do not ask questions. Search and return structured results immediately.
Task: Find a core assumption in [topic] that the field takes for granted but may be wrong.
Search for 3 papers that either challenge this assumption or would be undermined if it were false.
State the assumption clearly. Propose the counter-hypothesis. Suggest a falsification experiment.
Return: papers (title, year, 1-line contribution), assumption, counter-hypothesis, experiment, feasibility score 0–1.
Agent 4 prompt:
You are a research search agent. Do not ask questions. Search and return structured results immediately.
Task: Find a systems/efficiency angle for [topic] — how to make it work on constrained hardware or at scale.
Search for 3 papers on efficient implementations, approximations, or hardware-aware designs in [topic].
Identify the key bottleneck. Propose a hypothesis for removing it. Suggest a concrete experiment.
Return: papers (title, year, 1-line contribution), bottleneck, hypothesis, experiment, feasibility score 0–1.
For each lane result, score it (0.0–1.0) on:
Gate threshold: 0.5. Kill lanes below threshold.
Append all results to lab_notebook.md:
### Lane Results
#### Lane 1 — [angle]
Claim: <hypothesis>
Experiment: <what to change and measure>
Gate score: <X.XX>
Status: <survived / killed>
Kill reason (if killed): <why>
Papers found: <list>
#### Lane 2 — [angle]
...
For each surviving lane, show a card:
Lane [N]: [angle name] (score: X.XX) Claim: [one sentence — what you're testing] Experiment: [one sentence — what to change, what metric to watch] Why it survived: [one sentence]
Then:
"Which lane do you want to pursue? You can also ask me to dig deeper into any before deciding."
Append their choice:
### Selected Lane
Lane: <N>
Angle: <angle>
Claim: <full hypothesis>
Experiment: <full experiment description>
Researcher notes: <anything they said>
Update status:
## Status
<date>: Discovery complete. Selected lane: <N>. Next: /loop to start experiments.
"Good choice. Now set up the experiment. Run
/looponce your baseline repo is ready.Your hypothesis is saved in the lab notebook. To resume later:
/resume"