internal/embed/claude/skills/discover/SKILL.md
Use when user wants to test multiple angles on an idea, run parallel hypothesis lanes, or explore a gap from different entry points.
npx skillsauth add moralespanitz/research-loop discoverInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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"
testing
Plan and execute a structured replication workflow for a paper, claim, or benchmark with environment selection and integrity checks.
testing
End-to-end paper generation pipeline ported from AutoResearchClaw (Aiming Lab). 14 phases covering topic initiation through export/publish, with human- in-the-loop gates and quality gating at each handoff. Use this when the user wants a full paper pipeline run — topic to submission-ready manuscript. Delegates to researcher/reviewer/writer/verifier subagents for stage execution and to autonomous-iteration for experiment optimization loops.
testing
Run a structured literature review on a topic using parallel search, evidence tables with quality scoring, and primary-source synthesis.
development
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.