internal/embed/claude/skills/idea-selection/SKILL.md
Use when user wants to find gaps, evaluate ideas, or decide what's worth pursuing. Triggered by "what hasn't been tried", "is this a good idea", "find the gap".
npx skillsauth add moralespanitz/research-loop idea-selectionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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"The single most important skill is good taste in what problems are worth solving." — Nicholas Carlini
Check if a lab notebook exists:
ls .research-loop/sessions/
If yes, read the most recent one to understand context. If no, ask:
"What topic are we evaluating? (Or run
/explore <topic>first to build the foundation.)"
Spawn these 3 simultaneously:
Agent 1: What has NOT been tried in [topic]? Find explicit "open problem" or "future work" statements.
Agent 2: What methods from adjacent fields haven't been applied to [topic]?
Agent 3: What assumptions does the [topic] literature make that might be wrong?
Save raw results to lab_notebook.md:
## Gap Analysis (raw)
### Untried approaches
<agent 1 results>
### Adjacent field transfers
<agent 2 results>
### Questionable assumptions
<agent 3 results>
Then present top 3 gaps as options:
"I found 3 gaps worth considering:
1. [Gap name] — [one sentence]. Feasibility risk: [one sentence]. 2. [Gap name] — [one sentence]. Feasibility risk: [one sentence]. 3. [Gap name] — [one sentence]. Feasibility risk: [one sentence].
Which one resonates? Or a different angle?"
Append their choice:
## Selected Gap
Gap: <their choice>
Why they chose it: <their reasoning>
Ask one question at a time. Wait for each answer. Score internally (don't reveal yet).
Q1 — Taste:
"First — be honest: if someone solved this completely, would the field look meaningfully different? Or is this more of a nice-to-have?"
Append:
## Carlini Gate
### Taste (weight 0.30)
Question: Would solving this meaningfully change the field?
Researcher answer: <their answer>
Score: <0.0-1.0>
Reasoning: <why that score>
Q2 — Uniqueness:
"What can YOU specifically bring to this that others can't? Your background, timing, how you're framing it — what's your edge?"
Append:
### Uniqueness (weight 0.25)
Question: What is your comparative advantage?
Researcher answer: <their answer>
Score: <0.0-1.0>
Reasoning: <why that score>
Q3 — Impact:
"Write the best-case conclusion right now. If every experiment works perfectly — what does the paper say? Not 'X% improvement' — what changes about how people think?"
Append:
### Impact (weight 0.30)
Question: What is the best-case conclusion?
Researcher answer: <their answer>
Score: <0.0-1.0>
Reasoning: <why that score>
Q4 — Feasibility:
"Can you test the core claim with a single GPU in under a week? Describe the exact experiment."
Append:
### Feasibility (weight 0.15)
Question: What is the exact experiment?
Researcher answer: <their answer>
Score: <0.0-1.0>
Reasoning: <why that score>
Calculate and show:
Taste: X.XX × 0.30 = X.XX
Uniqueness: X.XX × 0.25 = X.XX
Impact: X.XX × 0.30 = X.XX
Feasibility: X.XX × 0.15 = X.XX
─────────────────────────────────
Overall: X.XX
Append to lab_notebook.md:
### Final Score
Taste: X.XX | Uniqueness: X.XX | Impact: X.XX | Feasibility: X.XX
Overall: X.XX
Verdict: <promising / conditional / skip>
Weakest axis: <which one and why>
If ≥ 0.70:
"Strong signal. Ready to run parallel discovery lanes? →
/discover"
If 0.50–0.69:
"Promising but [weakest axis] is the weak point. [One sentence on what would strengthen it.] Proceed anyway, or work on that first?"
If < 0.50:
"Honest take: not strong enough yet. Main problem is [weakest axis]. Want to try a different gap, or reframe the approach?"
Update lab_notebook.md status:
## Status
<date>: Carlini gate result: <verdict>. Next: <what to do>
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.