deltasci
Two-perspective co-reasoning for AI4Science hypothesis generation. Runs a structured 4-round dialogue between a domain scientist (parameterized by a domain pack) and an ML engineer, producing a grounded, falsifiable research hypothesis that is honest about the AI's training-distribution edges. Domain-agnostic via pluggable packs (biomed, materials, climate, or your own). Use when a researcher has a vague idea and wants to turn it into a defensible, evaluable hypothesis with explicit handoffs for the things only the researcher can know.
Profile →npx skills add boheling/deltasci --skill skill --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# DeltaScience: Two-Perspective Co-Reasoning ## Purpose Generate AI4Science research hypotheses that are: 1. **Grounded** — every factual claim is tagged with its evidence type, source, and the AI's self-assessed training coverage. 2. **Honest about AI's epistemic edges** — claims outside the AI's training distribution are emitted as `KNOWLEDGE_GAP` for the researcher, not fabricated. 3. **Explici
What does the deltasci skill do?
Two-perspective co-reasoning for AI4Science hypothesis generation. Runs a structured 4-round dialogue between a domain scientist (parameterized by a domain pack) and an ML engineer, producing a grounded, falsifiable research hypothesis that is honest about the AI's training-distribution edges. Domain-agnostic via pluggable packs (biomed, materials, climate, or your own). Use when a researcher has a vague idea and wants to turn it into a defensible, evaluable hypothesis with explicit handoffs for the things only the researcher can know.
How do I install it?
Run `npx skills add boheling/deltasci --skill skill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From boheling/deltasci, a repository with 143 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.