hypothesis-gen
Use when the user wants to generate and literature-vet a pool of novel, testable research hypotheses for a question or domain. A multi-agent loop: a Generator proposes candidate hypotheses, a LiteratureScout grounds each in real retrieved literature (already known? closest prior work? what gap does it fill?), and a Judge scores them against a fixed rubric and keeps the strong, non-duplicate ones; rounds repeat — mutating toward the open gaps — until fresh rounds stop adding keepers. Not for sharpening or decomposing a research question (no grounding/scoring there), and not for grading an exist
npx skills add gaasher/Agent-Loop-Skills --skill hypothesis-gen --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.
# Hypothesis Generation Loop A **multi-agent, literature-grounded** generation loop. The artifact is a growing **pool of hypotheses**; the feedback signal is the count of **strong, distinct hypotheses** that clear the bar — where "strong" is decided against real retrieved literature, not assertion. Each round: **generate → ground → judge → keep → mutate toward the gaps**, until the pool stops growing (saturation). The discipline: a hypothesis enters the pool only if the literature says it is **not already established** (novelty), prior work makes it **plausible** (grounding), and a **feasible test** exists. Generating is not confirming — the output is a ranked set of strong *candidates to test*, each stated with how to test it. The cast (all in `roles/`): - `roles/Generator.md` — proposes a batch of candidate hypotheses aimed at the open gaps. - `roles/LiteratureScout.md` — grounds each candidate in real literature (novelty · support · gap); emits `litscout.json` (validates `schemas/litscout.schema.json`). - `roles/Judge.md` — scores each against the **fixed rubric** and decides keep/kill/dedupe; emits `verdict.json` (validates `schemas/verdict.schema.json`). **Spawn-or-degrade.**
- When to use
- Setup
- The loop
- Ledger
- Constraints
- Stops
What does the hypothesis-gen skill do?
Use when the user wants to generate and literature-vet a pool of novel, testable research hypotheses for a question or domain. A multi-agent loop: a Generator proposes candidate hypotheses, a LiteratureScout grounds each in real retrieved literature (already known? closest prior work? what gap does it fill?), and a Judge scores them against a fixed rubric and keeps the strong, non-duplicate ones; rounds repeat — mutating toward the open gaps — until fresh rounds stop adding keepers. Not for sharpening or decomposing a research question (no grounding/scoring there), and not for grading an exist
How do I install it?
Run `npx skills add gaasher/Agent-Loop-Skills --skill hypothesis-gen --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 gaasher/Agent-Loop-Skills, a repository with 146 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.