Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/gaasher/agent-loop-skills/hypothesis-gennpx skills add gaasher/Agent-Loop-Skills --skill hypothesis-gengit clone --depth 1 https://github.com/gaasher/Agent-Loop-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gaasher/agent-loop-skills/hypothesis-gen)<a href="https://agentmods.dev/skills/gaasher/agent-loop-skills/hypothesis-gen"><img src="https://agentmods.dev/badge/skills/gaasher/agent-loop-skills/hypothesis-gen.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00129 | $0.02746 |
| Opus 5 | $0.00064 | $0.01373 |
| Sonnet 5 | $0.00026 | $0.00549 |
| Haiku 4.5 | $0.00013 | $0.00275 |
Grade A, and why
hypothesis-gen scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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); emitslitscout.json(validatesschemas/litscout.schema.json).roles/Judge.md— scores each against the fixed rubric and decides keep/kill/dedupe; emitsverdict.json(validatesschemas/verdict.schema.json).
Spawn-or-degrade. On Claude Code, spawn Generator / LiteratureScout / Judge as real Agent
subagents each round; otherwise adopt each role inline in this context. You are the orchestrator.
When to use
Use when the user wants candidate hypotheses generated and vetted for a question or domain. Default: run the full generate→ground→judge loop below until saturation. Escape hatch: if the user only wants a single batch (no looping), run one round and report the kept hypotheses. Not for sharpening or decomposing a question, and not for grading an existing written proposal.
Setup
Resolve bindings interactively. If loop.run.yaml exists in the working dir, load it, confirm the
values in one line, and skip to the loop. Otherwise: on Claude Code (the AskUserQuestion tool is
available) infer a likely value for each binding and present it as the recommended option; on other
hosts ask each as a quoted plain-text prompt. Then write loop.run.yaml (format:
examples/run.example.yaml) and confirm every value plus the live/degraded literature tier before
creating any other files.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 170 lines · 129 tokens per session scan A 3cf00a47cef4
hypothesis-gen is a skill published in the GitHub repository gaasher/Agent-Loop-Skills (163 stars, last pushed 2mo ago), licensed MIT. It adds 129 tokens to every session and 2,746 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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