Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 skills add foryourhealth111-pixel/Vibe-Skills --skill hypothesis-generationgit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-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/foryourhealth111-pixel/vibe-skills/hypothesis-generation)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/hypothesis-generation"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/hypothesis-generation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/hypothesis-generation"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/hypothesis-generation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00073 | $0.02539 |
| Opus 5 | $0.00036 | $0.01269 |
| Sonnet 5 | $0.00015 | $0.00508 |
| Haiku 4.5 | $0.00007 | $0.00254 |
Grade A, and why
hypothesis-generation 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 9d 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.
This is a copy
83% identical to hypothesis-generation — 54 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientific Hypothesis Generation
Overview
Hypothesis generation is a systematic process for developing testable explanations. Formulate evidence-based hypotheses from observations, design experiments, explore competing explanations, and develop predictions. Apply this skill for scientific inquiry across domains.
When to Use This Skill
This skill should be used when:
- Developing hypotheses from observations or preliminary data
- Designing experiments to test scientific questions
- Exploring competing explanations for phenomena
- Formulating testable predictions for research
- Conducting literature-based hypothesis generation
- Running a lightweight HypoGeniC-style workflow when the user explicitly asks for automated hypothesis generation/testing
- Planning mechanistic studies across scientific domains
Outputs
Produce a structured hypothesis report when useful:
- Core observation or anomaly to explain
- Competing mechanistic hypotheses
- Testable predictions for each hypothesis
- Minimal validation experiments or studies
- Decision criteria for supporting, revising, or rejecting each hypothesis
Figures or diagrams may be included when they clarify the hypothesis structure, but this skill does not require a second skill or helper expert to create them.
Workflow
Follow this systematic process to generate robust scientific hypotheses:
1. Understand the Phenomenon
Start by clarifying the observation, question, or phenomenon that requires explanation:
- Identify the core observation or pattern that needs explanation
- Define the scope and boundaries of the phenomenon
- Note any constraints or specific contexts
- Clarify what is already known vs. what is uncertain
- Identify the relevant scientific domain(s)
2. Conduct Comprehensive Literature Search
Search existing scientific literature to ground hypotheses in current evidence. Use both PubMed (for biomedical topics) and general web search (for broader scientific domains):
For biomedical topics:
- Use WebFetch with PubMed URLs to access relevant literature
- Search for recent reviews, meta-analyses, and primary research
- Look for similar phenomena, related mechanisms, or analogous systems
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.
- 9d ago First seen · 268 lines · 73 tokens per session scan A 17739ba3f71f
hypothesis-generation is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 12d ago), licensed Apache-2.0. It adds 73 tokens to every session and 2,539 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to hypothesis-generation, differing in 54 lines, and is treated as a copy.
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