Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/dralkh/iktinahnpx agentmods add skills/dralkh/iktinah/hypogenicWrote 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/dralkh/iktinah/hypogenic)<a href="https://agentmods.dev/skills/dralkh/iktinah/hypogenic"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/hypogenic/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/dralkh/iktinah/hypogenic"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/hypogenic.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 | $0.00069 | $0.04949 |
| Opus 5 | $0.00034 | $0.02475 |
| Sonnet 5 | $0.00014 | $0.00990 |
| Haiku 4.5 | $0.00007 | $0.00495 |
Grade A, and why
hypogenic 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 8d 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
92% identical to hypogenic — 10 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 — 655 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hypogenic
Overview
Hypogenic provides automated hypothesis generation and testing using large language models to accelerate scientific discovery. The framework supports three approaches: HypoGeniC (data-driven hypothesis generation), HypoRefine (synergistic literature and data integration), and Union methods (mechanistic combination of literature and data-driven hypotheses).
Quick Start
Get started with Hypogenic in minutes:
# Install the package
uv pip install hypogenic
# Clone example datasets
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data
# Run basic hypothesis generation
hypogenic_generation --config ./data/your_task/config.yaml --method hypogenic --num_hypotheses 20
# Run inference on generated hypotheses
hypogenic_inference --config ./data/your_task/config.yaml --hypotheses output/hypotheses.json
Or use Python API:
from hypogenic import BaseTask
# Create task with your configuration
task = BaseTask(config_path="./data/your_task/config.yaml")
# Generate hypotheses
task.generate_hypotheses(method="hypogenic", num_hypotheses=20)
# Run inference
results = task.inference(hypothesis_bank="./output/hypotheses.json")
When to Use This Skill
Use this skill when working on:
- Generating scientific hypotheses from observational datasets
- Testing multiple competing hypotheses systematically
- Combining literature insights with empirical patterns
- Accelerating research discovery through automated hypothesis ideation
- Domains requiring hypothesis-driven analysis: deception detection, AI-generated content identification, mental health indicators, predictive modeling, or other empirical research
Key Features
Automated Hypothesis Generation
- Generate 10-20+ testable hypotheses from data in minutes
- Iterative refinement based on validation performance
- Support for both API-based (OpenAI, Anthropic) and local LLMs
Literature Integration
- Extract insights from research papers via PDF processing
- Combine theoretical foundations with empirical patterns
- Systematic literature-to-hypothesis pipeline with GROBID
What ships with it
1 file 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.
- 8d ago First seen · 655 lines · 69 tokens per session scan A b634b456ec76
hypogenic is a skill published in the GitHub repository dralkh/iktinah (77 stars, last pushed 2mo ago), licensed MIT. It adds 69 tokens to every session and 4,949 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to hypogenic, differing in 10 lines, and is treated as a copy.
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