Codeg is a collaborative workspace that brings multiple AI coding agents and their sessions into one searchable application. Developers use it to coordinate agents, delegate work, and review unattended coding tasks across desktop, server, Docker, and mobile clients. The catalogue entries are skills and instructions for workflows within Codeg.
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 xintaofei/codeg --skill hypothesis-generationgit clone --depth 1 https://github.com/xintaofei/codegWrote 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/xintaofei/codeg/hypothesis-generation)<a href="https://agentmods.dev/skills/xintaofei/codeg/hypothesis-generation"><img src="https://agentmods.dev/badge/skills/xintaofei/codeg/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/xintaofei/codeg/hypothesis-generation"><img src="https://agentmods.dev/badge/skills/xintaofei/codeg/hypothesis-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.02917 |
| Opus 5.5 | $0.00028 | $0.01167 |
| Sonnet 5.5 | $0.00014 | $0.00583 |
| Haiku 4.5 | $0.00007 | $0.00292 |
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 1mo 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- hypothesis-generation — 100% identical, 0 lines differ
- hypothesis-generation — 100% identical, 0 lines differ
- hypothesis-generation — 100% identical, 0 lines differ
- hypothesis-generation — 95% identical, 4 lines differ
- hypothesis-generation — 95% identical, 2 lines differ
- hypothesis-generation — 95% identical, 4 lines differ
- hypothesis-generation — 95% identical, 5 lines differ
- hypothesis-generation — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 298 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
- Planning mechanistic studies across scientific domains
Visual Enhancement with Scientific Schematics
⚠️ MANDATORY: Every hypothesis generation report MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.
This is not optional. Hypothesis reports without visual elements are incomplete. Before finalizing any document:
- Generate at minimum ONE schematic or diagram (e.g., hypothesis framework showing competing explanations)
- Prefer 2-3 figures for comprehensive reports (mechanistic pathway, experimental design flowchart, prediction decision tree)
How to generate figures:
- Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
- Simply describe your desired diagram in natural language
- Nano Banana Pro will automatically generate, review, and refine the schematic
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
- Create publication-quality images with proper formatting
- Review and refine through multiple iterations
- Ensure accessibility (colorblind-friendly, high contrast)
- Save outputs in the figures/ directory
When to add schematics:
- Hypothesis framework diagrams showing competing explanations
- Experimental design flowcharts
- Mechanistic pathway diagrams
- Prediction decision trees
- Causal relationship diagrams
- Theoretical model visualizations
- Any complex concept that benefits from visualization
What ships with it
8 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.
- assets/FORMATTING_GUIDE.md 19 KB
- assets/hypothesis_generation.sty 7.4 KB
- assets/hypothesis_report_template.tex 20 KB
- references/experimental_design_patterns.md 14 KB
- references/hypothesis_quality_criteria.md 7.9 KB
- references/literature_search_strategies.md 20 KB
- scripts/generate_schematic_ai.py 32 KB runs code
- scripts/generate_schematic.py 5.0 KB runs code
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.
- 1mo ago First seen · 298 lines · 69 tokens per session scan A 71ca3106e958
hypothesis-generation is a skill published in the GitHub repository xintaofei/codeg (3,887 stars, last pushed 5d ago), licensed Apache-2.0. It adds 69 tokens to every session and 2,917 once invoked, about $0.0003 per session on Opus 5.5. 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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