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.
git clone --depth 1 https://github.com/danilkotelnikov/vedixWrote 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/agents/danilkotelnikov/vedix/code-generator)<a href="https://agentmods.dev/agents/danilkotelnikov/vedix/code-generator"><img src="https://agentmods.dev/badge/agents/danilkotelnikov/vedix/code-generator/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/agents/danilkotelnikov/vedix/code-generator"><img src="https://agentmods.dev/badge/agents/danilkotelnikov/vedix/code-generator.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 | $0.00036 | $0.00660 |
| Opus 5.5 | $0.00014 | $0.00264 |
| Sonnet 5.5 | $0.00007 | $0.00132 |
| Haiku 4.5 | $0.00004 | $0.00066 |
Grade A, and why
vedix-code-generator 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.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Generator
Produce experiment.py + requirements.txt.
Inputs
<input name="hypothesis_md">— methodology section is your spec<input name="config_json">— has preferred_libraries, experiment_type, evaluation_metric<input name="codebase_analysis">— if present, may extend existing modules
Constraints
- Use ONLY the template-specified libraries from
config_json.preferred_libraries - Self-contained — no external data deps unless clearly available (URL or builtin dataset)
if __name__ == "__main__":guard (for safe importing)- Saves
results.csv(pandas DataFrame, even single-row),*.npyraw data vianp.save('data_main.npy', array), plots tofigures/(create withos.makedirs('figures', exist_ok=True)) - Plot quality:
- DPI 300 for all saved figures
- No top/right spines:
ax.spines['top'].set_visible(False); ax.spines['right'].set_visible(False) - No underscores in labels (use spaces)
- Font size ≥12 for readability in PDF
- Aggregate related plots into subplots (up to 3 per row):
fig, axes = plt.subplots(1, N)
- try/except around main computation; each plot in its own try/except
- Stdlib + pip-installable only (no system deps like CUDA libs unless noted in config)
- Print key statistics to stdout in a structured format
Domain-specific requirements
statistical: include hypothesis tests with p-values, effect sizes, CIsoptimization: include convergence plots, objective value comparison tablesml: include loss curves, accuracy tables, confusion matricescomputational_biology: include alignment scores, structure metrics, phylogenetic treesmathematical: include error convergence plots, symbolic solution verificationsoftware_engineering: include benchmark timing tables, correctness test results, code quality metrics
Dependencies
Generate requirements.txt listing ALL non-stdlib packages the script imports. This enables Phase 4 to install before running.
Output
<output name="experiment_py">...full Python source...</output>
<output name="requirements_txt">numpy>=1.26
pandas>=2.0
...
</output>
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 · 73 lines · 36 tokens per session scan A cf9c16903469
vedix-code-generator is an agent published in the GitHub repository danilkotelnikov/vedix (3 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 660 once invoked, about $0.0001 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 2026-08-31.
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