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 Leeroo-AI/superml --skill ml-experimentgit clone --depth 1 https://github.com/Leeroo-AI/supermlWrote 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/leeroo-ai/superml/ml-experiment)<a href="https://agentmods.dev/skills/leeroo-ai/superml/ml-experiment"><img src="https://agentmods.dev/badge/skills/leeroo-ai/superml/ml-experiment.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 | $0.00030 | $0.01217 |
| Opus 5 | $0.00015 | $0.00609 |
| Sonnet 5 | $0.00006 | $0.00243 |
| Haiku 4.5 | $0.00003 | $0.00122 |
Grade A, and why
ml-experiment 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 7d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Journal
Externalize your experimental reasoning. Every ML project is a sequence of hypotheses tested — this skill makes that sequence visible, persistent, and learnable.
The Iron Law
NO NEW EXPERIMENT WITHOUT LOGGING THE HYPOTHESIS FIRST
If you're about to change a hyperparameter, swap a dataset, try a new architecture, or modify a training recipe — write down what you expect to happen and why BEFORE running it. This is how you learn from experiments instead of just running them.
File Structure
Maintain these files in the project root (create if they don't exist):
experiments/
├── journal.md — Running experiment log (append-only)
└── lessons.md — Distilled patterns and rules (curated)
Phases
Phase 1: Before Any Experiment — Log the Hypothesis
Before changing anything or running anything new:
- Read
experiments/journal.md(if it exists) to see what's been tried - Write a new entry:
### YYYY-MM-DD HH:MM — [Experiment Name]
**Status**: PLANNED
**Hypothesis**: [What you expect to happen and why]
**Change**: [Exactly what's being modified — one variable at a time]
**Config**:
- key_param_1: old_value → new_value
- key_param_2: value (unchanged)
**Expected outcome**: [Specific metric target or qualitative expectation]
**Baseline**: [Current best metric to beat]
Gate: Entry is written before any code runs. No exceptions.
Phase 2: After the Experiment — Log the Result
Once results are in:
- Update the journal entry:
**Status**: COMPLETED
**Actual outcome**: [What actually happened — metrics, behavior]
**Delta**: [How this compared to expectation — better/worse/different than expected]
**Duration**: [Wall time, GPU hours]
**Learning**: [One sentence — what this taught you]
**Next**: [What to try based on this result]
Gate: Result is logged before starting the next experiment.
Phase 3: Before the Next Iteration — Review History
Before proposing or starting the next experiment:
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.
- 7d ago First seen · 142 lines · 30 tokens per session scan A d66bbaf2a3f3
ml-experiment is a skill published in the GitHub repository Leeroo-AI/superml (194 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 30 tokens to every session and 1,217 once invoked, about $0.0002 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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