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/yeaight7/agent-powerupsWrote 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/commands/yeaight7/agent-powerups/ml-run-review)<a href="https://agentmods.dev/commands/yeaight7/agent-powerups/ml-run-review"><img src="https://agentmods.dev/badge/commands/yeaight7/agent-powerups/ml-run-review/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/commands/yeaight7/agent-powerups/ml-run-review"><img src="https://agentmods.dev/badge/commands/yeaight7/agent-powerups/ml-run-review.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 | $0.00000 | $0.00180 |
| Opus 5.5 | $0.00000 | $0.00072 |
| Sonnet 5.5 | $0.00000 | $0.00036 |
| Haiku 4.5 | $0.00000 | $0.00018 |
Grade A, and why
ml-run-review 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.
What it actually says
ml-run-review
Review a machine learning training run or experiment configuration for soundness, reproducibility, and evaluation rigor.
Prerequisites
Provide the agent with the training script, experiment configuration, or metric logs you want reviewed.
Review Focus
- Evaluation Rigor: Is there a proper holdout set? Is the cross-validation strategy appropriate (e.g., time-based splitting for time series)?
- Data Leakage Check: Are preprocessing steps (scaling, imputing) fit only on the training set?
- Reproducibility: Are random seeds fixed? Are hyperparameters explicitly logged?
- Metrics: Do the chosen metrics align with the stated goal? Are baselines established?
Output
The agent will provide a brief markdown summary of identified risks and propose specific, targeted code fixes. The agent will not make changes without your explicit approval.
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 · 18 lines · 0 tokens per session scan A fe1425a4967e
ml-run-review is a command published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 19d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 180 tokens. 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 2026-09-14.
Other commands, from other repositories
prompt-optimize
Legacy slash-entry shim for the prompt-optimizer skill. Prefer the skill directly.
improve-prompt
Analyze and enhance vague or incomplete prompts before execution.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.