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 yeaight7/agent-powerups --skill reproducible-training-runsgit 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/skills/yeaight7/agent-powerups/reproducible-training-runs)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/reproducible-training-runs"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/reproducible-training-runs/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/yeaight7/agent-powerups/reproducible-training-runs"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/reproducible-training-runs.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 | $0.00044 | $0.00446 |
| Opus 5.5 | $0.00018 | $0.00178 |
| Sonnet 5.5 | $0.00009 | $0.00089 |
| Haiku 4.5 | $0.00004 | $0.00045 |
Grade A, and why
reproducible-training-runs 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
Purpose
Enforce seed setting, deterministic operations, and environment tracking so a training run can be reproduced exactly.
When to Use
- Reviewing or modifying a training script that must be deterministic
- Two runs with the "same" configuration produced different results
- A past result needs to be reconstructed exactly
Inputs
- A target Python training script
Workflow
- Global seed initialization: ensure a single function sets seeds for all relevant libraries (
random,numpy,torch,tensorflow). - Deterministic algorithms: for PyTorch or TensorFlow, check that deterministic algorithms are enabled (e.g.,
torch.use_deterministic_algorithms(True)). - Data loading: verify data loaders use deterministic shuffling and that worker processes are seeded correctly to avoid identical augmentations.
- Environment & config tracking: ensure the script logs the exact configuration, dependency versions, and data hashes.
- Review first: point out missing reproducibility guards before rewriting the script. Provide the exact seed-initialization snippet — do not hide side effects.
Output
- A list of missing reproducibility guards with the exact code snippets to add, plus any performance trade-off warnings
Verification
- All library seeds set from one place
- Deterministic-algorithm flags enabled (or the gap explicitly accepted)
- Loader shuffling and worker seeding deterministic
- Configuration, dependency versions, and data hashes logged
- User warned if determinism flags significantly slow training
Failure Modes
- Partial seeding — seeding
randombut not the framework or loader workers still yields nondeterminism. - Silent slowdown — enabling deterministic algorithms can cost real training speed; surface the trade-off instead of hiding it.
- Rewriting before reviewing — changing the script without first listing the gaps loses the audit trail.
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 · 45 lines · 44 tokens per session scan A 06e1c09aea6c
reproducible-training-runs is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 19d ago), licensed Apache-2.0. It adds 44 tokens to every session and 446 once invoked, about $0.0002 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 2026-09-14.
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