Borrowing it
Nothing to install: this file belongs to cybertronai/SutroYaro. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cybertronai/SutroYaro/main/.claude/skills/run-experiment/SKILL.mdgit clone --depth 1 https://github.com/cybertronai/SutroYaroWrote 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/cybertronai/sutroyaro/run-experiment)<a href="https://agentmods.dev/skills/cybertronai/sutroyaro/run-experiment"><img src="https://agentmods.dev/badge/skills/cybertronai/sutroyaro/run-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 | $0.00021 | $0.00687 |
| Opus 5 | $0.00010 | $0.00344 |
| Sonnet 5 | $0.00004 | $0.00137 |
| Haiku 4.5 | $0.00002 | $0.00069 |
Grade A, and why
run-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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Experiment
Experiment types
New method: not in the registry (search_space.yaml). Create a new experiment file from the template. Add the method to the registry if it works.
Existing method, new config: method is in the registry but you're testing a different configuration (different n, k, hyperparameters). Use the existing experiment code or copy and modify.
Either way, the steps and output format are the same.
Steps
-
Read DISCOVERIES.md. Check what's already proven. Do not repeat existing experiments.
-
Identify the hypothesis. Either from TODO.md, research/questions.yaml, or the user's request. State it as: "If we do X, then Y will happen because Z."
-
Create the experiment file. Copy
src/sparse_parity/experiments/_template.py. Change one variable from the baseline. -
Run the experiment. Capture results including accuracy, ARD, DMC, wall time. Record seed, config, environment (Python version, numpy version, OS, git hash).
-
Save Phase 1 output. Write
results/{exp_id}/results.jsonwith raw numbers, config, and environment. No interpretation in this file. -
Verify. Re-run with a different seed. If the result only holds on one seed, note that.
-
Write Phase 2 findings. Create
docs/findings/{exp_id}.mdusing the template from LAB.md. UseStatus: SUCCESS | PARTIAL | FAILED(not "COMPLETED"). Reference the results JSON. Add analysis and impact. -
Classify in research/log.jsonl. Use
"class": "WIN"only if the result is a clear improvement. Use"PARTIAL"for mixed results. Use"LOSS"for negative results. All three are valid findings. -
Update DISCOVERIES.md if the finding answers an open question or establishes a new fact.
After merge: changelog and reporting
Not every experiment needs a changelog entry. After a PR is merged, the reviewing agent decides:
- Add to changelog if the result changes the best known method, maps a new frontier, answers an open question from DISCOVERIES.md, or is the first contribution from a new researcher.
- Skip changelog if the result confirms what's already known or is a minor null result.
What ships with it
2 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.
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 · 58 lines · 21 tokens per session scan A fa707af75120
run-experiment is a skill published in the GitHub repository cybertronai/SutroYaro (16 stars, last pushed 3mo ago), licensed Unlicense. It adds 21 tokens to every session and 687 once invoked, about $0.0001 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.
Other skills, from other repositories
physicsnemo-discover
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment…
pysr
Use when fitting equations to data with PySR or SymbolicRegression.jl, when a user wants an interpretable formula, symbolic model, scaling law, or empirical relation discovered from numeric data, or when debugging a PySR search that is slow, stuck, or giving poor equations.
esmfold2
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release…
scgpt
Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology. Use this skill when: (1) Producing cell embeddings from an AnnData for clustering/integration, (2) Zero-shot or fine-tuned cell-type annotation, (3) Gene-level representation for perturbation/GRN tasks. For…
evo2
Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring…
ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning…