Borrowing it
Nothing to install: this file belongs to Hwiyeon/claude-sisyphus-grad. 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/Hwiyeon/claude-sisyphus-grad/main/.claude/commands/train.mdgit clone --depth 1 https://github.com/Hwiyeon/claude-sisyphus-gradWrote 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/hwiyeon/claude-sisyphus-grad/train)<a href="https://agentmods.dev/commands/hwiyeon/claude-sisyphus-grad/train"><img src="https://agentmods.dev/badge/commands/hwiyeon/claude-sisyphus-grad/train/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/hwiyeon/claude-sisyphus-grad/train"><img src="https://agentmods.dev/badge/commands/hwiyeon/claude-sisyphus-grad/train.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 | $0.00031 | $0.01489 |
| Opus 5 | $0.00015 | $0.00745 |
| Sonnet 5 | $0.00006 | $0.00298 |
| Haiku 4.5 | $0.00003 | $0.00149 |
Grade A, and why
train scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Automation Launcher (External Script Loop)
This command parses arguments, creates the initial state, and runs scripts/train-loop.sh in the background to automatically repeat the experiment loop.
Detailed orchestrator rules are defined in .claude/prompts/train-orchestrator.md.
Step 1: Parse Input Arguments ($ARGUMENTS)
Parse arguments in the format below. Use defaults for unspecified items:
/train script=train.py config=configs/myexp.yaml max_experiments=20 experiment_title="baseline_v1" env=myenv parallel=1 instructions="adjust lr based on previous run report before starting"
| argument | description | default |
|---|---|---|
script |
path to training script | required |
config |
path to config file for training | required |
experiment_title |
experiment title (used as folder name) | required |
max_experiments |
maximum number of experiments | 20 |
review_cycles |
number of G Research Brief → round 2 → round 3 → Judge decision cycle repetitions | 1 |
goal |
experiment termination condition (metric-based) | optional |
decision_criteria |
decision criteria (free text or @filepath) |
optional |
| env | Python virtual environment name (training runs after conda activate {env}) | required |
| parallel | number of models to train simultaneously. If >=2, each runs as a separate parallel process | 1 |
| instructions | pre-instructions for the orchestrator before training. Free text or @filepath to reference previous reports. Multiple files: @path1 @path2 | optional |
| analyze | before training, analyze best model performance, training reports, and research logs to derive and apply improvements. May include fundamental changes to architecture, loss, data pipeline, etc. | false |
| subset | if true, first validate with 20% subset, then switch to full dataset if promising | false |
| circuit_breaker | if the same Judge decision type occurs N times consecutively, pass pattern detection to the Judge during round 2 review to strengthen decision direction. null disables this | null |
| lang | output language for all records, messages, and reports (ko, en, etc.). The project's CLAUDE.md defines the default | project default |
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.
- 11d ago First seen · 151 lines · 31 tokens per session scan A ddb0f4956bcc
train is a command published in the GitHub repository Hwiyeon/claude-sisyphus-grad (59 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 1,489 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.
Other commands, from other repositories
ruview-train
Train a RuView model — camera-free WiFlow pose, camera-supervised pose (92.9% PCK@20), RuVector embeddings, domain generalization, local SNN, with optional GPU on GCloud.
ds:review
Peer review an ML experiment for methodology, leakage, reproducibility, and statistical validity.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.