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/review.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/review)<a href="https://agentmods.dev/commands/hwiyeon/claude-sisyphus-grad/review"><img src="https://agentmods.dev/badge/commands/hwiyeon/claude-sisyphus-grad/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/hwiyeon/claude-sisyphus-grad/review"><img src="https://agentmods.dev/badge/commands/hwiyeon/claude-sisyphus-grad/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 | $0.00557 |
| Opus 5 | $0.00000 | $0.00279 |
| Sonnet 5 | $0.00000 | $0.00111 |
| Haiku 4.5 | $0.00000 | $0.00056 |
Grade A, and why
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 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Standalone Review Command
Runs the multi-agent review pipeline independently on experiment results.
Can be used standalone outside the /train loop; performs a one-time review without ralph-loop integration.
Usage
/review results=path/to/experiment_N.json detail=path/to/experiment_N_detail.md [review_cycles=1] [config=...] [script=...]
Arguments
| argument | description | default |
|---|---|---|
results |
path to experiment result JSON file | required |
detail |
path to experiment detail MD file | required |
review_cycles |
number of G→round2→round3→Judge cycle repetitions | 1 |
config |
config file path (passed to reviewers as reference) | optional |
script |
training script path (passed to reviewers as reference) | optional |
| lang | output language for review output (ko, en, etc.). The project's CLAUDE.md defines the default | project default |
Procedure
Step 1: Parse and Validate Arguments
Parse arguments from $ARGUMENTS.
- Verify that
resultsanddetailfiles exist
- Infer metric cache path:
cache/metric_cache.jsonlin the same session directory asdetailfile
- Print parsed values for user confirmation
Step 2: Load Review Pipeline Module
Read .claude/prompts/train-review-pipeline.md to load review rules.
Step 3: Run Review
Run multi-agent review per train-review-pipeline.md rules:
- Input mapping:
experiment_n: extracted from filename (e.g.,experiment_3.json→ 3)detail_file:detailargumentresults_file:resultsargument
metric_cache: inferred path (skip if not found)
review_cycles: argument valueconfig,script: argument values (not passed to reviewers if absent)
Step 4: Output Results
- Output Judge decision directly to user
- No
session_continuation.jsonmanipulation - No ralph-loop integration
- Review content is recorded in the
detailfile (handled by the review pipeline scribe)
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 · 71 lines · 0 tokens per session scan A 091a509bfee8
review is a command published in the GitHub repository Hwiyeon/claude-sisyphus-grad (59 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 557 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 2026-08-30.
Other commands, from other repositories
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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.