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/analyze.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/analyze)<a href="https://agentmods.dev/commands/hwiyeon/claude-sisyphus-grad/analyze"><img src="https://agentmods.dev/badge/commands/hwiyeon/claude-sisyphus-grad/analyze/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/analyze"><img src="https://agentmods.dev/badge/commands/hwiyeon/claude-sisyphus-grad/analyze.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 | $0.00716 |
| Opus 5 | $0.00000 | $0.00358 |
| Sonnet 5 | $0.00000 | $0.00143 |
| Haiku 4.5 | $0.00000 | $0.00072 |
Grade A, and why
analyze 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 12d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Standalone Pre-Analysis Command
Runs a Briefing + multi-agent discussion on the training script and config,
and optionally applies improvements automatically via a Code Modifier.
This is a command that runs the analyze mode of /train independently.
Usage
/analyze script=train.py config=configs/config.yaml [review_cycles=1] [apply=true] [branch_name=analyze-mod]
Arguments
| argument | description | default |
|---|---|---|
script |
path to the training script to analyze | required |
config |
path to the config file to analyze | required |
review_cycles |
number of G→round2→round3→Judge cycle repetitions | 1 |
apply |
whether to automatically apply the Judge decision's NEXT_ACTION | true |
branch_name |
branch name to create when modifying code | analyze-mod |
| lang | output language for reports and messages (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
scriptandconfigfiles exist - Print parsed values for user confirmation
Step 2: Create Output Directory
research/logs/{YYYY-MM-DD}/analyze_{HH-MM}/
├── reports/
│ ├── pre_analysis_briefing.md
│ └── pre_review_discussion.md
└── cache/
Step 3: Load Modules
Read the following two files to load rules:
.claude/prompts/train-recording-rules.md(Briefing agent, pre-review discussion format).claude/prompts/train-review-pipeline.md(multi-agent review discussion)
Step 4: Call Briefing Agent
Call Briefing agent per the "Pre-Analysis and Model Improvement" section of train-recording-rules.md:
- Input:
eval_results/,research/logs/,research/README.md,{script},{config} - Output:
reports/pre_analysis_briefing.md
Step 5: Multi-Agent Analysis Discussion
Run multi-agent discussion per train-review-pipeline.md rules:
- Discussion content recorded in
reports/pre_review_discussion.md - Discussion topics: performance bottlenecks, config vs. structural changes, specific improvements, risks/expected impact
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.
- 12d ago First seen · 89 lines · 0 tokens per session scan A ef98d573ad38
analyze 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 716 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
superpowers-status
Show the complete health of the project's AI Literacy habitat — harness enforcement, agent team, compound learning, model routing, and CI status.
change-models
Set, change, or audit model preferences for pipeline agents — interactive 6-mode workflow (global/workspace prefs, per-agent overrides, first-run wizard, catalog refresh).
optimize-pipeline
Optimize an existing pipeline on demand — survey topology, model-tier cost, and past-run signals, lock an optimization plan with the user, then batch-apply it atomically with a mandatory post-apply audit.
multi-execute
Multi-model collaborative execution: prototype from plan, refactor, multi-model audit, delivery.
dbt-audit-review
Internal RA review of dbt audit.
optimize-prompt
Takes an input prompt and returns ONLY a token-optimized version that preserves meaning while minimizing token count. Based on LLM tokenization principles: common words tokenize more efficiently, unusual words break into more tokens, and conciseness reduces cost.