Borrowing it
Nothing to install: this file belongs to swarm-ai-research/swarm. 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/swarm-ai-research/swarm/main/.claude/commands/analyze_experiment.mdgit clone --depth 1 https://github.com/swarm-ai-research/swarmWrote 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/swarm-ai-research/swarm/analyze_experiment)<a href="https://agentmods.dev/commands/swarm-ai-research/swarm/analyze_experiment"><img src="https://agentmods.dev/badge/commands/swarm-ai-research/swarm/analyze_experiment/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/swarm-ai-research/swarm/analyze_experiment"><img src="https://agentmods.dev/badge/commands/swarm-ai-research/swarm/analyze_experiment.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 | $0.02405 |
| Opus 5 | $0.00000 | $0.01203 |
| Sonnet 5 | $0.00000 | $0.00481 |
| Haiku 4.5 | $0.00000 | $0.00241 |
Grade A, and why
analyze_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 10d 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analyze_experiment
Run statistical analysis on SWARM experiment data. Two modes: run a scenario across multiple seeds, or analyze an existing sweep CSV — use when you have run data and need statistical rigor (p-values, confidence intervals, effect sizes). Distinct from /sweep (generates data without analysis), /run_scenario (single run without statistics), and /full_study (chains sweep + analysis + plots + paper end-to-end). Consolidates the former /stats command (now /analyze_experiment --csv).
Usage
/analyze_experiment <scenario_path_or_id> [--seeds N|seed1,seed2,...] [--groups auto|key=ids,...]
/analyze_experiment --csv <sweep_csv> [--output <dir>]
Examples:
/analyze_experiment rlm_recursive_collusion/analyze_experiment scenarios/rlm_memory_as_power.yaml --seeds 20/analyze_experiment rlm_governance_lag --seeds 42,7,123,256,999/analyze_experiment rlm_memory_as_power --groups high=rlm_1,rlm_2,rlm_3 mid=rlm_4,rlm_5,rlm_6 low=rlm_7/analyze_experiment --csv runs/20260210-223119_kernel_market_v2/sweep_results.csv/analyze_experiment --csv runs/latest/sweep_results.csv --output runs/latest/
Argument parsing
Parse $ARGUMENTS to extract:
--csv <path>: CSV mode — run statistics on an existing sweep CSV (see CSV Mode below)--seeds: Either an integer N (generate N random seeds) or a comma-separated list. Default:42,7,123,256,999,2024,314,577,1337,8080(10 seeds).--groups: How to group agents for comparison.auto(default) infers from scenario YAML. Otherwiselabel=id1,id2,....--output <dir>: Output directory (CSV mode only). Default: same directory as the CSV.
If --csv is present → CSV mode. Otherwise → Scenario mode.
Scenario Mode (default)
Preferred: Use the persistent analysis script
python -m swarm.scripts.analyze <scenario_path_or_id> [--seeds <seeds>]
This handles everything: scenario loading, seed execution, group detection, statistics, corrections, and artifact export. Just run it and print the output.
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.
- 10d ago First seen · 244 lines · 0 tokens per session scan A 154fbddb1d07
analyze_experiment is a command published in the GitHub repository swarm-ai-research/swarm (42 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,405 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.
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