Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/swarm-ai-research/swarm/full_studygit 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/full_study)<a href="https://agentmods.dev/commands/swarm-ai-research/swarm/full_study"><img src="https://agentmods.dev/badge/commands/swarm-ai-research/swarm/full_study.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 | $0.00000 | $0.03325 |
| Opus 5 | $0.00000 | $0.01663 |
| Sonnet 5 | $0.00000 | $0.00665 |
| Haiku 4.5 | $0.00000 | $0.00332 |
Grade A, and why
full_study 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 5d 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/full_study
End-to-end research pipeline: sweep parameters, analyze with statistical rigor, generate plots, and scaffold a paper draft. Chains /sweep, /analyze_experiment, /plot, and /write_paper into a single workflow.
Usage
/full_study <scenario_path> [title_slug] [--seeds N] [--params key=val1,val2 ...] [--refine [--depth lite|full]]
/full_study --detection [title_slug] [--seeds N] [--agents N] (detection-experiment mode; no scenario)
Examples:
/full_study scenarios/rlm_recursive_collusion.yaml collusion_tax_effect/full_study scenarios/kernel_market/baseline.yaml kernel_governance --seeds 10/full_study scenarios/baseline.yaml governance_sweep --seeds 5 --params governance.transaction_tax_rate=0.0,0.05,0.1,0.15 --params governance.circuit_breaker_enabled=True,False/full_study --detection soft_vs_binary_detection --seeds 10
Arguments
scenario_path: Path to the scenario YAML file. Omit (and pass--detection) for detection-experiment mode.title_slug(optional): Slug for the paper filename. Default: derived from scenario_id (orsoft_vs_binary_detectionin--detectionmode).--seeds N: Number of seeds per configuration. Default: 10.--params: Parameter sweep axes, passed through to the sweep step. If omitted, uses the default sweep inexamples/parameter_sweep.py. (Ignored in--detectionmode.)--detection: Run the matched soft-vs-binary detection experiment instead of a scenario sweep (see "Detection mode" below). Phase 1 runsexperiments/run_detection_experiment.py; Phases 2–4 consume itssummary.json.--agents N: (detection mode only) Agents per population. Default: 40.--refine: After the paper draft, run the AgentLab refinement pipeline (see Phase 4b below). Optional.--depth lite|full: Refinement depth (only used with--refine). Default:lite.
Behavior
Detection mode (
--detection) replaces the scenario-sweep pipeline with the matched soft-vs-binary detection experiment. The phase structure is preserved so the rest of the pipeline (analysis, plots, paper, summary) works unchanged — only the data source differs. Where a phase below says "in detection mode", follow that branch. The remaining phases (2b council, 4b refinement, 5 summary) work identically, readingsummary.jsonfrom the detection run dir.
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.
- 5d ago First seen · 273 lines · 0 tokens per session scan A 1018c7d7bdc4
full_study is a command published in the GitHub repository swarm-ai-research/swarm (41 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,325 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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checklist
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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.