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/sema-lisp/sema/swarmgit clone --depth 1 https://github.com/sema-lisp/semaWrote 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/sema-lisp/sema/swarm)<a href="https://agentmods.dev/commands/sema-lisp/sema/swarm"><img src="https://agentmods.dev/badge/commands/sema-lisp/sema/swarm.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 | $0.00000 | $0.01319 |
| Opus 5 | $0.00000 | $0.00660 |
| Sonnet 5 | $0.00000 | $0.00264 |
| Haiku 4.5 | $0.00000 | $0.00132 |
Grade A, and why
swarm 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 4d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Swarm Consensus
You are orchestrating a multi-agent consensus analysis. Your job is to spawn N independent agents, collect their responses, and synthesize consensus/divergence/outliers.
Step 1 — Determine N from complexity
Assess the problem on these axes:
- Scope: Is this a narrow tactical question or a broad strategic one?
- Ambiguity: Is there a clearly correct answer, or does it depend on values/tradeoffs?
- Stakes: How costly is a wrong decision?
- Domain breadth: Does it touch multiple disciplines?
Pick N:
| Complexity | N | When |
|---|---|---|
| Low | 3 | Single-domain, clear tradeoffs, reversible |
| Medium | 5 | Multi-factor, some ambiguity, moderate stakes |
| High | 7 | Cross-domain, high ambiguity, hard to reverse |
| Critical | 10 | Strategic, high stakes, deeply uncertain |
State your chosen N and why before spawning.
Step 2 — Spawn N agents in parallel
Use the Task tool to launch all N agents simultaneously (not sequentially).
Each agent gets the same core problem but a distinct analytical lens framing. Assign lenses from this list (rotate through them for larger N):
- First-principles — Strip away assumptions. What is fundamentally true here?
- Skeptic — What is most likely to go wrong? What are the weakest assumptions?
- Optimist — What is the best realistic outcome? What would have to be true for this to work brilliantly?
- Risk analyst — What are the tail risks? What failure modes are non-obvious?
- Contrarian — What does conventional wisdom get wrong here? What is the counterintuitive answer?
- Pragmatist — Ignoring what's ideal, what is actually executable given real constraints?
- Systems thinker — What are the second and third-order effects? What feedback loops exist?
- Devil's advocate — Steelman the opposite position as strongly as possible.
- Historian — What analogous situations exist? What did people learn the hard way?
- Minimalist — What is the simplest possible solution that could work?
- Pre-mortem — Assume it already failed. Work backwards: what specifically killed it?
- Economist — What are the real incentives at play? Who wins, who loses, and will behavior match intent?
- User proxy — Forget the builders. What does the person actually affected experience end-to-end?
- Futurist — In 3–5 years, how does this look? What trends does it ride or fight against?
- Bottleneck finder — What is the single constraint that limits everything else? Where is the true chokepoint?
- Ethicist — Who bears the costs of this decision? What obligations are being honored or ignored?
- Maximalist — What does the boldest, most ambitious version of this look like? What would going all-in unlock?
- Regulator — What rules, power structures, or institutional inertia does this touch? What requires permission?
- Archaeologist — Strip away all the new framing. What old problem is this actually? Has it been solved before?
- Synthesizer — Where do the opposing views secretly agree at a deeper level? What framing resolves the apparent conflict?
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.
- 4d ago First seen · 137 lines · 0 tokens per session scan A 64809105cb8c
swarm is a command published in the GitHub repository sema-lisp/sema (41 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,319 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
audit-crate
Audit a single crate (used by /audit coordinator or standalone).
simplicio
Run the next user task through the simplicio-prompt Tuple-Space + Yool safe-speed runtime.
simplicio-install
Install the simplicio-prompt runtime contract into the current repository's agent rules files (CLAUDE.md, AGENTS.md, .cursorrules, .github/copilot-instructions.md, etc.).
simplicio-status
Toggle the simplicio-prompt opt-in status output ([Tuple Space Snapshot] / [Active] / [Total] / [Next Yool] / [Partial Result]).
axon-deploy
Deploy or restart the axon stack (qdrant, tei, chrome, axon) via docker compose.
AGENTS
Per-subcommand modules for the solvela CLI. Each file is responsible for one top-level command's argument parsing (via nested clap Args), business logic, and output formatting.