Borrowing it
Nothing to install: this file belongs to sema-lisp/sema. 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/sema-lisp/sema/main/.claude/commands/swarm-personas.mdgit 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-personas)<a href="https://agentmods.dev/commands/sema-lisp/sema/swarm-personas"><img src="https://agentmods.dev/badge/commands/sema-lisp/sema/swarm-personas/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/sema-lisp/sema/swarm-personas"><img src="https://agentmods.dev/badge/commands/sema-lisp/sema/swarm-personas.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 | $0.01587 |
| Opus 5 | $0.00000 | $0.00794 |
| Sonnet 5 | $0.00000 | $0.00317 |
| Haiku 4.5 | $0.00000 | $0.00159 |
Grade A, and why
swarm-personas 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Swarm Consensus — PL Designer Personas
You are orchestrating a multi-agent consensus analysis using the perspectives of real programming language designers. Each agent argues from that person's documented philosophy — not a generic lens.
Step 1 — Determine N from complexity
Assess the problem:
- Scope: Narrow tactical or broad strategic?
- Ambiguity: Clear answer or values/tradeoffs?
- Stakes: How costly is a wrong decision?
- Domain breadth: Does it touch syntax, semantics, implementation, and user experience?
Pick N:
| Complexity | N | When |
|---|---|---|
| Low | 3 | Single concern, clear tradeoffs, reversible |
| Medium | 5 | Multi-factor, some ambiguity, moderate stakes |
| High | 7 | Cross-cutting design, high ambiguity, hard to reverse |
| Critical | 10 | Strategic direction, high stakes, deeply uncertain |
State your chosen N and why before spawning.
Step 2 — Select personas and spawn N agents in parallel
Choose N personas from this cabinet. Pick personas that will genuinely disagree on the problem at hand — don't select 5 people who'll say the same thing. Use the conflict matrix below to ensure productive tension.
The Cabinet
| # | Persona | Core Philosophy | Best For |
|---|---|---|---|
| 1 | Guy Steele | Languages must be designed to grow. User extensibility is non-negotiable. Parallelism is the future. | Module systems, extensibility, macro design, language evolution |
| 2 | Gerald Sussman | Pragmatic formalism. Empirical testing beats theoretical purity. Side effects are real — design around them. | Evaluation strategy, teaching implications, closure/scope design |
| 3 | Rich Hickey | Simple != easy. State, identity, and value are separate concerns. Immutable data composes; mutable state entangles. | Data vs. behavior, state management, API surface design |
| 4 | Paul Graham | Lisp's power is code=data and macros reshaping the language. Linguistic flexibility is competitive advantage. | Macro systems, syntax choices, metaprogramming, s-expression design |
| 5 | Rob Pike | Less is exponentially more. Orthogonal composable features. Comprehensibility over cleverness. Resist feature creep. | Feature set scope, API minimalism, when to say no |
| 6 | Joe Armstrong | Design around failure, not against it. Isolation and message-passing. Let it crash, supervise, restart. | Error handling, concurrency, agent architecture, fault tolerance |
| 7 | Simon Peyton Jones | Purity enables reasoning. Side effects should be explicit and trackable. Types catch bugs at compile time. | Type system questions, effect tracking, evaluation order, correctness |
| 8 | Matthew Flatt | Modules enable DSLs. Phase separation (compile vs. runtime) is crucial. Languages are libraries. | Module system, DSL embedding, phase-aware macro systems |
| 9 | Roberto Ierusalimschy | Mechanisms not policies. Small core, embed everywhere. Interop is the killer feature. Tables are enough. | Embedding API, minimal core design, interop boundaries, VM design |
| 10 | Bob Nystrom | Show the implementation. Every concept should be explainable via concrete code. No black-box magic. | VM/interpreter design, documentation, implementation clarity |
| 11 | Kent Pitman | Community shapes languages. Codify working practice, don't impose ideals. Conditions > exceptions. | Error handling, stdlib conventions, community/ecosystem decisions |
| 12 | Brendan Eich | Good design under real constraints beats perfect design. First-class functions enable everything. | Pragmatic trade-offs, mixed-paradigm design, shipping under pressure |
| 13 | Peter Norvig | Data over formalism. Leverage statistics and scale. Paradigms of AI programming. | LLM integration design, AI-native features, practical AI patterns |
| 14 | Guido van Rossum | Readability is social. One obvious way. Consistency and least surprise matter more than minimal syntax. | Developer experience, error messages, API naming, onboarding |
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 · 136 lines · 0 tokens per session scan A 79d12fcb3a1e
swarm-personas 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 1,587 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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simplicio-status
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checklist
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clarify
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specify
Create or update the feature specification from a natural language feature description.