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 skills add bolivian-peru/os-moda --skill scaled-swarm-predictgit clone --depth 1 https://github.com/bolivian-peru/os-modaWrote 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/skills/bolivian-peru/os-moda/scaled-swarm-predict)<a href="https://agentmods.dev/skills/bolivian-peru/os-moda/scaled-swarm-predict"><img src="https://agentmods.dev/badge/skills/bolivian-peru/os-moda/scaled-swarm-predict/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/skills/bolivian-peru/os-moda/scaled-swarm-predict"><img src="https://agentmods.dev/badge/skills/bolivian-peru/os-moda/scaled-swarm-predict.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00032 | $0.04151 |
| Opus 5 | $0.00016 | $0.02076 |
| Sonnet 5 | $0.00006 | $0.00830 |
| Haiku 4.5 | $0.00003 | $0.00415 |
Grade A, and why
scaled-swarm-predict 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 — 485 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scaled Swarm Predict
Large-scale social simulation engine. Generates 50-200 demographically diverse AI personas, runs them through a simulated Twitter or Reddit board debating a topic, then analyzes emergent consensus, fault lines, and predictions.
What this is: A structured simulation where you generate a population of diverse personas matching real-world demographics, run 4-6 rounds of simulated social media discussion in a local SQLite database, then analyze position shifts and emergent consensus to make predictions.
What this is NOT: This is not MiroFish/OASIS (which runs independent agent processes). All personas are generated and role-played by a single model. The value comes from forcing diverse demographic perspectives through structured rounds of interaction — not from emergent multi-agent behavior. Think of it as a sophisticated polling simulation, not swarm intelligence.
Cost per run: ~$5-15 with Claude Sonnet (100 agents × 5 rounds = 500 generations). ~$25-60 with Opus. Each round is one large prompt, not 100 separate API calls.
When to Use
- Predict public reaction: "How will Twitter react to this product launch?"
- Election/poll modeling: "What does a demographically representative sample think about X policy?"
- Content testing: "Will this announcement go viral or get ratio'd?"
- Market sentiment: "How will crypto twitter react to this protocol change?"
- Risk assessment: "What will the Reddit comments look like when we announce this pricing change?"
- Brainstorming at scale: "What arguments exist for/against X across different demographics?"
Phase 1: Define the Simulation
1.1 — Get the Topic
Ask the user for:
- Topic/scenario — What are we simulating discussion about?
- Platform — Twitter (short-form, viral dynamics) or Reddit (long-form, threaded)
- Population size — 50 (fast, cheap), 100 (balanced), 200 (thorough)
- Seed data — Any documents, articles, data to ground the simulation
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 · 485 lines · 32 tokens per session scan A 6e9251bbbfa7
scaled-swarm-predict is a skill published in the GitHub repository bolivian-peru/os-moda (117 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 4,151 once invoked, about $0.0002 per session on Opus 5. 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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