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 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/swarm-predict)<a href="https://agentmods.dev/skills/bolivian-peru/os-moda/swarm-predict"><img src="https://agentmods.dev/badge/skills/bolivian-peru/os-moda/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/swarm-predict"><img src="https://agentmods.dev/badge/skills/bolivian-peru/os-moda/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 | $0.00017 | $0.02390 |
| Opus 5 | $0.00009 | $0.01195 |
| Sonnet 5 | $0.00003 | $0.00478 |
| Haiku 4.5 | $0.00002 | $0.00239 |
Grade A, and why
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 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 — 262 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Swarm Predict
Structured multi-perspective risk analysis before acting on infrastructure changes. Uses persona-based debate to surface risks from different viewpoints, then deploys via SafeSwitch with auto-rollback.
What this is: A structured prompting technique where you role-play 6-8 expert personas debating a proposed change. It forces consideration of multiple angles (security, reliability, cost, UX) before committing. Think of it as a pre-flight checklist, not a crystal ball.
What this is NOT: This is not true multi-agent simulation (like MiroFish/OASIS with independent agent processes). All personas share one context window and one model. The value comes from structured thinking and the checklist effect, not from emergent behavior.
When to Use
- Before deploying infrastructure changes ("What if we switch to nginx?")
- Before system upgrades ("Will upgrading PostgreSQL break anything?")
- Incident response ("What's the safest recovery path?")
- Any change where you want a second opinion but don't have a team to consult
Workflow
Phase 1: Gather Context
Collect real system state. The analysis is only as good as the data it's grounded in.
1. system_health() → CPU, RAM, disk, load, uptime
2. system_query({ query: "services" }) → running services
3. journal_logs({ unit: "relevant-service", lines: 50 }) → recent activity
4. file_read({ path: "/relevant/config/file" }) → current config
Minimum data checklist — do NOT proceed without:
- system_health returned CPU/RAM/disk numbers
- At least one service query succeeded
- The proposed change is specific (not vague like "improve performance")
If data collection fails, tell the user: "Cannot run analysis without baseline system state. Please provide context manually or fix the service queries."
Build a situation briefing — a concise paragraph with:
- Current system state (concrete numbers, not "healthy")
- The exact proposed change
- Known constraints or dependencies
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 262 lines · 17 tokens per session scan A 423b2c4cdaf8
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 17 tokens to every session and 2,390 once invoked, about $0.0001 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.
Other skills, from other repositories
makefile-generation
Generates Makefiles with testing, linting, formatting, and automation targets. Use when starting a project or standardizing build automation.
workflow-setup
Configures GitHub Actions CI/CD workflows for testing, linting, and deployment. Use when setting up automation for a Python, Rust, or TypeScript project.
analyzing-linux-kernel-rootkits
Detect kernel-level rootkits in Linux memory dumps using Volatility3 linux plugins (checksyscall, lsmod, hiddenmodules), rkhunter system scanning, and /proc vs /sys discrepancy analysis to identify hooked syscalls, hidden kernel modules, and tampered system structures.
devops-expert
Expert-level DevOps practices, culture, automation, and continuous delivery. Use when the user mentions CI/CD, automation, infrastructure, or culture, or when the task involves DevOps Culture or Culture & Process.
shell-scripting
Use this skill when writing bash or zsh scripts, parsing arguments, handling errors, or automating CLI workflows. Triggers on bash scripting, shell scripts, argument parsing, process substitution, here documents, signal trapping, exit codes, and any task requiring portable shell script development.
agent-self-improvement
Use when monitor performance of other skills, identify bottlenecks, suggest improvements, and auto-optimize the skill portfolio. Use when monitoring performance of other skills, identify bottlenecks, suggest improvements, and.