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 vibeeval/vibecosystem --skill agentica-spawngit clone --depth 1 https://github.com/vibeeval/vibecosystemWrote 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/vibeeval/vibecosystem/agentica-spawn)<a href="https://agentmods.dev/skills/vibeeval/vibecosystem/agentica-spawn"><img src="https://agentmods.dev/badge/skills/vibeeval/vibecosystem/agentica-spawn/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/vibeeval/vibecosystem/agentica-spawn"><img src="https://agentmods.dev/badge/skills/vibeeval/vibecosystem/agentica-spawn.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 | $0.00011 | $0.00457 |
| Opus 5.5 | $0.00004 | $0.00183 |
| Sonnet 5.5 | $0.00002 | $0.00091 |
| Haiku 4.5 | $0.00001 | $0.00046 |
Grade A, and why
agentica-spawn 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 1mo 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.
This is a copy
100% identical to agentica-spawn — 148 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentica Spawn Skill
Use this skill after user selects an Agentica pattern.
When to Use
- After agentica-orchestrator prompts user for pattern selection
- When user explicitly requests a multi-agent pattern (swarm, hierarchical, etc.)
- When implementing complex tasks that benefit from parallel agent execution
- For research tasks requiring multiple perspectives (use Swarm)
- For implementation tasks requiring coordination (use Hierarchical)
- For iterative refinement (use Generator/Critic)
- For high-stakes validation (use Jury)
Pattern Selection to Spawn Method
Swarm (Research/Explore)
swarm = Swarm(
perspectives=[
"Security expert analyzing for vulnerabilities",
"Performance expert optimizing for speed",
"Architecture expert reviewing design"
],
aggregate_mode=AggregateMode.MERGE,
)
result = await swarm.execute(task_description)
Hierarchical (Build/Implement)
hierarchical = Hierarchical(
coordinator_premise="You break tasks into subtasks",
specialist_premises={
"planner": "You create implementation plans",
"implementer": "You write code",
"reviewer": "You review code for issues"
},
)
result = await hierarchical.execute(task_description)
Generator/Critic (Iterate/Refine)
gc = GeneratorCritic(
generator_premise="You generate solutions",
critic_premise="You critique and suggest improvements",
max_rounds=3,
)
result = await gc.run(task_description)
Jury (Validate/Verify)
jury = Jury(
num_jurors=5,
consensus_mode=ConsensusMode.MAJORITY,
premise="You evaluate the solution"
)
verdict = await jury.decide(bool, question)
Environment Variables
All spawned agents receive:
SWARM_ID: Unique identifier for this swarm runAGENT_ROLE: Role within the pattern (coordinator, specialist, etc.)PATTERN_TYPE: Which pattern is running
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.
- 1mo ago First seen · 75 lines · 11 tokens per session scan A 245ba85626ed
agentica-spawn is a skill published in the GitHub repository vibeeval/vibecosystem (538 stars, last pushed 2mo ago), licensed MIT. It adds 11 tokens to every session and 457 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agentica-spawn, differing in 148 lines, and is treated as a copy.
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