Best use case
agentica-spawn is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Spawn Agentica multi-agent patterns
Teams using agentica-spawn should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agentica-spawn/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agentica-spawn Compares
| Feature / Agent | agentica-spawn | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Spawn Agentica multi-agent patterns
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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)
```python
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)
```python
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)
```python
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)
```python
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 run
- `AGENT_ROLE`: Role within the pattern (coordinator, specialist, etc.)
- `PATTERN_TYPE`: Which pattern is runningRelated Skills
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agentica-infrastructure
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agentica-claude-proxy
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workflow-router
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websocket-patterns
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visual-verdict
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verification-loop
Comprehensive verification system covering build, types, lint, tests, security, and diff review before a PR.
vector-db-patterns
Embedding strategies, ANN algorithms, hybrid search, RAG chunking strategies, and reranking for semantic search and retrieval.
variant-analysis
Find similar vulnerabilities across a codebase after discovering one instance. Uses pattern matching, AST search, Semgrep/CodeQL queries, and manual tracing to propagate findings. Adapted from Trail of Bits. Use after finding a bug to check if the same pattern exists elsewhere.