74 lines
2.2 KiB
Markdown
74 lines
2.2 KiB
Markdown
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# Learnings - Chiron Agent Framework
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## Wave 1, Task 1: Create agents.json with 6 agent definitions
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### Agent Structure Pattern
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**Required fields per agent:**
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- `description`: Clear purpose statement
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- `mode`: "primary" for orchestrators, "subagent" for specialists
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- `model`: "zai-coding-plan/glm-4.7" (consistent across all agents)
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- `prompt`: File reference pattern `{file:./prompts/<name>.txt}`
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- `permission`: Either explicit permissions or simple "question": "allow"
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### Primary vs Subagent Modes
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**Primary agents** (2): chiron, chiron-forge
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- Can be invoked directly by user
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- Orchestrate and delegate work
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- Higher permission levels (external_directory rules)
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**Subagents** (4): hermes, athena, apollo, calliope
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- Invoked by primary agents via Task tool
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- Specialized single-purpose workflows
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- Simpler permission structure (question: "allow")
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### Permission Patterns
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**Primary agents**: Complex permission structure
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```json
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"permission": {
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"external_directory": {
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"~/p/**": "allow",
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"*": "ask"
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}
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}
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```
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**Subagents**: Simple permission structure
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```json
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"permission": {
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"question": "allow"
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}
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```
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### Agent Domains
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1. **chiron**: Plan Mode - Read-only analysis and planning
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2. **chiron-forge**: Build Mode - Full execution with safety prompts
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3. **hermes**: Work communication (Basecamp, Outlook, Teams)
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4. **athena**: Work knowledge (Outline wiki, documentation)
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5. **apollo**: Private knowledge (Obsidian vault, personal notes)
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6. **calliope**: Writing (documentation, reports, prose)
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### Verification Commands
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**Agent count:**
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```bash
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python3 -c "import json; data = json.load(open('agents/agents.json')); print(len(data))"
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# Expected output: 6
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```
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**Agent names:**
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```bash
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python3 -c "import json; data = json.load(open('agents/agents.json')); print(sorted(data.keys()))"
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# Expected output: ['apollo', 'athena', 'calliope', 'chiron', 'chiron-forge', 'hermes']
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```
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### Key Takeaways
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- Prompt files use file references, not inline content (Wave 2 will create these)
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- Model is consistent across all agents for predictable behavior
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- Permission structure matches agent capability level (more complex for primaries)
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- Mode determines how agent can be invoked (direct vs delegated)
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