fan-out

Solid

Parallel agent orchestration for OST exploration. Fan-out multiple solution explorations, fan-in results to compare and select winners.

AI & Automation 41 stars 3 forks Updated today MIT

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Skill Content

# Fan-Out / Fan-In Orchestration ## When to Use - OST has 2+ solutions for the same opportunity that need parallel exploration - Independent delivery work items that can be parallelized - Multiple research streams needed simultaneously ## Workflow ### 1. Prepare (Lead Agent) - Read .claude/canvas/opportunities.yml - Identify solutions needing parallel work - For each: identify riskiest assumption and design test ### 2. Fan-Out (Spawn Workers) For each solution: - Spawn subagent or Agent Team member - Provide: clear task, read-only canvas context, worktree isolation, time bound - Each worker runs independently ### 3. Fan-In (Collect Results) - Collect all worker results - Update ICE scores in canvas - Run `/mycelium:bias-check` on combined findings - Select winner(s) based on evidence - Log decision in .claude/harness/decision-log.md ### Leaf Bakeoff Mode When comparing competing OST leaves for the same opportunity: - Each worker returns a structured **scorecard** (Four Risks + ICE + assumption test result + segment fit) - Lead agent applies **winner selection rules**: clear winner (>20% ICE delta), close race (tiebreaker), segment split (both advance), both fail (archive and re-evaluate) - Losers are archived to `.claude/canvas/archived-solutions.yml` with full evidence snapshot See `${CLAUDE_PLUGIN_ROOT}/orchestration/leaf-bakeoff.md` for the complete bakeoff protocol. ## Rules - Workers NEVER write to canvas (lead agent does) - Workers NEVER progress diamonds - Lea...

Details

Author
haabe
Repository
haabe/mycelium
Created
3 months ago
Last Updated
today
Language
Python
License
MIT

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