haabe/mycelium/fan-out

Claude Code and agent skill by haabe/mycelium

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

Install

npx vskill@latest install haabe/mycelium/fan-out

SKILL.md

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, design test, and write its runs_on (disk | network | human, as in /mycelium:assumption-test)
  • Register each human test (survey, fake door, interviews, paid pilot) through /mycelium:handoff. No subagent runs it: a subagent can only simulate what people would do, and a simulated verdict is not a test result (v0.330.0)
2. Fan-Out (Spawn Workers, disk and network tests only)

For each solution with a disk or network test:

  • Spawn subagent or Agent Team member
  • Provide: clear task, read-only canvas context, worktree isolation, and a turn cap where the runtime has one (maxTurns in an agent definition). A time bound in the brief is advice: Claude Code has no time limit for a subagent
  • Each worker runs independently and reports its evidence with its source, not only a verdict
3. Fan-In (Collect Results)
  • Collect all worker results, and the human tests' verdicts once logged through /mycelium:log-evidence
  • 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 (two or more leaves that each have an untested riskiest assumption; no ICE gap is consulted):

  • Each worker returns a structured scorecard (Four Risks + ICE from the leaf's record, the result of its disk or network test, segment fit); a leaf whose test is human gets its scorecard from the lead once the verdict is logged
  • Lead agent applies winner selection rules, which read the assumption-test verdicts: clear winner (one passes, the others fail), several pass in the same segment (tiebreaker), several pass in different segments (segment split, both advance), both fail (archive and re-evaluate). A leaf whose riskiest assumption failed is archived.
  • The ICE total only orders which assumption is tested first. It never decides when to compare or which leaf wins (until 0.320.0 a "clear winner" was a leaf more than 20% ahead on ICE).
  • 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). On Claude Code the guard-state gate refuses a subagent's write to .claude/canvas/ or .claude/diamonds/ (v0.330.0); the other rules below are the lead's discipline and no hook checks them
  • Workers NEVER progress diamonds
  • Workers NEVER run a human test
  • Lead agent ALWAYS runs bias check on combined results
  • Minimum 2 workers for meaningful comparison
  • Maximum 3 leaves per bakeoff (respects L3 WIP ceiling)
  • Inside an L3 (v0.301.0), the leaves are its set under the L2's target, and a clear winner is the candidate for its front_runner; recording it is the human's choice

See ${CLAUDE_PLUGIN_ROOT}/orchestration/agent-teams.md for full orchestration patterns.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
51
Repository forks
6
7-day trend score
5

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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