hive-mind-advanced

name	hive-mind-advanced
description	Hive Mind orchestration patterns — queen-led multi-agent coordination with Byzantine/Raft/Gossip/CRDT consensus, typed collective memory, dialectic council, and session checkpoint/resume. Use for decision-bearing work; use swarm-advanced for parallel execution without consensus.
version	2.0.0
category	coordination
tags	["hive-mind","council","dialectic","consensus","byzantine","raft","gossip","crdt","queen-worker","multi-agent","fault-tolerance"]
author	Ruflo Fork Team
allowed-tools	Bash(npx *) Read Write Edit Grep Glob Task mcp__ruflo__hive-mind_init mcp__ruflo__hive-mind_spawn mcp__ruflo__hive-mind_status mcp__ruflo__hive-mind_join mcp__ruflo__hive-mind_leave mcp__ruflo__hive-mind_consensus mcp__ruflo__hive-mind_broadcast mcp__ruflo__hive-mind_shutdown mcp__ruflo__hive-mind_memory mcp__ruflo__memory_store mcp__ruflo__memory_search

Hive Mind Advanced

Hive Mind is the queen-led coordination capability for tasks that need a decision — a verdict, a vote, a ratified design. It complements swarm-advanced (which is for parallel execution without consensus). Pick a Hive Mind pattern when the goal is "reach agreement on X" or "produce a council-shaped review of Y." Pick swarm-advanced when the goal is "do parallel work and compose the artefacts."

This skill covers four concrete patterns. Each is a real recipe with verified MCP tool calls — not a feature list.

Quick Start

Prerequisites

The Ruflo MCP server must be registered (one-time, in shell):

claude mcp add claude-flow -- npx -y @sparkleideas/cli@latest mcp start

Tools resolve under mcp__ruflo__* (per ADR-0117 marketplace MCP server registration, Accepted/Implemented 2026-05-05). Use mcp__ruflo__* in invocations and worker contracts.

Basic pattern (Pattern 1 — minimal Council)

// Phase 1: Substrate
mcp__ruflo__hive-mind_init({
  topology: "hierarchical-mesh",
  consensus: "byzantine",
  maxAgents: 3,
  persist: true,
  memoryBackend: "hybrid"
})

// Phase 2: Register slot registry
mcp__ruflo__hive-mind_spawn({
  count: 3,
  role: "worker",
  agentTypes: ["researcher", "researcher", "researcher"],
  prefix: "council"
})

// Phase 3: Workforce — Claude's Agent tool, ONE message, parallel
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: <persona-1 worker contract> })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: <persona-2 worker contract> })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: <persona-3 worker contract — Devil's Advocate> })

// Phase 4: Resolve consensus (after workers return)
mcp__ruflo__hive-mind_consensus({
  action: "propose",
  type: "council-verdict",
  value: <topic>,
  strategy: "byzantine"
})
// then per-vote calls + final tally

// Phase 5: Persist verdict
mcp__ruflo__hive-mind_memory({
  action: "set",
  type: "consensus",
  key: <session-id>,
  value: <transcript>
})

// Phase 6: Status / shutdown
mcp__ruflo__hive-mind_status({ verbose: true })
mcp__ruflo__hive-mind_shutdown({ graceful: true })

Calling convention (read this first)

Queen calls MCP tools directly. Same convention as swarm-advanced:

mcp__ruflo__hive-mind_init({...})       // ← preferred
mcp__ruflo__hive-mind_spawn({...})      // ← preferred
mcp__ruflo__hive-mind_consensus({...})  // ← preferred

Sub-agents (Agent-tool spawns) also call MCP directly. Per ADR-0144 Amendment §arm B (verified empirically 2026-05-04, 30ms server-side), sub-agents spawned via Claude's Agent tool can invoke mcp__ruflo__* without a ToolSearch preamble. The earlier prohibition was rescinded.

Inter-sub-agent messaging uses SendMessage (Agent Teams). When a council pattern requires workers to exchange messages directly — see Pattern 1 §Transports (b) — spawn them with team_name: "<council-id>" and use SendMessage({type:"message", recipient:"<peer>", content:"...", summary:"..."}). Requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 (typically pre-set in ruflo environments via ~/.claude/settings.json env block; verify with env | grep CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS). This is the upstream-blessed inter-agent messaging primitive per USERGUIDE L1683.

Reserve Bash("npx ... hive-mind ...") for two cases only:

• The --claude subprocess-as-queen pattern (CLI Fallback in each Pattern below)
• Environments where the MCP server is not running (CI, daemon-less smoke). Note: the Bash CLI path hangs on flock contention against a running MCP server (per ADR-0140 §Amendment row D direct) — it is NOT a viable fallback inside an active session.

Core concepts

Queen types (3)

The active queen mode shapes the prompt the queen reads at launch. All three carry verbatim prose blocks at forks/ruflo/v3/@sparkleideas/cli/src/commands/hive-mind.ts:617-744. Sentinels (the "before declaring done verify" anchor):

Queen type	Disposition	Sentinel	When to use
strategic	Architect-first; full plan tree before spawning workers	"written plan"	Research, planning, analysis. Long-horizon synthesis.
tactical	Dispatcher-first; spawn within first 3 cycles, ping every cycle	"spawned workers within"	Implementation, execution. Short-horizon decomposition.
adaptive	Mode-selecting; weighs subtask count, ambiguity, prior priors; calls _consensus before mid-run mode flips	"named your chosen mode"	Optimisation. Switches between Strategic and Tactical based on task complexity signals.

Capability scoring (used by QueenCoordinator to assign work) is queen-type-invariant. Weights from queen-coordinator.ts:1437-1442: capability 0.30, performance 0.25, load 0.20, health 0.15, availability 0.10.

Worker specialisations (8)

All 8 types share subagent_type: researcher when spawned via Claude's Agent tool — the persona is carried in the prompt body, not the type. The hive's per-type prose blocks are emitted by renderWorkerTypeBlocks at commands/hive-mind.ts:240-345 and follow the ADR-0126 structural contract (3 fixed sections). Reference table:

Type	One-line role	Primary tools
researcher	Gather context, surface prior art, recall similar past hives	memory_search, embeddings_search, memory_retrieve
coder	Implement planned changes; edit files, run tests, surface diffs	Read/Write/Edit/Bash, task_assign, hive-mind_memory
analyst	Profile, measure, surface bottlenecks	performance_metrics, performance_bottleneck, performance_report
architect	Author ADRs, weigh diff-level risk, define boundaries	analyze_diff, analyze_diff-risk, Write (ADR)
tester	Execute acceptance harness, write failing-first tests	Bash (test runners), task_status, hive-mind_memory
reviewer	Audit changes for risk, recommend reviewers	analyze_diff-risk, analyze_diff-reviewers, analyze_file-risk
optimizer	Tune neural and runtime hot paths	performance_bottleneck, neural_optimize, performance_optimize
documenter	Update USERGUIDE/README/ADR cross-references	Edit/Write (markdown), markdown-editor skill, memory_search

Unknown types throw Unknown worker-type for prompt: ${type} per feedback-no-fallbacks.md — never silently falls through.

Consensus algorithms (7)

The fork supports 7 algorithms (Majority was removed; it survives only as the formula Raft uses). Strategy is selected at hive-mind_init and per-_consensus call.

Algorithm	Vote schema	Fault tolerance	Resolves	When to use
weighted	Record<voterId, boolean>; queen identified via state.queen.agentId	Requires queen present (MissingQueenForWeightedConsensusError if not). Denominator (N-1) + 3	approved / rejected / pending	Queen-led decisions where leader vote should dominate (USERGUIDE "Queen 3x" contract)
byzantine (alias bft)	Map<voterId, vote>	f = floor((N-1)/3); requiredVotes = 2*f + 1	approved / rejected / pending	Worker-unreliability tolerance (NOT adversarial — workers share auth)
raft	Record<voterId, boolean> + term uniqueness	Simple majority floor(N/2)+1	approved / rejected / pending	Leader-elected single-decision rounds with term ordering
quorum	Record<voterId, boolean> + quorumPreset	Preset-driven: unanimous / majority / supermajority	approved / rejected / pending	Caller-chosen threshold; configurable strictness
gossip	ConsensusProposal extended with gossipRound, lastVoteChangedRound, totalNodes, currentRoundBroadcastSet[]	No threshold; settles when gossipRound >= ceil(log₂N) AND no recent vote change. Hard budget 2·ceil(log₂N) rounds	{settled, result, round} / {exhausted, ...} / {settled:false, gossipRound, bound}	Eventual-consistency advisory rounds for small N (4-32); tolerates voter dropouts
crdt	Per-voter crdtState: { votes: GCounter, approvers: ORSet, verdict: LWWRegister }	No threshold; mathematical convergence under any message order	Merged {verdict.value(), approvers.elements(), votes.value()}	Conflict-free state-merging; re-broadcast safety dominates

Note on _status.consensus: per A2 finding, hive-mind_status returns consensus: 'byzantine' hardcoded regardless of init's persisted strategy. Read state.config.consensus from state.json directly if you need the truth.

Memory types (8 typed buckets)

All 8 share one entry shape { value, type, ttlMs, expiresAt, createdAt, updatedAt } keyed in state.sharedMemory. Eviction is lazy on get/list plus periodic sweep (CLAUDE_FLOW_HIVE_SWEEP_MS, default 60s). All 4 actions take withHiveStoreLock.

Type	Default TTL	Purpose
knowledge	null (permanent)	Learned facts to retain across sessions
context	1 hour	Per-session ambient state
task	30 minutes	Task-scoped data; cleared after completion window
result	null (permanent)	Task completion outputs; durable artifacts
error	24 hours	Failures, exceptions; post-mortem window
metric	1 hour	Counters, latencies; observability rollups
consensus	null (permanent)	Ratified votes; consensus history
system	null (permanent)	Bookkeeping, legacy migrations

Validation is fail-loud: missing type throws MissingMemoryTypeError, unknown type throws InvalidMemoryTypeError, non-finite ttlMs throws InvalidTTLError.

LRU cache: RUFLO_HIVE_CACHE_MAX default 1024 entries; classic move-to-front on hit. RVF is primary backend; per-write appendFile + fdatasync ensures power-loss durability on Linux (per ADR-0130). macOS is bounded by disk write cache.

Topologies (6)

Selected at hive-mind_init via topology parameter. Wires the queen prompt (per-topology dispatch at hive-mind.ts:91) and worker visibility surface.

Topology	Routing	Leader	When to use
mesh	Full peer broadcast; shared peer-visible memory	Static queen	Full peer collaboration; O(N²) acceptable
hierarchical	Queen-only broadcast; private worker namespaces	Static queen	Queen-summarised aggregation; no peer cross-talk needed
hierarchical-mesh	Mesh within sub-hive; sub-queens summarise to top queen	Top queen + 1 sub-queen per cluster (1-level cap)	Scale beyond mesh while keeping local cohesion
ring	Deterministic chain via hive-mind_memory; broadcasts disabled	Queen exists; coordination is P2P along ring edges	Deterministic ordered pipelines
star	Hub-and-spoke; queen sole memory writer	Queen as exclusive writer	Strict queen-writes-only audit/aggregation
adaptive	Meta-topology; defers to T9 control loop, resolves to one of the five at spawn time	Inherits from resolved target	Load-variable workloads

Adaptive autoscale config: poll 5s, settle/dampen 30s, high-water queue depth >3, low-water 0, CoV thresholds 0.6 (→ mesh) / 0.3 (→ hierarchical), max 4 flips/hour per axis (circuit-breaker halts loud), 3 dampening windows mid-task switch deferral.

Pattern 1: Council Hive (Dialectic)

Purpose

Convene N named experts for dialectic review of a proposition. Each expert takes a clear stance citing their published methodology; one is the Devil's Advocate. Workers cross-engage by name with specific claims. Queen composes a structured transcript.

Architecture

• Substrate: hive-mind init with consensus: "byzantine" (or weighted if queen vote should dominate), topology hierarchical-mesh (or mesh for ≤6 panellists)
• Protocol layer: project methodology file (e.g. ONT-0021) OR shipped templates/generic-council-protocol.md (see Piece 2)
• Workforce: N parallel Agent spawns, all subagent_type: researcher, persona in prompt body, run_in_background: true. ONE round of spawns.
• Cross-talk: queen-composed by default. For runtime cross-talk see "Transports" below.
• Vote: per-expert stance via _consensus({action: "vote"}); queen tallies via _consensus({action: "status"}).
• Transcript: queen composes from N return values. Every quotation traces to actual worker output (composition is legitimate; fabrication is illegitimate).

Workflow

// Phase 1: Substrate (queen)
mcp__ruflo__hive-mind_init({
  topology: "hierarchical-mesh",
  consensus: "byzantine",
  queenType: "strategic",
  maxAgents: 5,
  persist: true,
  memoryBackend: "hybrid"
})

// Phase 2: Read protocol layer (queen → Read tool)
// Read <project>/CLAUDE.md for the council-anchoring rule.
// Read <project>/<methodology>.md if the project ships one.
// Else read this skill's templates/generic-council-protocol.md.

// Phase 3: Register slot registry (queen)
mcp__ruflo__hive-mind_spawn({
  count: 5, role: "worker",
  agentTypes: ["researcher","researcher","researcher","researcher","researcher"],
  prefix: "council"
})

// Phase 4: Panellist spawn — ONE message, parallel
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: <persona-1 contract — see templates/worker-contract.md> })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: <persona-2 contract> })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: <persona-3 contract> })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: <persona-4 contract> })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: <persona-5 contract — Devil's Advocate flag set> })

// Phase 5: Vote (after all 5 return — queen tallies)
mcp__ruflo__hive-mind_consensus({
  action: "propose",
  type: "council-verdict",
  value: <proposition>,
  strategy: "byzantine",
  timeoutMs: 30000
})
// Per-voter ballots populated from worker return values:
mcp__ruflo__hive-mind_consensus({
  action: "vote", proposalId, voterId: "expert-1", vote: true
})
// ... × N
mcp__ruflo__hive-mind_consensus({
  action: "status", proposalId
})

// Phase 6: Transcript composition (queen → Write tool)
// 8-section format: agenda → positions → cross-expert discussion →
// vote table → findings → verdict → signatures.
// Use ONLY actual worker content. Each quotation traces to a return value.

// Phase 7: Persist verdict (queen)
mcp__ruflo__hive-mind_memory({
  action: "set",
  type: "consensus",
  key: `council-${sessionId}`,
  value: JSON.stringify({ transcript, verdict, signatures })
})

// Phase 8: Status / shutdown
mcp__ruflo__hive-mind_status({ verbose: true })
mcp__ruflo__hive-mind_shutdown({ graceful: true })

Transports (pick ONE for cross-talk)

Listed in order of preference. Pick the first one whose preconditions are met for your task.

(a) Queen-composed default — preferred for one-round councils. Workers don't see each other at runtime; queen reads N return values and composes the inter-expert discussion in main thread. Composition is legitimate when every quotation traces to actual worker output. This is the canonical pattern that produced 250+ working pre-regression council sessions — workers were one-shot independent returners, queen composed transcripts from real content. Use when:

• Workers don't need to revise their position after seeing peers (most one-round dialectic fits this)
• Latency budget is tight (no barrier; bottleneck is the slowest worker, not crosstalk overhead)

(b) SendMessage via Agent Teams — preferred for runtime cross-talk when workers must revise. Spawn workers with team_name: "<council-id>"; workers exchange messages via SendMessage({type:"message", recipient:"<peer-name>", content:"<text>", summary:"<one-line>"}). Upstream-blessed inter-agent messaging primitive (USERGUIDE L1683 "Mailbox: SendMessage — Inter-agent messaging for coordination"). Requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 (typically pre-set in ruflo environments via ~/.claude/settings.json env block). Use when:

• Workers must respond to peer claims before settling their final position
• You want the messaging surface that has hooks (teammate-idle, task-completed) and lifecycle (TeamCreate/TeamDelete) wired in

// Queen sets up team
TeamCreate({ team_name: "council-2026-05-05", description: "ADR-0140 review" })

// Spawn teammates with team_name
Task({ subagent_type: "researcher", team_name: "council-2026-05-05",
       name: "strategist", run_in_background: true,
       prompt: "<persona contract; messages peers via SendMessage>" })
// ... × N

// Inside each worker prompt, the agent uses:
//   SendMessage({ type:"message", recipient:"realist",
//                 content:"On Q3 you said X — I think Y because...",
//                 summary:"counter on Q3" })
//   (peer agent receives the message via its own mailbox)

// Queen cleanup
TeamDelete()

(c) Direct MCP _memory — preferred when you need durable, typed, cross-session crosstalk. Workers call mcp__ruflo__hive-mind_memory({action:"set", type:"context", key:"<persona>-pos", value:"..."}) and ({action:"get", key:"<peer>-pos"}). Typed buckets (8 types with TTLs), atomic under withHiveStoreLock, RVF-backed durable, 100% concurrent-write durability bar (ADR-0123). Verified working from sub-agent 2026-05-04 (ADR-0144 arm B). Use when:

• Verdict needs to survive session restart (transport (a)/(b) lose state)
• Long-running multi-round councils where memory of prior rounds matters
• You want the typed-bucket model (type:"context" for in-flight; type:"consensus" for final verdict)

(d) File-based via /tmp/<hive-id>/ — fallback only. Workers Write pos-<name>.md → sleep 60 barrier → Read pos-<peer>.md → Write reaction-<name>.md. Validated empirically 2026-05-04 (memory reference-hive-runtime-crosstalk-pattern.md). Use only when:

• CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS is not enabled AND
• The MCP server is unreliable AND
• Observability (ls /tmp/<hive-id>/) is more valuable than performance

This was the 2026-05-04 default before SendMessage and arm-B-verified MCP were considered. It's now demoted: the barrier sleep adds wall-clock latency without functional value over (b)/(c).

Transport selection cheat-sheet

Need	Transport
One-round dialectic; queen synthesises	(a) queen-composed
Workers must revise after seeing peers	(b) SendMessage
Cross-session durability; typed buckets	(c) _memory
MCP server down + Agent Teams unavailable	(d) file-based

CLI Fallback (subprocess-as-queen)

Use only when running this skill from a fresh terminal AND the spawned-claude-as-queen flow is specifically desired:

npx @sparkleideas/cli@latest hive-mind spawn -n 5 \
  --queen-type strategic --consensus byzantine \
  --worker-types researcher,researcher,researcher,researcher,researcher \
  --claude --objective "<council question>"

Add --non-interactive when invoking from inside an existing claude session (TTY-inherit will fail otherwise — falls back to stream-json print mode).

Test backing

p3-hm-init / p3-hm-spawn / p3-hm-consensus / p3-hm-memory / p3-hm-shutdown smoke checks; mcp-tools-deep.test.ts behavioural; adr0119-weighted-consensus.test.mjs for weighted. Lifecycle chain via p3-hm-lifecycle.

Pattern 2: Consensus Decision Hive (BFT)

Purpose

Reach agreement on a discrete decision (architecture pattern, technology choice, release readiness) using formal consensus. No dialectic; just propose → vote → resolve. The decision is the artefact.

Architecture

• Substrate: hive-mind init with chosen consensus algorithm
• Workforce: N parallel Agent spawns, each receives the proposal and returns a vote with rationale
• Vote: strategy determines fault-tolerance (Byzantine 2f+1, Weighted queen×3, Raft simple majority, Quorum preset, Gossip eventual, CRDT convergent)
• No transcript. No protocol layer. The verdict + per-voter rationale are the deliverables.

Workflow

// Phase 1: Substrate
mcp__ruflo__hive-mind_init({
  topology: "mesh",
  consensus: "byzantine",
  queenType: "tactical",
  maxAgents: 5
})

// Phase 2: Slot registry
mcp__ruflo__hive-mind_spawn({ count: 5, role: "worker" })

// Phase 3: Propose
mcp__ruflo__hive-mind_consensus({
  action: "propose",
  type: "decision",
  value: { question: <Q>, options: [<A>, <B>, <C>] },
  strategy: "byzantine",
  timeoutMs: 60000
})

// Phase 4: Workforce — voters
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: `Vote on: <Q>. Options: <A>/<B>/<C>. Return choice + rationale.
                 Then call mcp__ruflo__hive-mind_consensus with
                 action:"vote", proposalId:"${proposalId}", voterId:"<id>",
                 vote:<true if A else false>.` })
// ... × N

// Phase 5: Resolve + persist
const result = mcp__ruflo__hive-mind_consensus({
  action: "status", proposalId
})
mcp__ruflo__hive-mind_memory({
  action: "set", type: "consensus",
  key: `decision-${proposalId}`, value: JSON.stringify(result)
})

// Phase 6: Shutdown
mcp__ruflo__hive-mind_shutdown({ graceful: true })

Consensus algorithm selection guidance

• Use byzantine when N ≥ 4 and you want fault-tolerant agreement (f < N/3 workers can fail).
• Use weighted when the queen is the authoritative voice and worker votes are advisory (queen counts ×3).
• Use raft when the proposition has term ordering (sequential decisions where each supersedes the prior).
• Use quorum when you need configurable strictness (unanimous for safety-critical; supermajority for governance; majority for default).
• Use gossip for eventually-consistent advisory rounds with small N (4-32) where voter dropouts are common.
• Use crdt when re-broadcast safety dominates (network partitions; out-of-order delivery).

CLI Fallback

npx @sparkleideas/cli@latest hive-mind spawn -n 5 \
  --queen-type tactical --consensus byzantine \
  --claude --objective "<decision question>"

Test backing

adr0119-weighted-consensus, adr0120-gossip-consensus, adr0121-crdt-consensus, plus mcp-tools-deep.test.ts consensus block. byzantine.ts:177 is the BFT formula.

Pattern 3: Implementation Hive (Coordinated Development)

Purpose

Coordinated development of a feature across N specialists, with consensus checkpoints on architectural decisions during execution. Hybrid pattern: consensus for the design vote → parallel specialist execution → consensus for the review vote.

Architecture

• Substrate: hive-mind init with consensus: "weighted" (queen guides; team executes)
• Workforce: mixed-type via agentTypes (architect + coders + tester + reviewer)
• Phase pattern: Architect-vote → parallel-build → review-vote → consensus on merge
• Decision points use _consensus; execution uses Agent-tool spawns.

Workflow

// Phase 1: Substrate
mcp__ruflo__hive-mind_init({
  topology: "hierarchical-mesh",
  consensus: "weighted",
  queenType: "tactical",
  maxAgents: 6
})

// Phase 2: Architect produces design
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: "<architect persona — produce ADR + design rationale>" })

// Phase 3: Consensus on architecture
mcp__ruflo__hive-mind_consensus({
  action: "propose",
  type: "architecture",
  value: { adr: <path>, rationale: <text> },
  strategy: "weighted"
})
// ... vote calls × N
mcp__ruflo__hive-mind_consensus({ action: "status", proposalId: <archId> })

// Phase 4: Parallel implementation (mixed worker types)
mcp__ruflo__hive-mind_spawn({
  count: 4, role: "worker",
  agentTypes: ["coder", "coder", "tester", "documenter"],
  prefix: "impl"
})
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: "<coder persona — backend>" })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: "<coder persona — frontend>" })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: "<tester persona — acceptance harness>" })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: "<documenter persona — USERGUIDE updates>" })

// Phase 5: Reviewer surfaces concerns
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: "<reviewer persona — risk assessment via analyze_diff-risk>" })

// Phase 6: Consensus on merge readiness
mcp__ruflo__hive-mind_consensus({
  action: "propose",
  type: "merge-readiness",
  value: { changes: <summary>, riskScore: <n> },
  strategy: "weighted"
})

// Phase 7: Persist outcome
mcp__ruflo__hive-mind_memory({
  action: "set", type: "result",
  key: `impl-${featureId}`,
  value: JSON.stringify({ archDecision, reviewVerdict, artefacts })
})

mcp__ruflo__hive-mind_status({ verbose: true })
mcp__ruflo__hive-mind_shutdown({ graceful: true })

CLI Fallback

npx @sparkleideas/cli@latest hive-mind spawn -n 6 \
  --queen-type tactical --consensus weighted \
  --worker-types coder,coder,tester,architect,reviewer,documenter \
  --claude --objective "<feature description>"

Test backing

adr0108-mixed-type-spawn.test.mjs (round-robin + mutex), adr0126-worker-type-prompts.test.mjs, plus the consensus tests above.

Pattern 4: Review Hive (Multi-perspective)

Purpose

Review existing code, design, or decision from N independent perspectives (security / performance / accessibility / architecture / etc.) with consensus on severity and required actions. Like Pattern 1 but reviewers cite their own checklists rather than published methodologies.

Architecture

• Substrate: hive-mind init with consensus: "quorum" (use quorumPreset: "majority" for default; "unanimous" if zero-tolerance; "supermajority" for governance). Note: consensus: "majority" is NOT a valid _consensus.strategy enum value (Majority was removed per ADR-0119 — survives only as the formula Raft uses). Use quorum with the majority preset instead.
• Workforce: N reviewers, each carrying a perspective brief (security / performance / accessibility / etc.)
• Findings: categorised VIOLATION / WARNING / OBSERVATION
• Per-finding consensus: severity vote per finding; final verdict aggregates

Workflow

// Phase 1: Substrate
mcp__ruflo__hive-mind_init({
  topology: "mesh",
  consensus: "quorum",
  queenType: "strategic",
  maxAgents: 4
})

// Phase 2: Slot registry
mcp__ruflo__hive-mind_spawn({
  count: 4, role: "worker",
  agentTypes: ["reviewer", "reviewer", "reviewer", "reviewer"],
  prefix: "review"
})

// Phase 3: Reviewer spawn — ONE message, parallel
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: "<security perspective — checklist + worker contract>" })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: "<performance perspective>" })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: "<accessibility perspective>" })
Agent({ subagent_type: "researcher", run_in_background: true,
        prompt: "<architectural perspective>" })

// Phase 4: Per-finding cross-engagement (transport b — file-based)
// Workers cross-reference each other's findings via /tmp/<hive-id>/finding-*.md.

// Phase 5: Severity vote per finding
for (const finding of findings) {
  mcp__ruflo__hive-mind_consensus({
    action: "propose",
    type: "finding-severity",
    value: { finding: finding.id, candidates: ["VIOLATION","WARNING","OBSERVATION"] },
    strategy: "quorum",
    quorumPreset: "majority"
  })
  // ... per-voter ballots ...
}

// Phase 6: Findings table + verdict
mcp__ruflo__hive-mind_memory({
  action: "set", type: "result",
  key: `review-${reviewId}`,
  value: JSON.stringify({ findings, verdicts, recommendations })
})

CLI Fallback

npx @sparkleideas/cli@latest hive-mind spawn -n 4 \
  --queen-type strategic --consensus quorum \
  --worker-types reviewer,reviewer,reviewer,reviewer \
  --claude --objective "Review <target>"

Failure handling

The fork ships two verbatim protocol blocks at commands/hive-mind.ts:495-595 that the queen reads on launch. They are load-bearing — workers transitioning to failed-quorum-not-reached do so via runtime auto-status transitions, and sub-queen escalation in hierarchical-mesh follows a data-driven hybrid (promote-worker if ≥1 healthy in sub-hive; else escalate-to-root).

WORKER FAILURE PROTOCOL (commands/hive-mind.ts:495-536)

Sentinels: 'absent', 60s, retry-once, worker-<id>-status. Behaviour:

1. Immediate readback — after spawn, queen reads worker-<id>-status keys at every coordination cycle.
2. Record absence — 60s with no result key → mark 'absent'. Never wait indefinitely.
3. Retry-once OR proceed — at most one retry (retryOf lineage pointer; ID convention worker-<original-id>-retry-1). No retry-2.
4. Quorum handling — runtime auto-transitions: when Date.now() >= proposal.timeoutAt && totalVotes < requiredVotes, status moves pending → failed-quorum-not-reached with absentVoters snapshot. Queen does NOT mark manually.

Worker rejoin after failure throws WorkerAlreadyFailedError (synchronous; never silently re-admits).

SUB-QUEEN FAILURE PROTOCOL (commands/hive-mind.ts:538-595)

Triggers: missing summary key >60s, Task error, or unresponsive hive-mind_status probe. Strategy is data-driven, not policy-driven:

• promote-worker if ≥1 healthy worker remains in sub-hive — longest-lineage worker promoted; sub-hive boundary preserved.
• escalate-to-root if zero healthy workers — entire subtree marked FAILED; orphaned workers absorbed into top tier.

Recursion cap: 1 nesting level (no sub-sub-queens). Both paths emit queen.subqueen.failure event + persist SubQueenFailureRecord to hive-mind/<top-queen>/sub-queen-failures/<subQueenId>. Handler is idempotent.

Lock-wrap caveats (open follow-up)

hive-mind_init, _spawn, _memory are wrapped in withHiveStoreLock. _join, _leave, _broadcast, _shutdown are NOT. If you race these against _init or _spawn you may see torn writes — serialise externally where it matters.

Session lifecycle

The session model is simpler than the prose suggests: a single live state.json file plus zero-or-more immutable archives. There are no paused, stopped, completed, or archived states. Five subcommands:

npx @sparkleideas/cli@latest hive-mind sessions list
npx @sparkleideas/cli@latest hive-mind sessions checkpoint
npx @sparkleideas/cli@latest hive-mind sessions export <id> --output <path>
npx @sparkleideas/cli@latest hive-mind sessions import <path>
npx @sparkleideas/cli@latest hive-mind resume <id>

Archive schema (v1)

interface SessionArchiveV1 {
  schemaVersion: 1;
  hiveState: HiveState;          // typed memory shape per ADR-0122
  queenPrompt: string;           // non-empty, required
  queenType?: HiveQueenType;     // "strategic" | "tactical" | "adaptive"
  workerManifest: { id: string; type: string; manifest?: Record<string, unknown> }[];
  timestamp: string;             // iso8601
}

Format: gzipped JSON at .claude-flow/hive-mind/sessions/<sessionId>-<sanitised-iso>.json.gz. Mismatch on schemaVersion !== 1 throws SessionArchiveSchemaMismatchError.

Resume semantics

resume re-spawns via detached child_process.spawn('claude', [queenPrompt, '--continuation', sessionId], { detached, unref }). Spawnability is probed BEFORE state mutation. queenType, queenPrompt, and workerManifest are restored verbatim into typed memory at well-known keys (hive-mind/queen-prompt, hive-mind/worker-manifest).

import does NOT auto-resume — it places the archive under a fresh imported-<ts>-<rand> sessionId. Call resume separately if needed.

Best practices

1. Queen calls MCP tools directly (mcp__ruflo__hive-mind_*({...})) — same convention as swarm-advanced. Reserve npx for CLI-Fallback subsections and rare subprocess-as-queen scenarios.
2. Pick the right pattern. Don't shoehorn a Council Hive (Pattern 1) into a BFT decision (Pattern 2 fits) and don't run Pattern 2 when you need dialectic (Pattern 1 fits). Implementation work uses Pattern 3; reviews use Pattern 4.
3. Always spawn workers in ONE message. Parallel spawn = sync barrier. Sequential Agent calls serialise needlessly.
4. Sub-agents call MCP directly (post-ADR-0144 arm B). The Bash CLI fallback is structurally broken when the MCP server is running (flock contention).
5. The queen composes; the queen does not fabricate. Every expert quotation in a transcript traces to actual worker output.
6. Devil's Advocate must explicitly withdraw or hold. No vague "all agreed" closes — the DA either acknowledges the rationale won the argument or holds principled dissent.
7. Persist verdicts via _memory with type: "consensus" (or type: "result" for non-vote outputs). The TTL is permanent; survives session restart.
8. Honour the WORKER FAILURE PROTOCOL. 60s timeout, retry-once, never silently drop.

Real-world examples

• Pre-regression source — forks/ruflo/v3/@sparkleideas/cli/src/commands/hive-mind.ts at commit 0590bf29c (the substrate-only queen prompt that produced 250+ working council sessions before the late-March 2026 regression).
• File-based crosstalk validation 2026-05-04 — memory reference-hive-runtime-crosstalk-pattern.md (iter2/iter3 with Karpathy/Norman/Kambhampati personas; all 3 reactions cross-referenced peers by name with specific claims).
• Live _consensus strategy verification — adr0119-weighted-consensus.test.mjs, adr0120-gossip-consensus.test.mjs, adr0121-crdt-consensus.test.mjs.

Troubleshooting

Sub-agent MCP call hangs 600s

Tool-name mismatch. Use mcp__ruflo__* (registered). mcp__ruflo__* is not registered until ADR-0117 lands; calls to it dispatch to nothing and trigger the per-Agent watchdog.

--claude fails from inside an existing claude session

Add --non-interactive. The default TTY-inherit subprocess pattern requires a fresh terminal; from inside an active session, fall back to stream-json print mode.

_status.consensus shows 'byzantine' but I configured weighted

Known: hive-mind_status returns consensus: 'byzantine' hardcoded. Read state.config.consensus from state.json directly for the persisted strategy.

hive-mind_broadcast doesn't reach my Agent-tool workers

Two distinct workforces: hive-mind spawn registers slots that broadcast reaches; Agent-tool spawns are NOT bridged. Single-round Agent spawns + queen-composition is the canonical pattern. Use _memory writes from inside worker prompts if you need cross-worker state visibility.

No consensus reached (Byzantine deadlock)

Drop to weighted (queen ×3) or quorum with majority preset. Or restructure the proposition to be binary rather than n-ary.

CLI flags accepted but not persisted

Inspect .claude-flow/hive-mind/state.json after init. The topology / consensus / memoryBackend fields land in state.config (per ADR-0140 §Piece 3 row 3c, fixed in commit b7181aa89).

Related skills

• swarm-advanced — multi-agent coordination without consensus; pick this when you need parallel work and don't need a verdict.
• claude-flow-swarm — swarm CLI-first patterns.
• reasoningbank-agentdb — adaptive learning from hive outcomes.

Swarm vs Hive — when to use which

Dimension	Swarm	Hive
Architecture	Topology of peer agents (mesh / hierarchical / hybrid)	Queen-led hierarchy with strategic/tactical/adaptive coordinator
Coordination	Shared memory + parallel_execute + task_orchestrate	Queen prompts workers; consensus algorithm resolves decisions
Decision-making	Not a first-class concern	Primary feature — Byzantine / Raft / Gossip / CRDT / Weighted / Quorum
Memory	Per-namespace memory_usage with TTLs	Typed buckets (8 types) per ADR-0122; LRU + WAL
Failure handling	Implicit per-task retry	Worker-failure protocol (ADR-0131): retry-once, quorum-with-loss, lineage
Output	Distributed work artefacts (report, code, tests)	A verdict or vote, plus the work artefacts
Best canonical analogy	A team of contractors working in parallel	A jury (or panel of experts) reaching a ruling

Use Swarm when: parallel work where the goal is throughput, not consensus. Research / development / testing / analysis where each agent contributes a distinct artefact.

Use Hive when: you need a decision (architecture pattern, technology choice, code review approval, release readiness, severity vote on a finding). Multiple perspectives must reconcile into a single verdict. Fault tolerance matters.

Overlap (judgment call): Implementation work with mid-task design decisions → Pattern 3 (Implementation Hive) — start as a hive for the design vote, parallel-execute, return to hive for review consensus.

References

• ADR-0139 — canonical spec from upstream guidance registry
• ADR-0140 — implementation outline
• ADR-0145 — research collection that this skill consumes
• ADR-0118 — T1-T14 runtime closure (all complete)
• ADR-0119–0128, ADR-0130, ADR-0131, ADR-0132, ADR-0108 — per-task contracts
• ADR-0144 §Amendment — sub-agent MCP transport rule (post-arm-B)
• Memory reference-hive-runtime-crosstalk-pattern — file-based fallback transport
• Memory feedback-hive-discussion-mechanics — 5-point dialectic criteria

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Skill version: 2.0.0 Last updated: 2026-05-05 Maintained by: Ruflo Fork Team License: MIT