coordinate-swarm
关于
The coordinate-swarm skill provides patterns like stigmergy and quorum sensing for building distributed systems that self-coordinate without central control. It helps developers design resilient, event-driven architectures by focusing on signal design, agent autonomy, and tuning emergent behavior. Use it when you need to eliminate coordination bottlenecks or replace fragile orchestration with decentralized coordination.
快速安装
Claude Code
推荐npx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/coordinate-swarm在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
Coordinate Swarm
Stigmergy + local rules + quorum → coherent collective, no central ctrl.
Use When
- Distributed sys → no central bottleneck
- Self-coord teams → no mgr overhead
- Event-driven arch → shared state, not direct msg
- Works @3 agents → breaks @30 → scale
- Bootstrap new swarm domain (
forage-resources,build-consensus) - Replace fragile central orch → resilient emergent
In
- Required: Agents desc (workers, services, team)
- Required: Collective goal / target behavior
- Optional: Current coord + fail modes
- Optional: Agent count → pattern choice
- Optional: Latency tolerance (realtime vs eventual)
- Optional: Env constraints (shared state, bandwidth)
Do
Step 1: Classify Problem
- Map: who agents, what do, where coord breaks
- Classify:
- Foraging → search distributed res (
forage-resources) - Consensus → agree collective decision (
build-consensus) - Construction → build shared structure
- Defense → detect threats (
defend-colony) - Division of labor → self-organize roles
- Foraging → search distributed res (
- Fail mode:
- Single point fail (central ctrl)
- Comm bottleneck (too many msg)
- Coherence loss (drift, no feedback)
- Rigidity (no adapt)
→ Clear class + fail mode → pattern choice.
If err: no single class → composite → decompose. Heterogeneous → layered coord (homogeneous clusters + inter-cluster stigmergy).
Step 2: Design Signals
Indirect comm channels.
- Shared env (DB, queue, FS, board)
- Signal types:
- Trail: accumulate on success paths (ant pheromone)
- Threshold: counter → behavior switch
- Inhibition: repel from exhausted areas
- Props:
- Decay: fade rate → no stale dominance
- Reinforce: success strengthens
- Radius: propagation range
- Signal → behavior map:
- Signal X > T → action A
- A done → deposit Y
- No signal → default explore
Signal Design Template:
┌──────────────┬───────────────────┬──────────────┬────────────────────┐
│ Signal Name │ Deposited When │ Decay Rate │ Agent Response │
├──────────────┼───────────────────┼──────────────┼────────────────────┤
│ success-trail│ Task completed OK │ 50% per hour │ Follow toward │
│ busy-marker │ Agent starts task │ On completion│ Avoid / pick other │
│ help-signal │ Agent stuck >5min │ 25% per hour │ Assist if nearby │
│ danger-flag │ Error detected │ 10% per hour │ Retreat & report │
└──────────────┴───────────────────┴──────────────┴────────────────────┘
→ Signal table: deposit conds + decay + responses. Simple + composable.
If err: too complex → 2 signals (attract/repel). Add nuance after basic works.
Step 3: Local Rules
Simple rules, local info only.
- Perception radius (what sense?)
- 3-7 rules, priority order:
- Rule 1 (safety): danger-flag → flee
- Rule 2 (response): help-signal + idle → move toward
- Rule 3 (exploit): success-trail → follow strongest
- Rule 4 (explore): no signal → random + unexplored bias
- Rule 5 (deposit): task done → deposit success-trail
- Each rule:
- Local: only what agent perceives
- Simple: one if-then
- Stateless (pref): no past mem
- Mental test → does collective behavior emerge?
→ Prioritized rules, independent exec → target behavior emerges.
If err: no emergence → feedback loop needed. Add signal for collective state + adjust rule.
Step 4: Quorum Thresholds
Trigger collective changes when enough agree.
- Collective decisions:
- Explore → exploit mode
- New worksite commit / abandon
- Normal → emergency
- Per decision:
- Threshold: # / % agents agreeing
- Window: signal count period
- Hysteresis: different on/off thresh → no osc
- Quorum = signal accumulation:
- Fav agent → vote-signal
- Votes > thresh in window → activate
- Votes < deact thresh → reverse
→ Leaderless decisions. Hysteresis gap → no rapid osc.
If err: oscillation → widen hyst gap (70/30). Never reaches quorum → lower thresh / widen window. Too slow → shrink window (beware premature).
Step 5: Test + Tune
- Pilot 5-10 agents
- Observe:
- Converges on behavior?
- How long?
- Conditions change mid-task → what?
- Agents fail / added → what?
- Tune params:
- Decay: fast → no memory; slow → stale dominates
- Quorum: low → premature; high → paralysis
- Explore/exploit balance: too explore → inefficient; too exploit → local optima
- Stress:
- Remove 30% agents → recover?
- Double count → still coord?
- Conflict signals → resolve / deadlock?
→ Tuned params, self-organizes, recovers, scales.
If err: stress fails → too tightly coupled. Simplify: fewer signals, faster decay, robust default. Swarm w/ zero-signal default > signal-dependent swarm.
Check
- Problem classified (foraging / consensus / construction / defense / labor)
- Signal table: deposit + decay + response
- Rules simple + local + prioritized (3-7)
- Quorum w/ hysteresis → no osc
- Small test → emergent behavior matches goal
- Stress test → graceful degradation
Traps
- Signal bloat: Too many types → confusion. Start 2 (attract/repel)
- Fake local: Rule needs global state → not local. Refactor
- No decay: Fossilized coord state. Half-life per task scale
- Zero hysteresis: Rapid osc. Deact < act always
- Homogeneity assumed: Diff caps → role-diff rules (
scale-colony)
→
forage-resources— res search + explore-exploitbuild-consensus— distrib agreement deep-divedefend-colony— collective defense on signal frameworkscale-colony— scaling past initial coordadapt-architecture— morphic arch transformdeploy-to-kubernetes— distrib sys deployplan-capacity— capacity + swarm scalingcoordinate-reasoning— AI self-variant; stigmergy → ctx mgmt
GitHub 仓库
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