MCP HubMCP Hub
스킬 목록으로 돌아가기

scale-colony

pjt222
업데이트됨 2 days ago
3 조회
17
2
17
GitHub에서 보기
기타ai

정보

이 스킬은 생물 군집을 모델로 하여 출아와 역할 분화 같은 메커니즘을 활용해 분산 시스템과 팀의 확장 전략을 제공합니다. 성장 단계를 인식하고 규모 증가에 따른 조정 실패를 방지하기 위한 아키텍처 전환을 구현하는 데 도움을 줍니다. 의사소통 부담이 생산적 성과를 초과하거나, 소규모에서는 작동하던 시스템이 성장하며 고장 나는 경우에 사용하세요.

빠른 설치

Claude Code

추천
기본
npx skills add pjt222/agent-almanac -a claude-code
플러그인 명령대체
/plugin add https://github.com/pjt222/agent-almanac
Git 클론대체
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/scale-colony

Claude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요

문서

Scale Colony

Scale distributed sys|teams|orgs → budding (split), role diff (age polyethism), growth-triggered arch transitions — maintain coord quality as colony grows beyond initial design.

Use When

  • Worked @ 10 agents, breaks @ 50
  • Comms overhead > productive output
  • Implicit coord patterns need explicit
  • Plan growth → scale proactive
  • Coord fails correlate w/ size (lost msgs, dup work, unclear ownership)
  • Existing sys needs split → semi-autonomous sub-colonies

In

  • Required: Current size + target growth
  • Required: Current coord mechanisms + stress points
  • Optional: Structure (flat|hierarchical|clustered)
  • Optional: Role diff already in place
  • Optional: Growth timeline + constraints
  • Optional: Inter-colony coord needs (if splitting)

Do

Step 1: Recognize Growth Phase

Identify scaling phase → apply right strategy.

  1. Classify phase:
Colony Growth Phases:
┌───────────┬──────────────┬───────────────────────────────────────────┐
│ Phase     │ Size Range   │ Characteristics                           │
├───────────┼──────────────┼───────────────────────────────────────────┤
│ Founding  │ 1-7 agents   │ Everyone does everything, direct comms,   │
│           │              │ implicit coordination, high agility       │
├───────────┼──────────────┼───────────────────────────────────────────┤
│ Growth    │ 8-30 agents  │ Roles emerge, some specialization, comms  │
│           │              │ overhead increases, need for structure     │
├───────────┼──────────────┼───────────────────────────────────────────┤
│ Maturity  │ 30-100 agents│ Formal roles, layered coordination,       │
│           │              │ sub-groups form, inter-group coordination  │
├───────────┼──────────────┼───────────────────────────────────────────┤
│ Fission   │ 100+ agents  │ Colony too large for single coordination  │
│           │              │ framework, must bud into sub-colonies     │
└───────────┴──────────────┴───────────────────────────────────────────┘
  1. Stress signals:
    • Comms overload: msgs/agent/day grows faster than colony size
    • Decision latency: proposal→decision time ↑
    • Coord failures: dup work, dropped tasks, conflicting actions ↑
    • Knowledge dilution: newcomers slow to productive
    • Identity loss: agents can't describe purpose consistently
  2. About to cross phase boundary or already crossed?

→ Clear phase ID + stress signals indicating approach|cross.

If err: phase unclear → measure 3 metrics: comm vol/agent, decision latency, coord fail rate. Plot over time. Inflection points = phase transitions. No metrics → likely Founding (where metrics not yet needed).

Step 2: Role Differentiation (Age Polyethism)

Progressive specialization → roles by experience + colony needs.

  1. Role progression:
    • Newcomers: observation, learning, simple (low autonomy, high guidance)
    • Workers: standard exec, signal following (mod autonomy)
    • Specialists: domain expertise, complex tasks, mentor newcomers (high autonomy)
    • Foragers/Scouts: exploration, innovation, external interface (see forage-resources)
    • Coordinators: inter-group comms, conflict resolution, quorum mgmt
  2. Role transitions:
    • Triggered by experience thresholds, not appointment
    • Agent done threshold tasks successfully → next role (calibrate by complexity + growth rate — 5-10 simple, 20-30 specialist)
    • Reverse possible (specialist → worker in new domain)
    • Distribution adapts to needs:
      • Growing → more newcomer slots, active mentoring
      • Stable → balanced across all roles
      • Threatened → more defenders, fewer scouts (see defend-colony)
  3. Preserve flexibility:
    • No agent permanently locked
    • Emergency protocols can temp reassign any agent any role
    • Cross-training → cover adjacent roles

→ Roles where agents progress simple→complex, distribution reflects needs+phase.

If err: rigid silos → ↑cross-training + rotation freq. Newcomers struggle progress → mentoring insufficient — pair w/ specialist for first N tasks. Too many in one role → triggers miscalibrated — adjust by colony-wide demand.

Step 3: Restructure Coord for Scale

Adapt mechanisms from coordinate-swarm for size.

  1. Replace direct comms → layered signaling:
    • Founding: everyone→everyone (N×N)
    • Growth: cluster squads of 5-8; direct in squad, signal between
    • Maturity: squads → departments; intra-squad direct, inter-squad signal, inter-dept broadcast
  2. Coord layers:
    • Local: in squad, direct signal exchange (stigmergy)
    • Regional: between squads same dept, aggregated signals
    • Colony: between depts, broadcast only for colony-wide decisions
  3. Inter-layer interfaces:
    • Each squad has 1 designated communicator who aggregates+relays
    • Communicators filter noise: not every local signal relayed up
    • Colony broadcasts rare → quorum, alarm escalation, major state changes
  4. Comms overhead budget:
    • Target: each agent <20% capacity on coord
    • Measure actual; exceed → add layer or split oversized squad

→ Layered coord, comms overhead grows logarithmic (not linear) w/ size. Local fast direct; colony-wide slower but functional.

If err: layers create info bottlenecks (communicators overloaded) → add redundant communicators or ↓relay freq. Layers create isolation (squads don't know others) → ↑inter-layer signal freq or cross-squad liaison roles.

Step 4: Execute Budding (Fission)

Split colony → semi-autonomous sub-colonies when exceeds single-coord capacity.

  1. Fission triggers:
    • 100 agents (or coord layer count >3)

    • Comms overhead >30% capacity despite layering
    • Decision latency exceeds time-sensitive thresholds
    • Subgroups have distinct identities + can operate independent
  2. Plan fission:
    • Identify natural split lines (existing clusters, domain bounds, geo)
    • Each daughter has viable role distribution (can't split all specialists into one)
    • Each must have: ≥1 coordinator, sufficient workers, access to shared resources
    • Define inter-colony interface: what shared, what independent
  3. Execute split:
    • Announce plan + timeline (consensus required — see build-consensus)
    • Transfer agents → daughters by existing cluster membership
    • Establish inter-colony channels (lightweight, async)
    • Each daughter bootstraps own local coord (inheriting from parent)
  4. Post-fission stabilization:
    • Monitor each for viability (sustains itself?)
    • Inter-colony coord minimal (quarterly sync, not daily)
    • Failed daughter → reabsorb into nearest viable

→ ≥2 viable daughters semi-autonomous w/ own coord, connected by lightweight interfaces.

If err: daughters too small → fission premature; remerge + retry larger. Inter-colony coord as heavy as pre-fission → split lines wrong, too interdependent. Re-draw on natural independence.

Step 5: Monitor Limits + Adapt

Continuous assess: structure matches size+needs?

  1. Scaling health metrics:
    • Coord overhead ratio: time coord/time produce
    • Decision throughput: decisions/time (↑ or steady w/ growth)
    • Agent satisfaction: engagement, retention, purpose (drops on fail)
    • Err rate: coord fails/time (not linear w/ growth)
  2. Limit indicators:
    • Overhead ratio >25% → more automation or layer
    • Throughput declining → governance needs revision
    • Turnover spiking → cultural|structural issues
    • Err rate accelerating → coord failing
  3. Trigger adapt:
    • Phase transition → apply Step 1 strategy
    • Limit reached → escalate (role diff → coord restructure → fission)
    • External change (market, tech) → may need transformation (see adapt-architecture)

→ Colony monitors own health + proactively adapts before stress = failure.

If err: no metrics → lacks observability — build measurement before more structure. Metrics show problems but can't adapt → resistance cultural not technical — address human factors (fear, ownership, trust) before restructure.

Check

  • Phase ID'd w/ specific stress signals
  • Role diff defined w/ progressive specialization
  • Coord layered for size
  • Comms overhead <20-25% capacity
  • Fission plan exists for >single-coord capacity
  • Health metrics tracked + thresholds trigger adapt
  • Daughter colonies (post-fission) viable distribution

Traps

  • Scale structure pre-needed: Premature layering = overhead w/o benefit. 10-team doesn't need dept coordinators. Stress signals guide.
  • Preserve founding culture at all costs: 5-agent ways break @ 50. Scaling needs evolution; nostalgia prevents adaptation.
  • Fission w/o independence: Sub-colonies still depend daily → worst of both — coord overhead + separation overhead.
  • Uniform role distribution: Not every sub-colony needs same ratios. Research → more scouts; production → more workers.
  • Ignore remerge: Sometimes fission fails; remerge best move. Treating fission irreversible prevents recovery.

  • coordinate-swarm — foundational patterns this skill scales
  • forage-resources — scales diff than production; role diff affects scout alloc
  • build-consensus — must adapt for larger groups
  • defend-colony — defense scales w/ colony
  • adapt-architecture — morphic skill for structural transformation
  • plan-capacity — capacity planning for growth
  • conduct-retrospective — identify stress before failure

GitHub 저장소

pjt222/agent-almanac
경로: i18n/caveman-ultra/skills/scale-colony
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

연관 스킬

llamaguard

기타

LlamaGuard는 폭력 및 혐오 발언 등 6가지 안전 범주에서 LLM 입력과 출력을 조정하기 위한 Meta의 70-80억 파라미터 모델입니다. 94-95% 정확도를 제공하며 vLLM, Hugging Face 또는 Amazon SageMaker를 사용해 배포할 수 있습니다. 이 기술을 사용하여 AI 애플리케이션에 콘텐츠 필터링 및 안전 가드레일을 손쉽게 통합하세요.

스킬 보기

cost-optimization

기타

이 Claude Skill은 리소스 적정화, 태깅 전략, 지출 분석을 통해 개발자들이 클라우드 비용을 최적화할 수 있도록 지원합니다. AWS, Azure, GCP에서 클라우드 비용을 절감하고 비용 거버넌스를 구현하기 위한 프레임워크를 제공합니다. 인프라 비용을 분석하거나, 리소스를 적정화하거나, 예산 제약을 충족해야 할 때 사용하세요.

스킬 보기

quantizing-models-bitsandbytes

기타

이 스킬은 bitsandbytes를 사용하여 LLM을 8비트 또는 4비트 정밀도로 양자화하며, 최소한의 정확도 손실로 50-75%의 메모리 감소를 달성합니다. 제한된 GPU 메모리에서 더 큰 모델을 실행하거나 추론을 가속화하는 데 이상적이며, INT8, NF4, FP4와 같은 형식을 지원합니다. 이 스킬은 HuggingFace Transformers와 통합되어 QLoRA 학습 및 8비트 옵티마이저를 가능하게 합니다.

스킬 보기

dispatching-parallel-agents

기타

이 Claude Skill은 3개 이상의 독립적인 문제를 동시에 조사하고 해결하기 위해 다중 에이전트를 배치합니다. 공유 상태나 의존성 없이 해결 가능한 무관련 장애 시나리오에 맞게 설계되었습니다. 핵심 기능은 병렬 문제 해결로, 각 독립 문제 영역마다 하나의 에이전트를 할당하여 효율성을 극대화합니다.

스킬 보기