MCP HubMCP Hub
스킬 목록으로 돌아가기

build-coherence

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

정보

이 스킬은 개발자가 여러 유효한 접근법이 존재할 때 구조화된 "벌 민주주의" 방식을 사용해 각 옵션을 독립적으로 평가하고 확신에 찬 합의에 도달하도록 돕습니다. 아키텍처 선택, 도구 선택의 근거 마련, 또는 고비용 작업을 확정하기 전에 사용하기에 이상적입니다. 주요 기능으로는 투명성을 위한 사고 과정 공개와 신뢰도 기반 의사결정 임계값을 위한 쿼럼 감지가 포함됩니다.

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/build-coherence

Claude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요

문서

Build Coherence

Evaluate competing approaches via independent assessment, explicit reasoning-out-loud advocacy, confidence-calibrated commitment thresholds, structured deadlock resolution — produce coherent decisions from multiple reasoning paths.

When Use

  • forage-solutions identified multiple valid approaches, selection must be made
  • Oscillating between two approaches without committing to either
  • Need to justify decision with structured reasoning (architecture choice, tool selection, implementation strategy)
  • Previous decision made by gut feeling, needs evidence-based validation
  • Internal reasoning producing contradictory conclusions, coherence must be restored
  • Before irreversible action (merging, deploying, deleting) where cost of wrong choice high

Inputs

  • Required: Two or more competing approaches to evaluate
  • Optional: Quality assessments from prior scouting (see forage-solutions)
  • Optional: Decision stakes (reversible, moderate, irreversible) for threshold calibration
  • Optional: Time budget for decision
  • Optional: Known failure mode (oscillation, premature commitment, groupthink)

Steps

Step 1: Independent Evaluation

Assess each approach on its own merits before comparing. Critical rule: don't let assessment of approach A bias assessment of approach B.

For each approach, evaluate independently:

Approach Evaluation Template:
┌────────────────────────┬──────────────────────────────────────────┐
│ Dimension              │ Assessment                               │
├────────────────────────┼──────────────────────────────────────────┤
│ Approach name          │                                          │
├────────────────────────┼──────────────────────────────────────────┤
│ Core mechanism         │ How does this approach solve the problem? │
├────────────────────────┼──────────────────────────────────────────┤
│ Strengths (2-3)        │ What does this approach do well?          │
├────────────────────────┼──────────────────────────────────────────┤
│ Risks (2-3)            │ What could go wrong? What is assumed?     │
├────────────────────────┼──────────────────────────────────────────┤
│ Evidence quality        │ How well-supported is this approach?      │
│                        │ (verified / inferred / speculated)        │
├────────────────────────┼──────────────────────────────────────────┤
│ Quality score (0-100)  │ Overall assessment                        │
├────────────────────────┼──────────────────────────────────────────┤
│ Confidence (0-100)     │ How confident in this assessment?         │
└────────────────────────┴──────────────────────────────────────────┘

Fill this out for each approach separately. Do not write comparison until all individual evaluations complete.

Got: Independent evaluations where each approach assessed on its own terms. Evaluation of approach B does not reference approach A. Quality scores reflect genuine assessment, not ranking.

If fail: Evaluations contaminated (you find yourself writing "better than A" while assessing B)? Reset. Assess A completely, then clear framing, assess B from scratch. Scores all identical? Evaluation dimensions too coarse — add domain-specific criteria.

Step 2: Waggle Dance — Reason Out Loud

Advocate for each approach proportionally to its quality. AI equivalent of bee waggle dance: make implicit reasoning explicit and public.

  1. For each approach, state case for it — as if presenting to skeptical user:
    • "Approach A is strong because [evidence]. Main risk is [risk], mitigated by [mitigation]."
  2. Advocacy intensity proportional to quality score:
    • High-quality approach: detailed advocacy with specific evidence
    • Medium-quality approach: brief advocacy with acknowledged limitations
    • Low-quality approach: mentioned for completeness, not actively advocated
  3. Cross-inspection: after advocating for A, actively look for evidence supporting B instead. After advocating for B, look for evidence supporting A. Counteracts confirmation bias

Purpose of reasoning-out-loud: make decision auditable — to yourself and user. Reasoning cannot be articulated? Assessment shallower than score suggests.

Got: Explicit reasoning for each approach that would be persuasive to neutral observer. Cross-inspection reveals at least one consideration initially overlooked.

If fail: Advocacy feels perfunctory (going through motions)? Approaches may not be genuinely different — may be variations of same idea. Check: do approaches differ in mechanism, or only implementation detail? Latter? Decision may not matter much — pick either, move on.

Step 3: Set Quorum Threshold and Commit

Set confidence threshold required to commit, calibrated to decision's stakes.

Confidence Thresholds by Stakes:
┌─────────────────────┬───────────┬──────────────────────────────────┐
│ Decision Type       │ Threshold │ Rationale                        │
├─────────────────────┼───────────┼──────────────────────────────────┤
│ Easily reversible   │ 60%       │ Cost of trying and reverting is  │
│ (can undo)          │           │ low. Speed matters more than     │
│                     │           │ certainty                        │
├─────────────────────┼───────────┼──────────────────────────────────┤
│ Moderate stakes     │ 75%       │ Reverting has cost but is        │
│ (costly to reverse) │           │ possible. Worth investing in     │
│                     │           │ evaluation                       │
├─────────────────────┼───────────┼──────────────────────────────────┤
│ Irreversible or     │ 90%       │ Cannot undo. Must be confident.  │
│ high-stakes         │           │ If threshold not met, gather     │
│                     │           │ more information before deciding │
└─────────────────────┴───────────┴──────────────────────────────────┘
  1. Classify decision stakes
  2. Check: does leading approach's quality score × confidence reach threshold?
  3. If yes: commit. State decision, reasoning, key risk accepted
  4. If no: identify what additional information would raise confidence to threshold
  5. Once committed, do not revisit unless new disqualifying evidence emerges

Got: Clear commitment moment with stated reasoning. Decision made at appropriate confidence level for its stakes.

If fail: Threshold never met (can't reach 90% on irreversible decision)? Ask: decision truly irreversible? Can it be decomposed into reversible test phase + irreversible commit? Most apparently irreversible decisions can be staged. Staging impossible? Communicate uncertainty to user, ask guidance.

Step 4: Resolve Deadlocks

Two or more approaches with similar scores, quorum threshold not met for any single one.

Deadlock Resolution:
┌────────────────────────┬──────────────────────────────────────────┐
│ Deadlock Type          │ Resolution                               │
├────────────────────────┼──────────────────────────────────────────┤
│ Genuine tie            │ The approaches are equivalent. Pick one  │
│ (scores within 5%)     │ and commit. The cost of deliberating     │
│                        │ exceeds the cost of picking the "wrong"  │
│                        │ equivalent option. Flip a coin mentally  │
├────────────────────────┼──────────────────────────────────────────┤
│ Information deficit    │ The tie exists because evaluation is     │
│ (scores uncertain)     │ incomplete. Invest one more specific     │
│                        │ investigation — a targeted file read, a  │
│                        │ quick test — then re-score               │
├────────────────────────┼──────────────────────────────────────────┤
│ Oscillation            │ Scoring keeps flip-flopping depending on │
│ (scores keep changing) │ which dimension gets attention. Time-box:│
│                        │ set a timer, evaluate once more, commit  │
│                        │ to the result regardless                 │
├────────────────────────┼──────────────────────────────────────────┤
│ Approach merge         │ The best parts of A and B can be         │
│ (compatible strengths) │ combined. Check for compatibility. If    │
│                        │ merge is coherent, use it. If forced,    │
│                        │ don't — pick one                         │
└────────────────────────┴──────────────────────────────────────────┘

Got: Deadlock resolved via appropriate mechanism. Resolution decisive — no lingering doubt undermining execution.

If fail: Deadlock persists through all resolution strategies? Decision may be premature. Ask user: "Two equally strong approaches: [A] and [B]. [Brief case for each.] Which aligns better with your priorities?" Delegating genuine tie to user not failure — acknowledges decision depends on values AI cannot infer.

Step 5: Assess Coherence Quality

After committing, evaluate whether process produced genuine coherence or just a decision.

  1. Decision evidence-based, or rubber-stamping initial preference?
    • Test: preference same before and after evaluation? If so, did evaluation change anything?
  2. Losing approaches genuinely considered, or were they straw men?
    • Test: can you articulate strongest case for losing approach?
  3. What signal would trigger reassessment?
    • Define specific observation that would invalidate decision ("If API doesn't support X, approach B becomes better")
  4. Useful information from losing approaches that should inform implementation?
    • Risk identified in approach B might apply to approach A too

Got: Brief quality check either confirming decision or identifying it as weak. Weak? Return to appropriate earlier step rather than proceeding on shaky ground.

If fail: Quality check reveals decision was preference-based rather than evidence-based? Acknowledge honestly. Sometimes preference is all available — but label it as such, not dress up as analysis.

Checks

  • Each approach evaluated independently before comparison
  • Advocacy proportional to quality (not equal attention regardless of merit)
  • Cross-inspection performed (looking for counter-evidence after advocacy)
  • Quorum threshold calibrated to decision stakes
  • If deadlocked, specific resolution strategy applied
  • Post-decision quality check performed
  • Reassessment trigger defined

Pitfalls

  • Premature commitment: Deciding before evaluating all approaches. First approach considered has anchoring advantage — gets more mental attention simply by being first. Evaluate all before comparing
  • Equal advocacy for unequal approaches: Approach A scored 85, approach B scored 45? Spending equal time advocating both wastes effort, creates false equivalence
  • Rubber-stamping: Going through evaluation process to justify decision already made. Test: could evaluation have changed outcome? If not, process was theater
  • Threshold avoidance: Lowering confidence threshold to make decision easier rather than gathering information needed to meet appropriate threshold
  • Ignoring losing side: Losing approach often contains warnings applying to winning one. Risks identified in approach B don't disappear just because approach A was chosen

See Also

  • build-consensus — multi-agent consensus model this skill adapts to single-agent reasoning
  • forage-solutions — scouts solution space coherence evaluates; typically precedes this skill
  • coordinate-reasoning — manages information flow during multi-path evaluation
  • center — establishes balanced baseline needed for unbiased evaluation
  • meditate — clears assumptions between evaluating different approaches

GitHub 저장소

pjt222/agent-almanac
경로: i18n/caveman/skills/build-coherence
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

연관 스킬

content-collections

메타

이 스킬은 콘텐츠 콜렉션(Content Collections)을 위한 프로덕션 검증된 설정을 제공합니다. 콘텐츠 콜렉션은 Markdown/MDX 파일을 Zod 검증이 포함된 타입 안전한 데이터 콜렉션으로 변환해주는 TypeScript 최우선 도구입니다. 블로그, 문서 사이트 또는 콘텐츠 중심의 Vite + React 애플리케이션을 구축할 때 타입 안전성과 자동 콘텐츠 검증을 보장하기 위해 사용하세요. Vite 플러그인 구성과 MDX 컴파일부터 배포 최적화 및 스키마 검증에 이르기까지 모든 것을 다룹니다.

스킬 보기

polymarket

메타

이 스킬은 개발자들이 Polymarket 예측 시장 플랫폼을 활용한 애플리케이션을 구축할 수 있도록 지원하며, 거래 및 시장 데이터를 위한 API 통합 기능을 포함합니다. 또한 WebSocket을 통한 실시간 데이터 스트리밍을 제공하여 실시간 거래와 시장 활동을 모니터링할 수 있습니다. 이를 통해 거래 전략을 구현하거나 실시간 시장 업데이트를 처리하는 도구를 생성하는 데 활용할 수 있습니다.

스킬 보기

creating-opencode-plugins

메타

이 스킬은 개발자들이 명령어, 파일, LSP 작업 등 25개 이상의 이벤트 유형에 연결되는 OpenCode 플러그인을 만들 수 있도록 돕습니다. JavaScript/TypeScript 모듈을 위한 플러그인 구조, 이벤트 API 명세, 구현 패턴을 제공합니다. OpenCode AI 어시스턴트의 라이프사이클을 사용자 정의 이벤트 기반 로직으로 가로채거나, 모니터링하거나, 확장해야 할 때 사용하세요.

스킬 보기

sglang

메타

SGLang은 RadixAttention 프리픽스 캐싱을 활용하여 JSON, 정규식, 에이전트 워크플로우를 위한 고속 구조화 생성에 특화된 고성능 LLM 서빙 프레임워크입니다. 특히 반복되는 프리픽스가 있는 작업에서 상당히 빠른 추론 속도를 제공하여 복잡한 구조화 출력 및 다중 턴 대화에 이상적입니다. 제약 디코딩이 필요하거나 광범위한 프리픽스 공유가 있는 애플리케이션을 구축할 때는 vLLM과 같은 대안보다 SGLang을 선택하십시오.

스킬 보기