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build-coherence

pjt222
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빌드-코히런스 스킬은 벌 군집 행동에서 영감을 받은 구조화된 다중 경로 추론 프로세스를 적용하여, 여러 유효한 접근법이 존재할 때 개발자가 최적의 결정을 내릴 수 있도록 돕습니다. 이 스킬은 경쟁 옵션을 독립적으로 평가하고, 추론 과정을 소리 내어 설명하며, 선택을 위해 신뢰도 임계값을 사용하므로, 아키텍처 선택이나 되돌릴 수 없는 조치 전에 이상적입니다. 이를 사용하여 결정 교착 상태를 해결하고 명확하고 구조화된 추론으로 선택을 정당화하세요.

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 → independent assess, explicit reasoning-out-loud advocacy, confidence-calibrated commit thresholds, structured deadlock res → coherent decisions from multi-path reasoning.

Use When

  • forage-solutions ID'd many valid approaches, must select
  • Oscillating between 2 approaches, no commit
  • Need to justify decision w/ structured reasoning (arch, tool, impl strategy)
  • Prev decision by gut, needs evidence validation
  • Internal reasoning → contradictory conclusions, restore coherence
  • Before irreversible action (merge, deploy, delete) where wrong = high cost

In

  • Required: ≥2 competing approaches
  • Optional: Quality assessments from prior scouting (see forage-solutions)
  • Optional: Decision stakes (reversible, moderate, irreversible) for threshold calibration
  • Optional: Time budget
  • Optional: Known failure mode (oscillation, premature commit, groupthink)

Do

Step 1: Independent Evaluate

Assess each on own merits before comparing. Critical: A's assessment doesn't bias 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 out each separately. No comparison until all individual evals complete.

Independent evals, each on own terms. B's eval doesn't ref A. Scores = real assessment, not ranking.

If err: Evals contaminated (writing "better than A" while assessing B) → reset. Assess A fully, clear frame, assess B fresh. All scores identical → dimensions too coarse, add domain-specific criteria.

Step 2: Waggle Dance — Reason Out Loud

Advocate proportional to quality. AI eq of bee waggle: implicit reasoning → explicit + public.

  1. Each approach, state case — as if presenting to skeptical user:
    • "Approach A strong because [evidence]. Main risk [risk], mitigated by [mitigation]."
  2. Advocacy intensity proportional to quality score:
    • High: detailed advocacy + specific evidence
    • Medium: brief advocacy + acknowledged limits
    • Low: mentioned for completeness, not actively advocated
  3. Cross-inspection: After advocating A, actively seek evidence supporting B. After B, seek A. Counters confirmation bias

Point of reasoning-out-loud = decision auditable. Can't articulate → assessment shallower than score suggests.

Explicit reasoning per approach, persuasive to neutral observer. Cross-inspection reveals ≥1 initially overlooked consideration.

If err: Advocacy perfunctory (motions) → approaches maybe not genuinely diff, just variations. Differ in mechanism or only impl detail? Latter → decision doesn't matter much, pick either, move on.

Step 3: Quorum Threshold + Commit

Confidence threshold to commit, calibrated to 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 stakes
  2. Check: leading approach quality × confidence ≥ threshold?
  3. Yes → commit. State decision, reasoning, key risk accepted
  4. No → ID additional info that raises confidence to threshold
  5. Committed → don't revisit unless new disqualifying evidence

Clear commit moment + stated reasoning. Decision at right confidence for stakes.

If err: Threshold never met (can't hit 90% on irreversible) → ask: truly irreversible? Decomposable into reversible test + irreversible commit? Most apparently irreversible can be staged. Impossible → tell user uncertainty, ask guidance.

Step 4: Deadlock Resolution

≥2 approaches similar scores + quorum not met for any.

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                         │
└────────────────────────┴──────────────────────────────────────────┘

Deadlock resolved via mechanism. Decisive — no lingering doubt that undermines execution.

If err: Deadlock persists through all strategies → decision premature. Ask user: "2 equally strong approaches: [A], [B]. [Brief case each.] Which aligns w/ priorities?" Delegating genuine tie = not fail, ack decision depends on values AI can't infer.

Step 5: Coherence Quality

Post-commit: real coherence or just a decision?

  1. Evidence-based or rubber-stamped initial pref?
    • Test: Pref same before + after eval? Eval changed anything?
  2. Losing approaches genuinely considered or straw men?
    • Test: Can articulate strongest case for losing approach?
  3. What signal triggers reassess?
    • Specific obs that would invalidate ("If API doesn't support X, approach B better")
  4. Useful info from losing approaches for impl?
    • Risk in B may apply to A too

Brief quality check that confirms decision OR IDs it as weak. Weak → return to earlier step, not proceed on shaky ground.

If err: Quality check reveals pref-based not evidence-based → ack honestly. Sometimes pref all that's available — label as such, not dressed up as analysis.

Check

  • Each approach evaluated independently before comparison
  • Advocacy proportional to quality (not equal regardless of merit)
  • Cross-inspection done (counter-evidence after advocacy)
  • Quorum threshold calibrated to stakes
  • Deadlocked → specific resolution strategy applied
  • Post-decision quality check done
  • Reassess trigger defined

Traps

  • Premature commit: Decide before evaluating all. First approach has anchoring advantage (more mental attention from being first). Evaluate all before comparing
  • Equal advocacy, unequal approaches: A=85, B=45 → equal time = wasted effort + false equivalence
  • Rubber-stamp: Going through process to justify already-made decision. Test: could eval have changed outcome? If not = theater
  • Threshold avoidance: Lower threshold to ease decision vs gather info needed to meet appropriate threshold
  • Ignore losing side: Losing approach often contains warnings applying to winner. Risks in B don't vanish just because A chosen

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

GitHub 저장소

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

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