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build-coherence

pjt222
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Esta habilidad ayuda a los desarrolladores a tomar decisiones cuando existen múltiples enfoques válidos, utilizando un método estructurado de "democracia de abejas" para evaluar opciones de manera independiente y alcanzar un consenso confiable. Es ideal para seleccionar arquitecturas, justificar la elección de herramientas o antes de comprometerse con acciones de alto costo. Sus características clave incluyen el razonamiento en voz alta para transparencia y la detección de quórum para umbrales de decisión basados en confianza.

Instalación rápida

Claude Code

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Principal
npx skills add pjt222/agent-almanac -a claude-code
Comando PluginAlternativo
/plugin add https://github.com/pjt222/agent-almanac
Git CloneAlternativo
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/build-coherence

Copia y pega este comando en Claude Code para instalar esta habilidad

Documentación

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

Repositorio GitHub

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

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