build-coherence（建立连贯性）

Build Coherence

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

When to Use

• forage-solutions has identified multiple valid approaches and a selection must be made
• Oscillating between two approaches without committing to either
• Needing to justify a decision with structured reasoning (architecture choice, tool selection, implementation strategy)
• When a previous decision was made by gut feeling and needs evidence-based validation
• When internal reasoning is producing contradictory conclusions and coherence must be restored
• Before an irreversible action (merging, deploying, deleting) where the cost of the wrong choice is 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 the decision
• Optional: Known failure mode (oscillation, premature commitment, groupthink)

Procedure

Step 1: Independent Evaluation

Assess each approach on its own merits before comparing them. The critical rule: do not let the assessment of approach A bias the 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 a comparison until all individual evaluations are complete.

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

On failure: If the evaluations are contaminated (you find yourself writing "better than A" while assessing B), reset. Assess A completely, then clear the framing and assess B from scratch. If the scores are all identical, the evaluation dimensions are too coarse — add domain-specific criteria.

Step 2: Waggle Dance — Reason Out Loud

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

1. For each approach, state the case for it — as if presenting to a skeptical user:

- "Approach A is strong because [evidence]. The main risk is [risk], which is mitigated by [mitigation]."

1. Advocacy intensity should be 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

1. Cross-inspection: after advocating for A, actively look for evidence that supports B instead. After advocating for B, look for evidence that supports A. This counteracts confirmation bias

The purpose of reasoning-out-loud is to make the decision auditable — to yourself and to the user. If the reasoning cannot be articulated, the assessment is shallower than the score suggests.

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

On failure: If advocacy feels perfunctory (going through motions), the approaches may not be genuinely different — they may be variations of the same idea. Check: do the approaches differ in mechanism, or only in implementation detail? If the latter, the decision may not matter much — pick either and move on.

Step 3: Set Quorum Threshold and Commit

Set the confidence threshold required to commit, calibrated to the 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 the decision stakes
2. Check: does the leading approach's quality score × confidence reach the threshold?
3. If yes: commit. State the decision, the reasoning, and the key risk being accepted
4. If no: identify what additional information would raise confidence to the threshold
5. Once committed, do not revisit unless new disqualifying evidence emerges

Expected: A clear commitment moment with stated reasoning. The decision is made at an appropriate confidence level for its stakes.

On failure: If the threshold is never met (can't reach 90% on an irreversible decision), ask: is the decision truly irreversible? Can it be decomposed into a reversible test phase + an irreversible commit? Most apparently irreversible decisions can be staged. If staging is impossible, communicate the uncertainty to the user and ask for guidance.

Step 4: Resolve Deadlocks

When two or more approaches have similar scores and the quorum threshold is 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                         │
└────────────────────────┴──────────────────────────────────────────┘

Expected: Deadlock resolved through the appropriate mechanism. The resolution is decisive — no lingering doubt that undermines execution.

On failure: If the deadlock persists through all resolution strategies, the decision may be premature. Ask the user: "I see two equally strong approaches: [A] and [B]. [Brief case for each.] Which aligns better with your priorities?" Delegating a genuine tie to the user is not a failure — it is acknowledging that the decision depends on values the AI cannot infer.

Step 5: Assess Coherence Quality

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

1. Was the decision evidence-based, or was it rubber-stamping an initial preference?

- Test: was the preference the same before and after evaluation? If so, did the evaluation change anything?

1. Were the losing approaches genuinely considered, or were they straw men?

- Test: can you articulate the strongest case for the losing approach?

1. What signal would trigger reassessment?

- Define a specific observation that would invalidate the decision ("If I discover that the API doesn't support X, then approach B becomes better")

1. Is there useful information from the losing approaches that should inform implementation?

- A risk identified in approach B might apply to approach A as well

Expected: A brief quality check that either confirms the decision or identifies it as weak. If weak, return to the appropriate earlier step rather than proceeding on shaky ground.

On failure: If the quality check reveals that the decision was preference-based rather than evidence-based, acknowledge it honestly. Sometimes preference is all that is available — but it should be labeled as such, not dressed up as analysis.

Validation

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

Common Pitfalls

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

Related Skills

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