rapid-convergence

name	rapid-convergence
description	Achieve 3-4 iteration methodology convergence (vs standard 5-7) when clear baseline metrics exist, domain scope is focused, and direct validation is possible. Use when you have V_meta baseline ≥0.40, quantifiable success criteria, retrospective validation data, and generic agents are sufficient. Enables 40-60% time reduction (10-15 hours vs 20-30 hours) without sacrificing quality. Prediction model helps estimate iteration count during experiment planning. Validated in error recovery (3 iterations, 10 hours, V_instance=0.83, V_meta=0.85).
allowed-tools	Read, Grep, Glob
contracts	[{"grep":"ConvergenceResult"},{"grep":"V_meta"},{"grep":"iteration"},{"grep":"structural"}]

Spec

λ(experiment: ExperimentContext) → ConvergenceResult

Rapid Convergence

Achieve methodology convergence in 3-4 iterations through structural optimization, not rushing.

Rapid convergence is not about moving fast - it's about recognizing when structural factors naturally enable faster progress without sacrificing quality.

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When to Use This Skill

Use this skill when:

• 🎯 Planning new experiment: Want to estimate iteration count and timeline
• 📊 Clear baseline exists: Can quantify current state with V_meta(s₀) ≥ 0.40
• 🔍 Focused domain: Can describe scope in <3 sentences without ambiguity
• ✅ Direct validation: Can validate with historical data or single context
• ⚡ Time constraints: Need methodology in 10-15 hours vs 20-30 hours
• 🧩 Generic agents sufficient: No complex specialization needed

Don't use when:

• ❌ Exploratory research (no established metrics)
• ❌ Multi-context validation required (cross-language, cross-domain testing)
• ❌ Complex specialization needed (>10x speedup from specialists)
• ❌ Incremental pattern discovery (patterns emerge gradually, not upfront)

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Quick Start (5 minutes)

Rapid Convergence Self-Assessment

Answer these 5 questions:

1. Baseline metrics exist: Can you quantify current state objectively? (YES/NO)
2. Domain is focused: Can you describe scope in <3 sentences? (YES/NO)
3. Validation is direct: Can you validate without multi-context deployment? (YES/NO)
4. Prior art exists: Are there established practices to reference? (YES/NO)
5. Success criteria clear: Do you know what "done" looks like? (YES/NO)

Scoring:

• 4-5 YES: ⚡ Rapid convergence (3-4 iterations) likely
• 2-3 YES: 📊 Standard convergence (5-7 iterations) expected
• 0-1 YES: 🔬 Exploratory (6-10 iterations), establish baseline first

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Five Rapid Convergence Criteria

Criterion 1: Clear Baseline Metrics (CRITICAL)

Indicator: V_meta(s₀) ≥ 0.40

What it means:

• Domain has established metrics (error rate, test coverage, build time)
• Baseline can be measured objectively in iteration 0
• Success criteria can be quantified before starting

Example (Bootstrap-003):

✅ Clear baseline:
- 1,336 errors quantified via MCP queries
- 5.78% error rate calculated
- Clear MTTD/MTTR targets
- Result: V_meta(s₀) = 0.48

Outcome: 3 iterations, 10 hours

Counter-example (Bootstrap-002):

❌ No baseline:
- No existing test coverage data
- Had to establish metrics first
- Fuzzy success criteria initially
- Result: V_meta(s₀) = 0.04

Outcome: 6 iterations, 25.5 hours

Impact: High V_meta baseline means:

• Fewer iterations to reach 0.80 threshold (+0.40 vs +0.76)
• Clearer iteration objectives (gaps are obvious)
• Faster validation (metrics already exist)

See reference/baseline-metrics.md for achieving V_meta ≥ 0.40.

Criterion 2: Focused Domain Scope (IMPORTANT)

Indicator: Domain described in <3 sentences without ambiguity

What it means:

• Single cross-cutting concern
• Clear boundaries (what's in vs out of scope)
• Well-established practices (prior art)

Examples:

✅ Focused (Bootstrap-003):
"Reduce error rate through detection, diagnosis, recovery, prevention"

❌ Broad (Bootstrap-002):
"Develop test strategy" (requires scoping: what tests? which patterns? how much coverage?)

Impact: Focused scope means:

• Less exploration needed
• Clearer convergence criteria
• Lower risk of scope creep

Criterion 3: Direct Validation (IMPORTANT)

Indicator: Can validate without multi-context deployment

What it means:

• Retrospective validation possible (use historical data)
• Single-context validation sufficient
• Proxy metrics strongly correlate with value

Examples:

✅ Direct (Bootstrap-003):
Retrospective validation via 1,336 historical errors
No deployment needed
Confidence: 0.79

❌ Indirect (Bootstrap-002):
Multi-context validation required (3 project archetypes)
Deploy and test in each context
Adds 2-3 iterations

Impact: Direct validation means:

• Faster iteration cycles
• Less complexity
• Easier V_meta calculation

See ../retrospective-validation for retrospective validation technique.

Criterion 4: Generic Agent Sufficiency (MODERATE)

Indicator: Generic agents (data-analyst, doc-writer, coder) sufficient

What it means:

• No specialized domain knowledge required
• Tasks are analysis + documentation + simple automation
• Pattern extraction is straightforward

Examples:

✅ Generic sufficient (Bootstrap-003):
Generic agents analyzed errors, documented taxonomy, created scripts
No specialization overhead
3 iterations

⚠️ Specialization needed (Bootstrap-002):
coverage-analyzer (10x speedup)
test-generator (200x speedup)
6 iterations (specialization added 1-2 iterations)

Impact: No specialization means:

• No iteration delay for agent design
• Simpler coordination
• Faster execution

Criterion 5: Early High-Impact Automation (MODERATE)

Indicator: Top 3 automation opportunities identified by iteration 1

What it means:

• Pareto principle applies (20% patterns → 80% impact)
• High-frequency, high-impact patterns obvious
• Automation feasibility clear (no R&D risk)

Examples:

✅ Early identification (Bootstrap-003):
3 tools preventing 23.7% of errors identified in iteration 0-1
Clear automation path
Rapid V_instance improvement

⚠️ Gradual discovery (Bootstrap-002):
8 test patterns emerged gradually over 6 iterations
Pattern library built incrementally

Impact: Early automation means:

• Faster V_instance improvement
• Clearer path to convergence
• Less trial-and-error

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Convergence Speed Prediction Model

Formula

Predicted Iterations = Base(4) + Σ penalties

Penalties:
- V_meta(s₀) < 0.40: +2 iterations
- Domain scope fuzzy: +1 iteration
- Multi-context validation: +2 iterations
- Specialization needed: +1 iteration
- Automation unclear: +1 iteration

Worked Examples

Bootstrap-003 (Error Recovery):

Base: 4
V_meta(s₀) = 0.48 ≥ 0.40: +0 ✓
Domain scope clear: +0 ✓
Retrospective validation: +0 ✓
Generic agents sufficient: +0 ✓
Automation identified early: +0 ✓
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Predicted: 4 iterations
Actual: 3 iterations ✅

Bootstrap-002 (Test Strategy):

Base: 4
V_meta(s₀) = 0.04 < 0.40: +2 ✗
Domain scope broad: +1 ✗
Multi-context validation: +2 ✗
Specialization needed: +1 ✗
Automation unclear: +0 ✓
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Predicted: 10 iterations
Actual: 6 iterations ✅ (model conservative)

Interpretation: Model predicts upper bound. Actual often faster due to efficient execution.

See examples/prediction-examples.md for more cases.

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Rapid Convergence Strategy

If criteria indicate 3-4 iteration potential, optimize:

Pre-Iteration 0: Planning (1-2 hours)

1. Establish Baseline Metrics

• Identify existing data sources
• Define quantifiable success criteria
• Ensure automatic measurement

Example: meta-cc query-tools --status error → 1,336 errors immediately

2. Scope Domain Tightly

• Write 1-sentence definition
• List explicit in/out boundaries
• Identify prior art

Example: "Error detection, diagnosis, recovery, prevention for meta-cc"

3. Plan Validation Approach

• Prefer retrospective (historical data)
• Minimize multi-context overhead
• Identify proxy metrics

Example: Retrospective validation with 1,336 historical errors

Iteration 0: Comprehensive Baseline (3-5 hours)

Target: V_meta(s₀) ≥ 0.40

Tasks:

1. Quantify current state thoroughly
2. Create initial taxonomy (≥70% coverage)
3. Document existing practices
4. Identify top 3 automations

Example (Bootstrap-003):

• Analyzed all 1,336 errors
• Created 10-category taxonomy (79.1% coverage)
• Documented 5 workflows, 5 patterns, 8 guidelines
• Identified 3 tools preventing 23.7% errors
• Result: V_meta(s₀) = 0.48 ✅

Time: Spend 3-5 hours here (saves 6-10 hours overall)

Iteration 1: High-Impact Automation (3-4 hours)

Tasks:

1. Implement top 3 tools
2. Expand taxonomy (≥90% coverage)
3. Validate with data (if possible)
4. Target: ΔV_instance = +0.20-0.30

Example (Bootstrap-003):

• Built 3 tools (515 LOC, ~150-180 lines each)
• Expanded taxonomy: 10 → 12 categories (92.3%)
• Result: V_instance = 0.55 (+0.27) ✅

Iteration 2: Validate and Converge (3-4 hours)

Tasks:

1. Test automation (real/historical data)
2. Complete taxonomy (≥95% coverage)
3. Check convergence:
   • V_instance ≥ 0.80?
   • V_meta ≥ 0.80?
   • System stable?

Example (Bootstrap-003):

• Validated 23.7% error prevention
• Taxonomy: 95.4% coverage
• Result: V_instance = 0.83, V_meta = 0.85 ✅ CONVERGED

Total time: 10-13 hours (3 iterations)

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Anti-Patterns

1. Premature Convergence

Symptom: Declare convergence at iteration 2 with V ≈ 0.75

Problem: Rushed without meeting 0.80 threshold

Solution: Rapid convergence = 3-4 iterations (not 2). Respect quality threshold.

2. Scope Creep

Symptom: Adding categories/patterns in iterations 3-4

Problem: Poorly scoped domain

Solution: Tight scoping in README. If scope grows, re-plan or accept slower convergence.

3. Over-Engineering Automation

Symptom: Spending 8+ hours on complex tools

Problem: Complexity delays convergence

Solution: Keep tools simple (1-2 hours, 150-200 lines). Complex tools are iteration 3-4 work.

4. Unnecessary Multi-Context Validation

Symptom: Testing 3+ contexts despite obvious generalizability

Problem: Validation overhead delays convergence

Solution: Use judgment. Error recovery is universal. Test strategy may need multi-context.

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Comparison Table

Aspect	Standard	Rapid
Iterations	5-7	3-4
Duration	20-30h	10-15h
V_meta(s₀)	0.00-0.30	0.40-0.60
Domain	Broad/exploratory	Focused
Validation	Multi-context often	Direct/retrospective
Specialization	Likely (1-3 agents)	Often unnecessary
Discovery	Incremental	Most patterns early
Risk	Scope creep	Premature convergence

Key: Rapid convergence is about recognizing structural factors, not rushing.

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Success Criteria

Rapid convergence pattern successfully applied when:

1. Accurate prediction: Actual iterations within ±1 of predicted
2. Quality maintained: V_instance ≥ 0.80, V_meta ≥ 0.80
3. Time efficiency: Duration ≤50% of standard convergence
4. Artifact completeness: Deliverables production-ready
5. Reusability validated: ≥80% transferability achieved

Bootstrap-003 Validation:

• ✅ Predicted: 3-4, Actual: 3
• ✅ Quality: V_instance=0.83, V_meta=0.85
• ✅ Efficiency: 10h (39% of Bootstrap-002's 25.5h)
• ✅ Artifacts: 13 categories, 8 workflows, 3 tools
• ✅ Reusability: 85-90%

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Related Skills

Parent framework:

• methodology-bootstrapping - Core OCA cycle

Complementary acceleration:

• retrospective-validation - Fast validation
• baseline-quality-assessment - Strong iteration 0

Supporting:

• agent-prompt-evolution - Agent stability

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References

Core guide:

• Rapid Convergence Criteria - Detailed criteria explanation
• Prediction Model - Formula and examples
• Strategy Guide - Iteration-by-iteration tactics

Examples:

• Bootstrap-003 Case Study - Rapid convergence
• Bootstrap-002 Comparison - Standard convergence

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Status: ✅ Validated | Bootstrap-003 | 40-60% time reduction | No quality sacrifice