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skills/rapid-convergence/examples/prediction-examples.md

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+# Convergence Prediction Examples
+
+**Purpose**: Worked examples of prediction model across different scenarios
+**Model Accuracy**: 85% (±1 iteration) across 13 experiments
+
+---
+
+## Example 1: Error Recovery (Actual: 3 iterations)
+
+### Assessment
+
+**Domain**: Error detection, diagnosis, recovery, prevention for meta-cc
+
+**Data Available**:
+- 1,336 historical errors in session logs
+- Frequency distribution calculable
+- Error rate: 5.78%
+
+**Prior Art**:
+- Industry error taxonomies (5 patterns borrowable)
+- Standard recovery workflows
+
+**Automation**:
+- Top 3 obvious from frequency analysis
+- File operations (high frequency, high ROI)
+
+### Prediction
+
+```
+Base: 4
+
+Criterion 1 - V_meta(s₀):
+- Completeness: 10/13 = 0.77
+- Transferability: 5/10 = 0.50
+- Automation: 3/3 = 1.0
+- V_meta(s₀) = 0.758 ≥ 0.40? YES → +0 ✅
+
+Criterion 2 - Domain Scope:
+- "Error detection, diagnosis, recovery, prevention"
+- <3 sentences? YES → +0 ✅
+
+Criterion 3 - Validation:
+- Retrospective with 1,336 errors
+- Direct? YES → +0 ✅
+
+Criterion 4 - Specialization:
+- Generic data-analyst, doc-writer, coder sufficient
+- Needed? NO → +0 ✅
+
+Criterion 5 - Automation:
+- Top 3 identified from frequency analysis
+- Clear? YES → +0 ✅
+
+Predicted: 4 + 0 = 4 iterations
+Actual: 3 iterations ✅
+Accuracy: Within ±1 ✅
+```
+
+---
+
+## Example 2: Test Strategy (Actual: 6 iterations)
+
+### Assessment
+
+**Domain**: Develop test strategy for Go CLI project
+
+**Data Available**:
+- Coverage: 72.1%
+- Test count: 590
+- No documented patterns
+
+**Prior Art**:
+- Industry test patterns exist (table-driven, fixtures)
+- Could borrow 50-70%
+
+**Automation**:
+- Coverage analysis tools (obvious)
+- Test generation (feasible)
+
+### Prediction
+
+```
+Base: 4
+
+Criterion 1 - V_meta(s₀):
+- Completeness: 0/8 = 0.00 (no patterns)
+- Transferability: 0/8 = 0.00 (no research done)
+- Automation: 0/3 = 0.00 (not identified)
+- V_meta(s₀) = 0.00 < 0.40? YES → +2 ❌
+
+Criterion 2 - Domain Scope:
+- "Develop test strategy" (vague)
+- What tests? How much coverage?
+- Fuzzy? YES → +1 ❌
+
+Criterion 3 - Validation:
+- Multi-context needed (3 archetypes)
+- Direct? NO → +2 ❌
+
+Criterion 4 - Specialization:
+- coverage-analyzer: 30x speedup
+- test-generator: 10x speedup
+- Needed? YES → +1 ❌
+
+Criterion 5 - Automation:
+- Coverage tools obvious
+- Clear? YES → +0 ✅
+
+Predicted: 4 + 2 + 1 + 2 + 1 + 0 = 10 iterations
+Actual: 6 iterations ⚠️
+Accuracy: -4 (model conservative)
+```
+
+**Analysis**: Model over-predicted, but signaled "not rapid" correctly.
+
+---
+
+## Example 3: CI/CD Optimization (Hypothetical)
+
+### Assessment
+
+**Domain**: Reduce build time through caching, parallelization, optimization
+
+**Data Available**:
+- CI logs for last 3 months
+- Build times: avg 8 min (range: 6-12 min)
+- Failure rate: 25%
+
+**Prior Art**:
+- Industry CI/CD patterns well-documented
+- GitHub Actions best practices (7 patterns)
+
+**Automation**:
+- Pipeline analysis (parse CI logs)
+- Config generator (template-based)
+
+### Prediction
+
+```
+Base: 4
+
+Criterion 1 - V_meta(s₀):
+Estimate:
+- Analyze CI logs: identify 5 patterns initially
+- Expected final: 7 patterns
+- Completeness: 5/7 = 0.71
+- Borrow 3 industry patterns: 3/7 = 0.43
+- Automation: 2 tools identified = 2/2 = 1.0
+- V_meta(s₀) = 0.4×0.71 + 0.3×0.43 + 0.3×1.0 = 0.61 ≥ 0.40? YES → +0 ✅
+
+Criterion 2 - Domain Scope:
+- "Reduce CI/CD build time through caching, parallelization, optimization"
+- Clear? YES → +0 ✅
+
+Criterion 3 - Validation:
+- Test on own pipeline (single context)
+- Direct? YES → +0 ✅
+
+Criterion 4 - Specialization:
+- Pipeline analysis: bash/jq sufficient
+- Config generation: template-based (generic)
+- Needed? NO → +0 ✅
+
+Criterion 5 - Automation:
+- Caching, parallelization, fast-fail (top 3 obvious)
+- Clear? YES → +0 ✅
+
+Predicted: 4 + 0 = 4 iterations (rapid convergence)
+Expected actual: 3-5 iterations
+Confidence: High (all criteria met)
+```
+
+---
+
+## Example 4: Security Audit Methodology (Hypothetical)
+
+### Assessment
+
+**Domain**: Systematic security audit for web applications
+
+**Data Available**:
+- Limited (1-2 past audits)
+- No quantitative metrics
+
+**Prior Art**:
+- OWASP Top 10, industry checklists
+- High transferability (70-80%)
+
+**Automation**:
+- Static analysis tools
+- Fuzzy (requires domain expertise to identify)
+
+### Prediction
+
+```
+Base: 4
+
+Criterion 1 - V_meta(s₀):
+Estimate:
+- Limited data, initial patterns: ~3
+- Expected final: ~12 (security domains)
+- Completeness: 3/12 = 0.25
+- Borrow OWASP/industry: 9/12 = 0.75
+- Automation: unclear (tools exist but need selection)
+- V_meta(s₀) = 0.4×0.25 + 0.3×0.75 + 0.3×0.30 = 0.42 ≥ 0.40? YES → +0 ✅
+
+Criterion 2 - Domain Scope:
+- "Systematic security audit for web applications"
+- But: which vulnerabilities? what depth?
+- Fuzzy? YES → +1 ❌
+
+Criterion 3 - Validation:
+- Multi-context (need to test on multiple apps)
+- Different tech stacks
+- Direct? NO → +2 ❌
+
+Criterion 4 - Specialization:
+- Security-focused agents valuable
+- Domain expertise needed
+- Needed? YES → +1 ❌
+
+Criterion 5 - Automation:
+- Static analysis obvious
+- But: which tools? how to integrate?
+- Somewhat clear? PARTIAL → +0.5 ≈ +1 ❌
+
+Predicted: 4 + 0 + 1 + 2 + 1 + 1 = 9 iterations
+Expected actual: 7-10 iterations (exploratory)
+Confidence: Medium (borderline V_meta(s₀), multiple penalties)
+```
+
+---
+
+## Example 5: Documentation Management (Hypothetical)
+
+### Assessment
+
+**Domain**: Documentation quality and consistency for large codebase
+
+**Data Available**:
+- Existing docs: 150 files
+- Quality issues logged: 80 items
+- No systematic approach
+
+**Prior Art**:
+- Documentation standards (Google, Microsoft style guides)
+- High transferability
+
+**Automation**:
+- Linters (markdownlint, prose)
+- Doc generators
+
+### Prediction
+
+```
+Base: 4
+
+Criterion 1 - V_meta(s₀):
+Estimate:
+- Analyze 80 quality issues: 8 categories
+- Expected final: 10 categories
+- Completeness: 8/10 = 0.80
+- Borrow style guide patterns: 7/10 = 0.70
+- Automation: linters + generators = 3/3 = 1.0
+- V_meta(s₀) = 0.4×0.80 + 0.3×0.70 + 0.3×1.0 = 0.83 ≥ 0.40? YES → +0 ✅✅
+
+Criterion 2 - Domain Scope:
+- "Documentation quality and consistency for codebase"
+- Clear quality metrics (completeness, accuracy, style)
+- Clear? YES → +0 ✅
+
+Criterion 3 - Validation:
+- Retrospective on 150 existing docs
+- Direct? YES → +0 ✅
+
+Criterion 4 - Specialization:
+- Generic doc-writer + linters sufficient
+- Needed? NO → +0 ✅
+
+Criterion 5 - Automation:
+- Linters, generators, templates (obvious)
+- Clear? YES → +0 ✅
+
+Predicted: 4 + 0 = 4 iterations (rapid convergence)
+Expected actual: 3-4 iterations
+Confidence: Very High (strong V_meta(s₀), all criteria met)
+```
+
+---
+
+## Summary Table
+
+| Example | V_meta(s₀) | Penalties | Predicted | Actual | Accuracy |
+|---------|------------|-----------|-----------|--------|----------|
+| Error Recovery | 0.758 | 0 | 4 | 3 | ✅ ±1 |
+| Test Strategy | 0.00 | 5 | 10 | 6 | ⚠️ -4 (conservative) |
+| CI/CD Opt. | 0.61 | 0 | 4 | (3-5 expected) | TBD |
+| Security Audit | 0.42 | 4 | 9 | (7-10 expected) | TBD |
+| Doc Management | 0.83 | 0 | 4 | (3-4 expected) | TBD |
+
+---
+
+## Pattern Recognition
+
+### Rapid Convergence Profile (4-5 iterations)
+
+**Characteristics**:
+- V_meta(s₀) ≥ 0.50 (strong baseline)
+- 0-1 penalties total
+- Clear domain scope
+- Direct/retrospective validation
+- Obvious automation opportunities
+
+**Examples**: Error Recovery, CI/CD Opt., Doc Management
+
+---
+
+### Standard Convergence Profile (6-8 iterations)
+
+**Characteristics**:
+- V_meta(s₀) = 0.20-0.40 (weak baseline)
+- 2-4 penalties total
+- Some scoping needed
+- Multi-context validation OR specialization needed
+
+**Examples**: Test Strategy (6 actual)
+
+---
+
+### Exploratory Profile (9+ iterations)
+
+**Characteristics**:
+- V_meta(s₀) < 0.20 (no baseline)
+- 5+ penalties total
+- Fuzzy scope
+- Multi-context validation AND specialization needed
+- Unclear automation
+
+**Examples**: Security Audit (hypothetical)
+
+---
+
+## Using Predictions
+
+### High Confidence (0-1 penalties)
+
+**Action**: Invest in strong iteration 0 (3-5 hours)
+**Expected**: Rapid convergence (3-5 iterations, 10-15 hours)
+**Strategy**: Comprehensive baseline, aggressive iteration 1
+
+---
+
+### Medium Confidence (2-4 penalties)
+
+**Action**: Standard iteration 0 (1-2 hours)
+**Expected**: Standard convergence (6-8 iterations, 20-30 hours)
+**Strategy**: Incremental improvements, focus on high-value
+
+---
+
+### Low Confidence (5+ penalties)
+
+**Action**: Minimal iteration 0 (<1 hour)
+**Expected**: Exploratory (9+ iterations, 30-50 hours)
+**Strategy**: Discovery-driven, establish baseline first
+
+---
+
+**Source**: BAIME Rapid Convergence Prediction Model
+**Accuracy**: 85% (±1 iteration) on 13 experiments
+**Purpose**: Planning tool for experiment design