gh-yaleh-meta-cc-claude/skills/rapid-convergence/reference/baseline-metrics.md

Achieving Strong Baseline Metrics

Purpose: How to achieve V_meta(s₀) ≥ 0.40 for rapid convergence Impact: Strong baseline reduces iterations by 2-3 (40-60% time savings)

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V_meta Baseline Formula

V_meta(s₀) = 0.4 × completeness +
             0.3 × transferability +
             0.3 × automation_effectiveness

Where (at iteration 0):
- completeness = initial_coverage / target_coverage
- transferability = existing_patterns_reusable / total_patterns_needed
- automation_effectiveness = identified_automation_ops / automation_opportunities

Target: V_meta(s₀) ≥ 0.40

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Component 1: Completeness (40% weight)

Definition: Initial taxonomy/pattern coverage

Calculation:

completeness = initial_categories / estimated_final_categories

Achieve ≥0.50 by:

1. Comprehensive data analysis (3-5 hours)
2. Create initial taxonomy (10-15 categories)
3. Classify ≥70% of observed cases

Example (Bootstrap-003):

Iteration 0 taxonomy: 10 categories
Estimated final: 12-13 categories
Completeness: 10/12.5 = 0.80

Contribution: 0.4 × 0.80 = 0.32 ✅

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Component 2: Transferability (30% weight)

Definition: Reusability of existing patterns/knowledge

Calculation:

transferability = (borrowed_patterns + existing_knowledge) / total_patterns_needed

Achieve ≥0.30 by:

1. Research prior art (1-2 hours)
2. Identify similar methodologies
3. Document reusable patterns

Example (Bootstrap-003):

Borrowed from industry: 5 error patterns
Existing knowledge: Error taxonomy basics
Total patterns needed: ~10

Transferability: 5/10 = 0.50

Contribution: 0.3 × 0.50 = 0.15 ✅

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Component 3: Automation Effectiveness (30% weight)

Definition: Early identification of automation opportunities

Calculation:

automation_effectiveness = identified_high_ROI_tools / expected_tool_count

Achieve ≥0.30 by:

1. Analyze high-frequency tasks (1-2 hours)
2. Identify top 3-5 automation candidates
3. Estimate ROI (>5x preferred)

Example (Bootstrap-003):

Identified in iteration 0: 3 tools
  - validate-path.sh: 65.2% prevention, 61x ROI
  - check-file-size.sh: 100% prevention, 31.6x ROI
  - check-read-before-write.sh: 100% prevention, 26.2x ROI

Expected final tool count: ~3

Automation effectiveness: 3/3 = 1.0

Contribution: 0.3 × 1.0 = 0.30 ✅

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Worked Example: Bootstrap-003

Iteration 0 Investment: 120 min

Data Analysis (60 min):

• Queried session history: 1,336 errors
• Calculated error rate: 5.78%
• Identified frequency distribution

Taxonomy Creation (40 min):

• Created 10 initial categories
• Classified 1,056/1,336 errors (79.1%)
• Estimated 2-3 more categories needed

Pattern Research (15 min):

• Reviewed industry error taxonomies
• Identified 5 reusable patterns
• Documented error handling best practices

Automation Identification (5 min):

• Top 3 opportunities obvious from data:
  1. File-not-found: 250 errors (18.7%)
  2. File-size-exceeded: 84 errors (6.3%)
  3. Write-before-read: 70 errors (5.2%)

V_meta(s₀) Calculation

Completeness: 10/12.5 = 0.80
Transferability: 5/10 = 0.50
Automation: 3/3 = 1.0

V_meta(s₀) = 0.4 × 0.80 +
             0.3 × 0.50 +
             0.3 × 1.0

           = 0.32 + 0.15 + 0.30
           = 0.77 ✅✅ (far exceeds 0.40 target)

Result: 3 iterations total (rapid convergence)

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Contrast: Bootstrap-002 (Weak Baseline)

Iteration 0 Investment: 60 min

Coverage Measurement (30 min):

• Ran coverage analysis: 72.1%
• Counted tests: 590
• No systematic approach documented

Pattern Identification (20 min):

• Wrote 3 ad-hoc tests
• Noted duplication issues
• No pattern library yet

No Prior Research (0 min):

• Started from scratch
• No borrowed patterns

No Automation Planning (10 min):

• Vague ideas about coverage tools
• No concrete automation identified

V_meta(s₀) Calculation

Completeness: 0/8 patterns = 0.00 (none documented)
Transferability: 0/8 = 0.00 (no research)
Automation: 0/3 tools = 0.00 (none identified)

V_meta(s₀) = 0.4 × 0.00 +
             0.3 × 0.00 +
             0.3 × 0.00

           = 0.00 ❌ (far below 0.40 target)

Result: 6 iterations total (standard convergence)

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Achieving V_meta(s₀) ≥ 0.40: Checklist

Completeness Target: ≥0.50

Tasks:

• Analyze ALL available data (3-5 hours)
• Create initial taxonomy/pattern library (10-15 items)
• Classify ≥70% of observed cases
• Estimate final taxonomy size
• Calculate: initial_count / estimated_final ≥ 0.50?

Time: 3-5 hours Contribution: 0.4 × 0.50 = 0.20

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Transferability Target: ≥0.30

Tasks:

• Research prior art (1-2 hours)
• Identify similar methodologies
• Document borrowed patterns (≥30% reusable)
• List existing knowledge applicable
• Calculate: borrowed / total_needed ≥ 0.30?

Time: 1-2 hours Contribution: 0.3 × 0.30 = 0.09

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Automation Target: ≥0.30

Tasks:

• Analyze task frequency (1 hour)
• Identify top 3-5 automation candidates
• Estimate ROI for each (>5x preferred)
• Document automation plan
• Calculate: identified / expected ≥ 0.30?

Time: 1-2 hours Contribution: 0.3 × 0.30 = 0.09

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Total Baseline Investment

Minimum: 5-9 hours for V_meta(s₀) = 0.38-0.40 Recommended: 6-10 hours for V_meta(s₀) = 0.45-0.55 Aggressive: 8-12 hours for V_meta(s₀) = 0.60-0.80

ROI: 5-9 hours investment → Save 10-15 hours overall (2-3x)

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Quick Assessment: Can You Achieve 0.40?

Question 1: Do you have quantitative data to analyze?

• YES: Proceed with completeness analysis
• NO: Gather data first (delays rapid convergence)

Question 2: Does prior art exist in this domain?

• YES: Research and document (1-2 hours)
• NO: Lower transferability expected (<0.20)

Question 3: Are high-frequency patterns obvious?

• YES: Identify automation opportunities (1 hour)
• NO: Requires deeper analysis (adds time)

Scoring:

• 3 YES: V_meta(s₀) ≥ 0.40 achievable (5-9 hours)
• 2 YES: V_meta(s₀) = 0.30-0.40 (7-12 hours)
• 0-1 YES: V_meta(s₀) < 0.30 (not rapid convergence candidate)

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Common Pitfalls

❌ Insufficient Data Analysis

Symptom: Analyzing <50% of available data Impact: Low completeness (<0.40) Fix: Comprehensive analysis (3-5 hours)

Example:

❌ Analyzed 200/1,336 errors → 5 categories → completeness = 0.38
✅ Analyzed 1,336/1,336 errors → 10 categories → completeness = 0.80

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❌ Skipping Prior Art Research

Symptom: Starting from scratch Impact: Zero transferability Fix: 1-2 hours research

Example:

❌ No research → 0 borrowed patterns → transferability = 0.00
✅ Research industry taxonomies → 5 patterns → transferability = 0.50

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❌ Vague Automation Ideas

Symptom: "Maybe we could automate X" Impact: Low automation score Fix: Concrete identification + ROI estimate

Example:

❌ "Could automate coverage" → automation = 0.10
✅ "Coverage gap analyzer, 30x speedup, 6x ROI" → automation = 0.33

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Measurement Tools

Completeness:

# Count initial categories
initial=$(grep "^##" taxonomy.md | wc -l)

# Estimate final (from analysis)
estimated=12

# Calculate
echo "scale=2; $initial / $estimated" | bc
# Target: ≥0.50

Transferability:

# Count borrowed patterns
borrowed=$(grep "Source:" patterns.md | grep -v "Original" | wc -l)

# Estimate total needed
total=10

# Calculate
echo "scale=2; $borrowed / $total" | bc
# Target: ≥0.30

Automation:

# Count identified tools
identified=$(ls scripts/ | wc -l)

# Estimate final count
expected=3

# Calculate
echo "scale=2; $identified / $expected" | bc
# Target: ≥0.30

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Source: BAIME Rapid Convergence Framework Target: V_meta(s₀) ≥ 0.40 for 3-4 iteration convergence Investment: 5-10 hours in iteration 0 ROI: 2-3x (saves 10-15 hours overall)