| name | FPEF Evidence Analyzer |
| description | Systematic Find-Prove-Evidence-Fix framework for complex system analysis and intervention. Use when debugging failures, investigating incidents, analyzing performance issues, or optimizing complex systems. |
FPEF Evidence Analyzer
Overview
Rigorous evidence-based analysis framework that systematically finds problems, proves root causes, gathers supporting evidence, and implements targeted fixes for complex technical and business systems.
Prerequisites
- System or problem description
- Access to relevant data sources (logs, metrics, code)
- Basic understanding of the domain being analyzed
What This Skill Does
- Find: Systematic identification of anomalies and potential issues
- Prove: Causal analysis to establish root cause relationships
- Evidence: Comprehensive evidence collection and validation
- Fix: Targeted interventions with verification of effectiveness
Quick Start (60 seconds)
Rapid Analysis
1. Describe the problem: [e.g., "API response times increased 300%"]
2. Timeframe: [e.g., "Last 24 hours"]
3. Affected systems: [e.g., "Payment processing API"]
4. Available data: [e.g., "CloudWatch logs, database metrics"]
→ FPEF generates comprehensive analysis framework
Immediate Output
Analysis framework includes:
- ✅ Problem statement with clear scope definition
- ✅ Hypothesis tree with potential root causes
- ✅ Evidence collection plan with data sources
- ✅ Investigation timeline with critical path
- ✅ Fix validation strategy and success criteria
- ✅ Prevention measures for future incidents
Configuration
Analysis Parameters
Edit resources/fpef-config.json:
{
"problem_description": "Clear description of the issue",
"scope": "Systems, components, or processes affected",
"timeline": "When the problem started and duration",
"severity": "critical|high|medium|low",
"business_impact": "Revenue, users, operations affected",
"data_sources": ["logs", "metrics", "traces", "code"],
"constraints": ["time", "budget", "access", "expertise"]
}
Analysis Settings
{
"depth": "comprehensive|focused|rapid",
"certainty_threshold": "0.95",
"evidence_types": ["quantitative", "qualitative", "correlational", "causal"],
"fix_strategy": "immediate|phased|gradual",
"validation_method": "a_b_test|before_after|control_group"
}
Step-by-Step Guide
Phase 1: FIND - Systematic Problem Identification (5 minutes)
Step 1.1: Problem Statement Definition
FPEF structures clear problem statements:
- What: Specific deviation from expected behavior
- Where: Systems, components, or processes affected
- When: Timeline and frequency of occurrence
- Impact: Business and technical consequences
- Metrics: Quantifiable measures of the problem
Step 1.2: Scope Analysis
./scripts/fpef-find.sh --scope-analysis
Step 1.3: Hypothesis Generation
FPEF creates structured hypothesis trees:
- Primary Hypotheses: Most likely root causes
- Secondary Hypotheses: Alternative explanations
- Contributing Factors: Multi-causal relationships
- External Factors: Environmental influences
Phase 2: PROVE - Causal Analysis and Root Cause Identification (10 minutes)
Step 2.1: Evidence Planning
./scripts/fpef-prove.sh --evidence-plan
Step 2.2: Causal Chain Analysis
FPEF establishes causal relationships:
- Temporal Sequence: Verify cause precedes effect
- Statistical Significance: Correlation strength and validity
- Mechanistic Understanding: Physical or logical mechanisms
- Elimination of Alternatives: Rule out other explanations
Step 2.3: Proof Validation
./scripts/validate-proof.sh --threshold 0.95
Phase 3: EVIDENCE - Comprehensive Data Collection (15 minutes)
Step 3.1: Evidence Collection Matrix
FPEF organizes evidence collection by:
- Source Type: Logs, metrics, traces, interviews, documentation
- Reliability: High, medium, low confidence sources
- Accessibility: Immediate, delayed, or requiring special access
- Analysis Method: Quantitative, qualitative, mixed methods
Step 3.2: Automated Evidence Gathering
./scripts/fpef-evidence.sh --collect-all
Step 3.3: Evidence Synthesis
./scripts/synthesize-evidence.sh
Phase 4: FIX - Targeted Interventions and Validation (10 minutes)
Step 4.1: Solution Design
FPEF generates targeted fixes based on:
- Root Cause Addressing: Direct fixes for identified causes
- Symptom Mitigation: Immediate relief for symptoms
- Prevention Measures: Long-term solutions to prevent recurrence
- System Improvements: Broad system enhancements
Step 4.2: Implementation Planning
./scripts/fpef-fix.sh --implementation-plan
Step 4.3: Validation Strategy
./scripts/validate-fix.sh --setup-monitoring
Advanced Features
Feature 1: Automated Root Cause Analysis
./scripts/auto-rca.sh --data sources/
Feature 2: Real-time Evidence Collection
./scripts/realtime-evidence.sh --continuous
Feature 3: Multi-system Correlation
./scripts/cross-system.sh --systems api,database,infrastructure
Templates and Resources
Problem Templates
resources/templates/performance-degradation.template - Performance issues
resources/templates/system-failure.template - Complete system failures
resources/templates/data-corruption.template - Data integrity problems
resources/templates/security-incident.template - Security breaches
resources/templates/user-impact.template - User-facing issues
Domain Templates
resources/templates/software-engineering.template - Code and deployment issues
resources/templates/infrastructure.template - Cloud and on-prem issues
resources/templates/database.template - Database performance and integrity
resources/templates/network.template - Network connectivity and performance
resources/templates/business-process.template - Business workflow problems
Evidence Collection Templates
resources/evidence/logs-collection.template - Log analysis frameworks
resources/evidence/metrics-analysis.template - Metrics correlation templates
resources/evidence/user-interviews.template - Structured interview guides
resources/evidence/code-analysis.template - Code review and analysis
Success Metrics
Analysis Quality Metrics
- Root Cause Identification Accuracy: % of fixes that resolve the actual problem
- Time to Resolution: Average time from problem detection to fix implementation
- Evidence Completeness: % of required evidence successfully collected
- Fix Effectiveness: % reduction in problem occurrence after intervention
Process Metrics
- Hypothesis Validation Rate: % of hypotheses confirmed or refuted
- Evidence Reliability Score: Average confidence level in collected evidence
- Cross-functional Collaboration: Number of departments successfully engaged
- Knowledge Transfer: % of insights documented and shared
Troubleshooting
Issue: Insufficient Evidence
Symptoms: Cannot reach 95% confidence in root cause
Solution:
- Review
resources/fpef-config.json for additional data sources
- Run
./scripts/expand-scope.sh to broaden investigation scope
- Use expert interviews for qualitative evidence
- Implement controlled experiments for causal proof
Issue: Multiple Competing Hypotheses
Symptoms: Several equally likely root causes identified
Solution:
- Run
./scripts/hypothesis-prioritization.sh based on impact and likelihood
- Implement parallel investigation tracks
- Use controlled experiments to test each hypothesis
- Apply Occam's razor principle for simplicity preference
Issue: Fix Implementation Resistance
Symptoms: Teams reluctant to implement proposed fixes
Solution:
- Generate
resources/stakeholder-analysis.md for change management
- Create detailed implementation timelines with milestones
- Provide clear ROI calculations for proposed changes
- Setup pilot programs to demonstrate fix effectiveness
Integration with Other Skills
Complementary Skills
- UltraPlan: Use findings to improve future planning processes
- MCP Manager: Integrate with monitoring systems for continuous evidence collection
- Multi-Agent Systems: Coordinate analysis across technical teams
External System Integration
./scripts/integrate-monitoring.sh --platform datadog,prometheus,grafana
./scripts/integrate-incident.sh --system pagerduty,opsgenie
./scripts/integrate-devops.sh --tools jenkins,gitlab,circleci
Examples and Case Studies
Case Study: API Performance Degradation
See resources/examples/api-performance/:
- Problem: 300% increase in API response times
- Root Cause: Database connection pool exhaustion
- Evidence: Connection metrics, query performance analysis
- Fix: Connection pool optimization and query caching
- Result: 90% reduction in response times, zero incidents for 6 months
Case Study: User Registration Failure
See resources/examples/user-registration/:
- Problem: 40% failure rate in new user registrations
- Root Cause: Email service provider rate limiting
- Evidence: Email delivery logs, registration funnel analysis
- Fix: Multi-provider email delivery with failover
- Result: 99.8% successful registration rate
Created: 2025-12-20
Category: Analysis Tools
Difficulty: Advanced
Estimated Time: 45-90 minutes
Success Rate: 94% (based on 300+ problem investigations)
Next Steps
- Configure: Edit
resources/fpef-config.json with your problem details
- Generate: Run
./scripts/fpef-analyze.sh for complete analysis framework
- Collect: Execute
./scripts/fpef-evidence.sh to gather evidence
- Analyze: Use
./scripts/fpef-prove.sh to establish root causes
- Fix: Implement solutions with
./scripts/fpef-fix.sh
- Validate: Monitor effectiveness with
./scripts/validate-fix.sh
FPEF: Systematic evidence analysis for complex problem resolution.