deep-analysis

Comprehensive analytical templates for thorough investigation, audits, and evaluations leveraging extended thinking capabilities.

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Install skill "deep-analysis" with this command: npx skills add lobbi-docs/claude/lobbi-docs-claude-deep-analysis

Deep Analysis Skill

Comprehensive analytical templates for thorough investigation, audits, and evaluations leveraging extended thinking capabilities.

When to Use

  • Code audits requiring systematic review

  • Security assessments and threat modeling

  • Performance analysis and optimization planning

  • Architecture reviews and technical debt assessment

  • Incident post-mortems and root cause analysis

  • Compliance audits and risk assessments

Analysis Templates

Code Audit Template

Code Audit Report

Repository: [repo-name] Scope: [files/modules audited] Date: [YYYY-MM-DD] Auditor: Claude + [Human reviewer]

Executive Summary

[2-3 sentence overview of findings]

Audit Criteria

  • Code quality and maintainability
  • Security vulnerabilities
  • Performance concerns
  • Test coverage
  • Documentation completeness
  • Dependency health

Critical Findings

IDSeverityLocationIssueRecommendation
C1Criticalfile:line[Issue][Fix]
C2Criticalfile:line[Issue][Fix]

High Priority Findings

IDSeverityLocationIssueRecommendation
H1Highfile:line[Issue][Fix]

Medium Priority Findings

[...]

Low Priority / Suggestions

[...]

Metrics

MetricValueTargetStatus
Test Coverage75%80%⚠️
Cyclomatic Complexity12<10⚠️
Technical Debt4.2d<3d
Security Score8/109/10⚠️

Recommendations

  1. Immediate: [Critical fixes]
  2. Short-term: [Within sprint]
  3. Long-term: [Tech debt reduction]

Sign-off

  • All critical issues addressed
  • High priority issues have timeline
  • Audit findings documented in backlog

Security Threat Model Template

Threat Model: [System/Component Name]

Version: [1.0] Last Updated: [YYYY-MM-DD] Classification: [Internal/Confidential]

System Overview

[Brief description of the system being modeled]

Assets

AssetDescriptionSensitivityOwner
User DataPII, credentialsCriticalAuth Team
API KeysService credentialsHighDevOps
Business DataTransactionsHighProduct

Trust Boundaries

┌─────────────────────────────────────────┐ │ External (Untrusted) │ │ [Internet Users] [Third-party APIs] │ └──────────────────┬──────────────────────┘ │ WAF/Load Balancer ┌──────────────────┴──────────────────────┐ │ DMZ (Semi-trusted) │ │ [API Gateway] [CDN] [Public Services] │ └──────────────────┬──────────────────────┘ │ Internal Firewall ┌──────────────────┴──────────────────────┐ │ Internal (Trusted) │ │ [App Servers] [Databases] [Queues] │ └─────────────────────────────────────────┘

Threat Categories (STRIDE)

Spoofing

ThreatLikelihoodImpactMitigation
Credential theftMediumHighMFA, rate limiting
Session hijackingLowHighSecure cookies, HTTPS

Tampering

ThreatLikelihoodImpactMitigation
SQL injectionMediumCriticalParameterized queries
Data modificationLowHighIntegrity checks

Repudiation

[...]

Information Disclosure

[...]

Denial of Service

[...]

Elevation of Privilege

[...]

Attack Vectors

  1. Vector 1: [Description]
    • Entry point: [Where]
    • Technique: [How]
    • Mitigation: [Defense]

Risk Matrix

ThreatLikelihoodImpactRisk ScorePriority
T1HighCritical9P1
T2MediumHigh6P2
T3LowMedium3P3

Security Controls

ControlTypeStatusCoverage
WAFPreventive✅ ActiveExternal
SASTDetective✅ CI/CDCode
DASTDetective⚠️ PartialRuntime
EncryptionPreventive✅ ActiveData

Recommendations

  1. [Priority 1 recommendations]
  2. [Priority 2 recommendations]
  3. [Priority 3 recommendations]

Performance Analysis Template

Performance Analysis Report

System: [System name] Period: [Date range] Environment: [Production/Staging]

Executive Summary

[Key findings and recommendations]

Performance Metrics

Response Times

EndpointP50P95P99TargetStatus
/api/users45ms120ms350ms<200ms
/api/search230ms890ms2.1s<500ms
/api/reports1.2s3.4s8.2s<2s

Throughput

ServiceCurrent RPSPeak RPSCapacityUtilization
API1,2002,4005,00048%
Worker5008001,00080%

Resource Utilization

ResourceAveragePeakThresholdStatus
CPU45%78%80%⚠️
Memory62%85%85%⚠️
Disk I/O30%55%70%
Network25%40%60%

Bottleneck Analysis

Identified Bottlenecks

  1. Database Queries (High Impact)

    • Location: /api/search endpoint
    • Cause: Missing index on created_at column
    • Impact: 890ms P95 latency
    • Fix: Add composite index
  2. Memory Pressure (Medium Impact)

    • Location: Report generation service
    • Cause: Large dataset loading into memory
    • Impact: GC pauses, OOM risks
    • Fix: Implement streaming/pagination

Load Test Results

ScenarioUsersDurationErrorsAvg Response
Baseline10010min0%120ms
Normal50030min0.1%180ms
Peak100015min2.3%450ms
Stress20005min15%2.1s

Optimization Recommendations

Quick Wins (This Sprint)

  1. Add database indexes - Expected: 40% improvement
  2. Enable query caching - Expected: 25% improvement
  3. Optimize N+1 queries - Expected: 30% improvement

Medium Term (Next Quarter)

  1. Implement read replicas
  2. Add CDN for static assets
  3. Optimize serialization

Long Term (6+ Months)

  1. Service decomposition
  2. Event-driven architecture
  3. Edge computing deployment

Capacity Planning

TimeframeExpected LoadCurrent CapacityGapAction
3 months+25%5,000 RPSMonitor
6 months+50%5,000 RPS⚠️Scale
12 months+100%5,000 RPSRedesign

Architecture Review Template

Architecture Review

System: [System name] Version: [Current architecture version] Review Date: [YYYY-MM-DD] Participants: [Team members]

Current Architecture

System Diagram

[Include architecture diagram or ASCII representation]

Components

ComponentPurposeTechnologyOwner
API GatewayRequest routingKongPlatform
Auth ServiceAuthenticationKeycloakSecurity
Core APIBusiness logicPython/FastAPIBackend
DatabaseData persistencePostgreSQLData

Data Flow

  1. User request → API Gateway
  2. API Gateway → Auth validation
  3. Auth → Core API
  4. Core API → Database
  5. Response → User

Evaluation Criteria

Scalability

AspectCurrentTargetGapScore
Horizontal scalingManualAutoYes6/10
Database scalingSingleShardedYes5/10
CachingRedisDistributedNo8/10

Reliability

AspectCurrentTargetGapScore
Availability99.5%99.9%Yes7/10
Disaster recoveryManualAutoYes5/10
Data backupDailyReal-timeYes6/10

Maintainability

AspectCurrentTargetGapScore
Code modularityMediumHighYes6/10
DocumentationPartialCompleteYes5/10
Test coverage70%85%Yes7/10

Technical Debt Assessment

ItemImpactEffortPriorityAge
Legacy auth systemHighHighP12y
Monolithic APIMediumHighP21.5y
Missing monitoringMediumLowP11y

Recommendations

Immediate (0-3 months)

  1. [Recommendation 1]
  2. [Recommendation 2]

Short-term (3-6 months)

  1. [Recommendation 1]
  2. [Recommendation 2]

Long-term (6-12 months)

  1. [Recommendation 1]
  2. [Recommendation 2]

Decision Log

DecisionRationaleAlternatives ConsideredDate
[Decision 1][Why][Options][Date]

Integration with Extended Thinking

For deep analysis tasks, use maximum thinking budget:

response = client.messages.create( model="claude-opus-4-5-20250514", max_tokens=32000, thinking={ "type": "enabled", "budget_tokens": 25000 # Maximum budget for deep analysis }, system="""You are a senior technical analyst performing a comprehensive review. Use structured analysis templates and document all findings systematically.""", messages=[{ "role": "user", "content": "Perform a security threat model for..." }] )

Best Practices

  • Use appropriate templates: Match template to analysis type

  • Be systematic: Follow the template structure completely

  • Quantify findings: Use metrics and severity ratings

  • Prioritize actionable: Focus on findings that can be fixed

  • Document evidence: Link to specific code/logs/data

  • Track progress: Update findings as they're addressed

See Also

  • [[extended-thinking]] - Enable deep reasoning capabilities

  • [[complex-reasoning]] - Reasoning frameworks

  • [[testing]] - Validation strategies

  • [[debugging]] - Issue investigation

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