
deep-analysis
Analytical thinking patterns for comprehensive evaluation, code audits, security analysis, and performance reviews. Provides structured templates for thorough investigation with extended thinking support.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/thelobbi/claude --skill deep-analysis- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads deep-analysis automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
| ID | Severity | Location | Issue | Recommendation |
|----|----------|----------|-------|----------------|
| C1 | Critical | file:line | [Issue] | [Fix] |
| C2 | Critical | file:line | [Issue] | [Fix] |
### High Priority Findings
| ID | Severity | Location | Issue | Recommendation |
|----|----------|----------|-------|----------------|
| H1 | High | file:line | [Issue] | [Fix] |
### Medium Priority Findings
[...]
### Low Priority / Suggestions
[...]
### Metrics
| Metric | Value | Target | Status |
|--------|-------|--------|--------|
| Test Coverage | 75% | 80% | ⚠️ |
| Cyclomatic Complexity | 12 | <10 | ⚠️ |
| Technical Debt | 4.2d | <3d | ❌ |
| Security Score | 8/10 | 9/10 | ⚠️ |
### 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
| Asset | Description | Sensitivity | Owner |
|-------|-------------|-------------|-------|
| User Data | PII, credentials | Critical | Auth Team |
| API Keys | Service credentials | High | DevOps |
| Business Data | Transactions | High | Product |
### 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
| Threat | Likelihood | Impact | Mitigation |
|--------|------------|--------|------------|
| Credential theft | Medium | High | MFA, rate limiting |
| Session hijacking | Low | High | Secure cookies, HTTPS |
#### Tampering
| Threat | Likelihood | Impact | Mitigation |
|--------|------------|--------|------------|
| SQL injection | Medium | Critical | Parameterized queries |
| Data modification | Low | High | Integrity 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
| Threat | Likelihood | Impact | Risk Score | Priority |
|--------|------------|--------|------------|----------|
| T1 | High | Critical | 9 | P1 |
| T2 | Medium | High | 6 | P2 |
| T3 | Low | Medium | 3 | P3 |
### Security Controls
| Control | Type | Status | Coverage |
|---------|------|--------|----------|
| WAF | Preventive | ✅ Active | External |
| SAST | Detective | ✅ CI/CD | Code |
| DAST | Detective | ⚠️ Partial | Runtime |
| Encryption | Preventive | ✅ Active | Data |
### 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
| Endpoint | P50 | P95 | P99 | Target | Status |
|----------|-----|-----|-----|--------|--------|
| /api/users | 45ms | 120ms | 350ms | <200ms | ✅ |
| /api/search | 230ms | 890ms | 2.1s | <500ms | ❌ |
| /api/reports | 1.2s | 3.4s | 8.2s | <2s | ❌ |
#### Throughput
| Service | Current RPS | Peak RPS | Capacity | Utilization |
|---------|-------------|----------|----------|-------------|
| API | 1,200 | 2,400 | 5,000 | 48% |
| Worker | 500 | 800 | 1,000 | 80% |
#### Resource Utilization
| Resource | Average | Peak | Threshold | Status |
|----------|---------|------|-----------|--------|
| CPU | 45% | 78% | 80% | ⚠️ |
| Memory | 62% | 85% | 85% | ⚠️ |
| Disk I/O | 30% | 55% | 70% | ✅ |
| Network | 25% | 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
| Scenario | Users | Duration | Errors | Avg Response |
|----------|-------|----------|--------|--------------|
| Baseline | 100 | 10min | 0% | 120ms |
| Normal | 500 | 30min | 0.1% | 180ms |
| Peak | 1000 | 15min | 2.3% | 450ms |
| Stress | 2000 | 5min | 15% | 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
| Timeframe | Expected Load | Current Capacity | Gap | Action |
|-----------|---------------|------------------|-----|--------|
| 3 months | +25% | 5,000 RPS | ✅ | Monitor |
| 6 months | +50% | 5,000 RPS | ⚠️ | Scale |
| 12 months | +100% | 5,000 RPS | ❌ | Redesign |
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
| Component | Purpose | Technology | Owner |
|-----------|---------|------------|-------|
| API Gateway | Request routing | Kong | Platform |
| Auth Service | Authentication | Keycloak | Security |
| Core API | Business logic | Python/FastAPI | Backend |
| Database | Data persistence | PostgreSQL | Data |
#### 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
| Aspect | Current | Target | Gap | Score |
|--------|---------|--------|-----|-------|
| Horizontal scaling | Manual | Auto | Yes | 6/10 |
| Database scaling | Single | Sharded | Yes | 5/10 |
| Caching | Redis | Distributed | No | 8/10 |
#### Reliability
| Aspect | Current | Target | Gap | Score |
|--------|---------|--------|-----|-------|
| Availability | 99.5% | 99.9% | Yes | 7/10 |
| Disaster recovery | Manual | Auto | Yes | 5/10 |
| Data backup | Daily | Real-time | Yes | 6/10 |
#### Maintainability
| Aspect | Current | Target | Gap | Score |
|--------|---------|--------|-----|-------|
| Code modularity | Medium | High | Yes | 6/10 |
| Documentation | Partial | Complete | Yes | 5/10 |
| Test coverage | 70% | 85% | Yes | 7/10 |
### Technical Debt Assessment
| Item | Impact | Effort | Priority | Age |
|------|--------|--------|----------|-----|
| Legacy auth system | High | High | P1 | 2y |
| Monolithic API | Medium | High | P2 | 1.5y |
| Missing monitoring | Medium | Low | P1 | 1y |
### 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
| Decision | Rationale | Alternatives Considered | Date |
|----------|-----------|------------------------|------|
| [Decision 1] | [Why] | [Options] | [Date] |
Integration with Extended Thinking
For deep analysis tasks, use adaptive thinking at maximum effort:
response = client.messages.create(
model="claude-opus-4-8",
max_tokens=32000,
thinking={"type": "adaptive"},
output_config={"effort": "max"}, # maximum reasoning depth 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
FAQ
What does the deep-analysis skill do?
Analytical thinking patterns for comprehensive evaluation, code audits, security analysis, and performance reviews. Provides structured templates for thorough investigation with extended thinking support.
How do I install deep-analysis?
Run npx skills add https://github.com/thelobbi/claude --skill deep-analysis in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/deep-analysis/ (personal) or .claude/skills/deep-analysis/ (project).
Which AI agents can use deep-analysis?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is deep-analysis safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains deep-analysis?
It is published by thelobbi in thelobbi/claude. It has 63 recorded installs.
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