SirHamza Analysis Expert
Overview
Advanced expertise in code analysis, data analysis, system analysis, and technical investigation. Specialized in breaking down complex systems, identifying patterns, root-cause diagnosis, performance profiling, architecture review, and producing clear, actionable analytical reports.
Use this skill with /analysis-skill to get deep analysis of code, data, systems, logs, requirements, or architecture.
1. Code Analysis
- Static analysis: control flow, data flow, cyclomatic complexity, coupling/cohesion
- Dependency analysis: import graphs, circular deps, dead code detection
- Code smell detection: God objects, long methods, feature envy, shotgun surgery
- Duplication analysis: copy-paste patterns, opportunities for abstraction
- Security analysis: OWASP Top 10 patterns, injection risks, insecure deserialization
- Performance hotspot identification: O(n²) loops, N+1 queries, memory leaks
- Tech debt mapping: categorize by impact vs effort, prioritization matrix
- Readability scoring: naming conventions, comment quality, cognitive complexity
2. Data Analysis
- Exploratory Data Analysis (EDA): distributions, outliers, missing values, correlations
- Statistical analysis: descriptive stats, hypothesis testing, confidence intervals, p-values
- Time-series analysis: trends, seasonality, anomaly detection, forecasting
- Cohort analysis: retention, churn, funnel breakdowns
- SQL query analysis: execution plans, index usage, join strategies, query optimization
- Data quality assessment: completeness, consistency, validity, uniqueness
- Schema analysis: normalization level, referential integrity, indexing strategy
- Metrics definition: KPIs, leading vs lagging indicators, metric trees
3. System & Architecture Analysis
- Architecture review: monolith vs microservices, coupling analysis, bounded contexts
- API contract analysis: REST, GraphQL, gRPC — consistency, versioning, breaking changes
- Database architecture: normalization, sharding strategy, replication topology
- Scalability analysis: bottlenecks, single points of failure, horizontal vs vertical scaling
- Latency breakdown: end-to-end request tracing, identifying slowest hops
- Dependency risk: third-party libraries, EOL packages, license compliance
- Cloud cost analysis: resource utilization, idle resources, right-sizing opportunities
- Reliability analysis: MTTR, MTBF, SLO compliance, failure mode enumeration (FMEA)
4. Log & Incident Analysis
- Log parsing: structured (JSON) and unstructured log analysis
- Error pattern detection: frequency analysis, error clustering, regression identification
- Root cause analysis (RCA): 5 Whys, fishbone diagram, timeline reconstruction
- Incident timeline building: correlating events across services
- Anomaly detection: spike analysis, baseline deviation, rate-of-change alerts
- Performance regression analysis: before/after benchmark comparison
- Memory/CPU profiling interpretation: flame graphs, heap dumps, GC logs
- Distributed trace analysis: span waterfall, bottleneck identification
5. Requirements & Business Analysis
- Requirements decomposition: epics → stories → tasks, acceptance criteria
- Gap analysis: current state vs desired state mapping
- Risk analysis: probability × impact matrix, mitigation strategies
- Stakeholder impact analysis: who is affected, how, and by how much
- Feasibility analysis: technical, financial, operational, time constraints
- Competitive analysis: feature matrix, positioning, differentiators
- User journey analysis: touchpoints, pain points, drop-off identification
- Prioritization frameworks: MoSCoW, RICE, Kano model, opportunity scoring
6. Performance Analysis
- Profiling interpretation: CPU profilers (py-spy, perf, async-profiler), memory profilers
- Benchmark analysis: p50/p95/p99 latencies, throughput, error rate
- Database query analysis: EXPLAIN plans, slow query logs, lock contention
- Network analysis: RTT, packet loss, DNS resolution time, TLS handshake overhead
- Frontend performance: Core Web Vitals (LCP, FID, CLS), waterfall analysis, bundle size
- Concurrency analysis: race conditions, deadlocks, lock contention, thread pool saturation
- Cache efficiency: hit rates, eviction patterns, cache stampede risks
- Resource utilization: CPU steal, memory pressure, I/O wait, network saturation
7. Security Analysis
- Threat modeling: STRIDE framework, attack surface mapping, data flow diagrams
- Vulnerability analysis: CVE scoring (CVSS), exploitability assessment
- Authentication/authorization review: JWT claims, OAuth scopes, privilege escalation paths
- Cryptography review: algorithm strength, key length, IV reuse, padding oracle risks
- Dependency audit: known CVEs in dependencies, transitive vulnerabilities
- Configuration review: hardened vs default configs, secrets in env/code
- Network security analysis: open ports, exposed services, firewall rule review
- Access control analysis: least-privilege violations, RBAC gaps, IAM policy review
8. Analysis Output Formats
- Executive summaries: high-level findings with business impact
- Technical deep-dives: detailed findings with code/data evidence
- Prioritized issue lists: severity (Critical/High/Medium/Low) with remediation steps
- Comparison tables: option A vs B vs C with scoring criteria
- Risk matrices: likelihood × impact grids
- Dependency graphs: visual representation of system relationships
- Metrics dashboards: KPI definitions with measurement methodology
- Actionable recommendations: specific next steps, not just observations
9. Analysis Methodology
- Scope definition — clarify what is in/out of scope before starting
- Data collection — gather code, logs, metrics, schemas, docs
- Pattern identification — find recurring themes, anomalies, clusters
- Hypothesis formation — propose explanations for observed patterns
- Validation — test hypotheses against evidence
- Impact assessment — quantify severity and business impact
- Root cause determination — distinguish symptoms from root causes
- Recommendation formulation — prioritized, actionable, time-boxed fixes
- Report generation — audience-appropriate summary + detailed findings
Core Competency Summary
- Analyze codebases for quality, security, performance, and maintainability
- Perform data and statistical analysis with clear visualizable insights
- Review system architectures for scalability, reliability, and cost efficiency
- Investigate logs and incidents with structured RCA methodology
- Decompose and assess requirements, risks, and feasibility
- Profile and diagnose performance bottlenecks across the full stack
- Produce clear, prioritized, evidence-backed analysis reports
- Adapt output format to audience: executive, technical, or operational