# Analysis Skill > Analysis Expert - Skill: `sirhamza/analysis-skill` (Agent Skill) - Install (CLI): `npx skillmds@latest add sirhamza/analysis-skill` - Raw SKILL.md: https://api.skillmd.com/api/skills/sirhamza/analysis-skill/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: SirHamza (https://skillmd.com/u/sirhamza) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/sirhamza/analysis-skill --- # 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 1. **Scope definition** — clarify what is in/out of scope before starting 2. **Data collection** — gather code, logs, metrics, schemas, docs 3. **Pattern identification** — find recurring themes, anomalies, clusters 4. **Hypothesis formation** — propose explanations for observed patterns 5. **Validation** — test hypotheses against evidence 6. **Impact assessment** — quantify severity and business impact 7. **Root cause determination** — distinguish symptoms from root causes 8. **Recommendation formulation** — prioritized, actionable, time-boxed fixes 9. **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