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Static code analysis, technical debt assessment, engineering velocity metrics
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| name | code-quality-analyzer |
| description | Static code analysis, technical debt assessment, engineering velocity metrics |
| allowed-tools | ["Read","Write","Glob","Grep","Bash","WebFetch"] |
| metadata | {"specialization":"venture-capital","domain":"business","skill-id":"vc-skill-014"} |
| graph | {"domains":["domain:venture-capital"],"skillAreas":["skill-area:investment-analysis","skill-area:valuation-analysis","skill-area:financial-modeling"],"roles":["role:financial-analyst"]} |
The Code Quality Analyzer skill provides detailed code-level analysis for technical due diligence. It performs static code analysis, assesses technical debt, and evaluates engineering team velocity to understand code health and development productivity.
Input: Repository access, analysis parameters
Process: Run static analysis, aggregate metrics
Output: Code quality report, issue summary
Input: Codebase access, debt categorization
Process: Inventory debt, estimate remediation
Output: Technical debt assessment, prioritization
Input: Git history, project management data
Process: Calculate velocity metrics
Output: Velocity report, trend analysis
Input: Repository data, team practices
Process: Analyze patterns, compare benchmarks
Output: Code health scorecard, recommendations
| Metric | Description | Target Range |
|---|---|---|
| Test Coverage | % of code covered by tests | 70-90% |
| Code Complexity | Cyclomatic complexity average | < 10 |
| Tech Debt Ratio | Debt remediation time / dev time | < 5% |
| Deployment Frequency | Deployments per week | Daily to weekly |
| Change Failure Rate | % of deployments causing issues | < 15% |
Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
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