193건 · 관련도순
Scan source code for security weaknesses with Semgrep after approving the target and rulesets. Choose full results or focus on important findings, then receive merged results and a record of failed or skipped scans.
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Analyze a monolithic codebase before splitting it into modules or services. The skill inventories components, examines duplication, hierarchy, and dependencies, then proposes groupings aligned with business domains, with optional checks against business terminology and boundaries.
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Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.
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Perform codebase analysis and architecture mapping as the first phase of a security assessment. Explores the tech stack, frameworks, entry points, data flows, and trust boundaries. Outputs sast/architecture.md. Run this before any vulnerability detection skill. Use when asked to analyze a codebase for security or when sast/architecture.md does not yet exist.
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Guidelines for introducing and using nullable reference types (NRT) and System.Diagnostics.CodeAnalysis nullable attributes in C# / .NET codebases. Covers the nullability model, flow analysis, the null-forgiving operator, API design rules, the full attribute catalog (AllowNull, DisallowNull, MaybeNull, NotNull, NotNullWhen, MaybeNullWhen, NotNullIfNotNull, MemberNotNull, MemberNotNullWhen, DoesNotReturn, DoesNotReturnIf), the C# 14 field keyword, incremental migration of legacy codebases, and a code-generation checklist.
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Run Semgrep static analysis across a codebase, optionally using Semgrep Pro for cross-file taint analysis. Use when Semgrep or a static-analysis scan is requested; use security-review for a manual audit.
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Codebase'i knowledge graph olarak analiz et. Dependency, call graph, hotspot analizi.
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Use the open-source CodeQL ecosystem for .NET security analysis. Use when a repo needs CodeQL query packs, CLI-based analysis on open source codebases, or GitHub Action setup with explicit licensing caveats. USE FOR: the repo uses or wants CodeQL for .NET security analysis; GitHub code scanning is part of the CI plan. DO NOT USE FOR: teams that need a tool with no private-repo licensing caveat. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use automated-test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for designing a new interface or contract — use design-an-api. Not for planning the change its findings imply — use plan-a-change. Not for discovering bounded contexts, ubiquitous language, or where code boundaries diverge from domain boundaries — use ddd-analysis. Not for writing documentation or architectural decision records.
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Measures cyclomatic complexity, cognitive complexity, and function length across codebases to identify maintenance hotspots. Use when someone asks about code complexity, function length analysis, maintainability metrics, or needs to find the most complex parts of their codebase. Trigger words: complexity, cyclomatic, cognitive complexity, long functions, hotspots, maintainability index, code metrics.
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Vulnerability audit and dataflow analysis built on the neug CPG engine. file_audit (default, recommended): first builds a function-level summary graph (tier1) over the entire project, then incrementally builds detailed CPGs (tier2) on demand for chosen functions; both tiers coexist in a single neug database (unified schema, cross-layer refines edges; C/Java/Python/Rust). All capabilities are exposed as CLI tool subcommands (audit/overview/build/query/methods/paths), so an agent can drive the audit step by step via shell; the audit subcommand can also run the built-in LLM loop. The same tooling also supports a dataflow analysis mode (Workflow C): no vulnerability hunting, only variable-level dataflow tracing for functions of interest, producing dataflow_analysis.json. repo_audit: multi-round LLM/Cypher audit over a full-repo CPG. Use when the user asks to audit a codebase/project for vulnerabilities, scan a project with CPG/neug, run repo_audit/file_audit, find source-to-sink taint paths, or analyze dataflow inside a function. Supports C, Java, Python, and Rust.
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Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.
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Local codebase analysis research angle — derives behavioral contracts, coding conventions, SKILL.md flow insertion points, and agent data availability maps from actual source files. Use when the blocking question is answered by reading the repository: what does this function actually do, what pattern does the codebase use for X, where in this workflow does a new step go, or what data does this agent already have.
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Produces a concise, structured analysis report of a codebase: dependencies, workflow, and context.
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Deep exploration and synthesis workflow using agent teams with dynamic planning and hub-and-spoke coordination. Use when asked for "deep analysis", "deep understanding", "analyze codebase", "explore and analyze", or "investigate codebase".
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Produce a structured codebase analysis report with architecture overview, critical files, patterns, and actionable recommendations.
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Deep exploration and synthesis workflow using Agent Teams with dynamic planning and hub-and-spoke coordination. Use when asked for "deep analysis", "deep understanding", "analyze codebase", "explore and analyze", or "investigate codebase".
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Analyze an arbitrary codebase to auto-detect workflows, data pipelines, and file dependencies using putior's put_auto() engine. Produces an annotation plan that maps detected I/O patterns to source files across 30+ supported languages with 902 auto-detection patterns. Use when onboarding onto an unfamiliar codebase to understand data flow, starting putior integration in a project without existing annotations, auditing a project's data pipeline before documentation, or preparing an annotation plan before running annotate-source-files.
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Analyze codebases using a graph database instead of reading source files. Use when understanding code architecture, finding functions or call patterns, tracing data flow, checking dependencies, or answering "where is X used?" questions. Grafema builds a queryable code graph from static analysis — prefer querying the graph over reading files manually.
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Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.
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Four-phase skill for planning and executing large-scale codebase migrations using full-context AI analysis. Produces an impact map, ordered batch plan, subagent execution protocol, and cross-codebase validation pass. Optimized for Fable 5's 1M-token context window.
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Use when a project has no docs/ directory or needs initial documentation generated from codebase analysis. Triggers on "init docs", "create documentation", "generate docs", "docs init", or when docs/ is empty. Do NOT use for updating existing docs (use mk:document-release).
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Deep multi-pass codebase audit — orchestrates project-analysis-core plus the framework-specific project-analysis-*; explicit request only. Single-pass scan → project-analyzer.
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Use when picking which analysis or project-analysis-* skill fits a request — routes by scope, framework, and symptom — even if the user just says 'analyze this' or 'dig into the codebase'.
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