Analyze functions with AST, call graph, CFG, DFG, and program slicing.
npx skills add https://github.com/parcadei/Continuous-Claude-v3 --skill tldr-deep
Please help me install this Agent Skill. Skill: tldr-deep Source: https://github.com/parcadei/Continuous-Claude-v3/tree/main/.claude/skills/tldr-deep Command: npx skills add https://github.com/parcadei/Continuous-Claude-v3 --skill tldr-deep
This Skill provides a comprehensive, multi-layered analysis of specific functions within code, helping developers understand complex logic, identify bugs, and optimize performance.
Analyze the function named 'process_data' in the file 'src/processor.py' in detail.
High-intent search queries and answers about installing and using this skill.
Deep function analysis for debugging uses a 5-layer breakdown—AST, call graph, CFG, DFG, and program slicing—to trace execution paths and data dependencies. This isolates root causes in complex code segments without reading raw files.
Analyzing call graphs and data flow for refactoring involves extracting structural and dependency information across five layers, including Abstract Syntax Trees and Control Flow Graphs. This reveals intricate function behaviors and dependencies to guide optimizations.
Program slicing helps understand complex code logic by isolating specific execution paths and data dependencies within a function. Combined with CFG and DFG analysis, it extracts only the relevant structural navigational information, reducing token usage.
You can use AST and CFG analysis to optimize code performance by breaking down function complexity and tracing all execution paths. This multi-layered extraction identifies performance bottlenecks and intricate dependencies within the codebase.
Raw file access is not needed to trace execution paths and data dependencies. The analysis leverages structural extraction to provide navigational and data flow information, offering significant token savings compared to analyzing raw files directly.
Limitations of multi-layer code analysis for bug tracking include its narrow scope: it targets specific functions rather than entire codebases. It extracts structural, navigational, and complexity data, making it less suited for broad architectural reviews.
DojoGenesisGenerate token-efficient structural summaries of codebases using AST, call graph, CFG, DFG, and PDG.
brainbrewlabsFix code scanner, security auditor, and linter issues with verified edits and reports.
yalehRefactor Go hotspots to reduce cyclomatic complexity and improve maintainability.
ByronWilliamsCPATrace code dependencies and call chains using a pre-built knowledge graph.
