athola/file-analysis
plugins/sanctum/skills/file-analysis/SKILL.md
Maps file structure and module organization of a codebase. Use before architecture reviews, refactoring planning, or migration scope estimation.
$ install --global
skillsauthnpx skillsauth add athola/claude-night-market file-analysisInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Jun 28, 2026, 4:10 AM118.1s1 file scanned
SKILL.md
- name:
- file-analysis
- description:
- Maps file structure and module organization of a codebase. Use before architecture reviews, refactoring planning, or migration scope estimation.
- alwaysApply:
- false
- category:
- workspace-ops
- tools:
- []
- complexity:
- medium
- model_hint:
- standard
- estimated_tokens:
- 800
- - imbue:
- proof-of-work
File Analysis
When To Use
- Before architecture reviews to understand module boundaries and file organization.
- When exploring unfamiliar codebases to map structure before making changes.
- As input to scope estimation for refactoring or migration work.
When NOT To Use
- General code exploration - use the Explore agent
- Searching for specific patterns - use Grep directly
Required TodoWrite Items
file-analysis:root-identifiedfile-analysis:structure-mappedfile-analysis:patterns-detectedfile-analysis:hotspots-noted
Mark each item as complete as you finish the corresponding step.
Step 1: Identify Root (file-analysis:root-identified)
- Confirm the analysis root directory with
pwd. - Note any monorepo boundaries, workspace roots, or subproject paths.
- Capture the project type (language, framework) from manifest files (
package.json,Cargo.toml,pyproject.toml, etc.).
Step 2: Map Structure (file-analysis:structure-mapped)
- Run
tree -L 2 -dorfind . -type d -maxdepth 2to capture the top-level directory layout. - Identify standard directories:
src/,lib/,tests/,docs/,scripts/,configs/. - Note any non-standard organization patterns that may affect downstream analysis.
Step 3: Detect Patterns (file-analysis:patterns-detected)
- Use
find . -name "*.ext" -not -path "*/.venv/*" -not -path "*/__pycache__/*" -not -path "*/node_modules/*" -not -path "*/.git/*" | wc -lto count files by extension. - Identify dominant languages and their file distributions.
- Note configuration files, generated files, and vendored dependencies.
- Run
wc -l $(find . -not -path "*/.venv/*" -not -path "*/__pycache__/*" -not -path "*/node_modules/*" -not -path "*/.git/*" -name "*.py" -o -name "*.rs" | head -20)to sample file sizes.
Step 4: Note Hotspots (file-analysis:hotspots-noted)
- Identify large files (potential "god objects"):
find . -type f -exec wc -l {} + | sort -rn | head -10. - Flag deeply nested directories that may indicate complexity.
- Note files with unusual naming conventions or placement.
Exit Criteria
TodoWriteitems are completed with concrete observations.- Downstream workflows (architecture review, refactoring) have structural context.
- File counts, directory layout, and hotspots are documented for reference.
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