Context-efficient codebase exploration using AST analysis. Use when exploring Kosmos architecture, understanding code structure, or preparing documentation for AI programmers. Triggers: xray, map structure, skeleton, interface, architecture, explore kosmos, warm start, token budget, context compression.
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
skills listSkill Instructions
name: kosmos-xray description: Context-efficient codebase exploration using AST analysis. Use when exploring Kosmos architecture, understanding code structure, or preparing documentation for AI programmers. Triggers: xray, map structure, skeleton, interface, architecture, explore kosmos, warm start, token budget, context compression.
Kosmos X-Ray Skill
Specialized tools for analyzing the Kosmos codebase efficiently within limited context windows. Uses AST parsing to extract structural information (classes, methods, signatures) without loading implementation details, achieving ~95% token reduction.
Enhanced Features (v2)
The skeleton extractor now captures:
- Pydantic/dataclass fields -
name: str = Field(...)visible in output - Decorators -
@dataclass,@property,@toolshown above definitions - Global constants -
CONFIG_VAR = "value"at module level - Line numbers - Every definition includes
# L{line}for navigation
IMPORTANT: Always use these features when exploring - they reveal data structures that would otherwise appear as empty pass statements.
When to Use This Skill
- Exploring the codebase - Map directory structure before diving into files
- Understanding architecture - Extract class hierarchies and dependencies
- Understanding data models - Skeleton shows Pydantic fields that define the data
- Onboarding - Generate documentation for new AI programmers
- Context management - Identify large files that should use skeleton view instead of full read
Core Tools
1. mapper.py - Directory Structure Map
Shows file tree with token estimates. Identifies context hazards (large files).
# Map entire project
python .claude/skills/kosmos-xray/scripts/mapper.py
# Map specific directory
python .claude/skills/kosmos-xray/scripts/mapper.py kosmos/workflow/
# Get summary only (no tree) - RECOMMENDED FIRST STEP
python .claude/skills/kosmos-xray/scripts/mapper.py --summary
# JSON output for parsing
python .claude/skills/kosmos-xray/scripts/mapper.py --json
2. skeleton.py - Interface Extraction (Enhanced)
Extracts Python file skeletons via AST. Now shows Pydantic fields, decorators, constants, and line numbers.
# Single file skeleton (includes line numbers by default)
python .claude/skills/kosmos-xray/scripts/skeleton.py kosmos/workflow/research_loop.py
# Directory with pattern filter
python .claude/skills/kosmos-xray/scripts/skeleton.py kosmos/ --pattern "**/base*.py"
# Filter by priority (critical, high, medium, low) - USE THIS FOR ONBOARDING
python .claude/skills/kosmos-xray/scripts/skeleton.py kosmos/ --priority critical
# Include private methods (_method) for internal understanding
python .claude/skills/kosmos-xray/scripts/skeleton.py kosmos/agents/ --private
# Omit line numbers if not needed
python .claude/skills/kosmos-xray/scripts/skeleton.py kosmos/config.py --no-line-numbers
# JSON output for programmatic use
python .claude/skills/kosmos-xray/scripts/skeleton.py kosmos/models/ --json
What skeleton.py reveals:
# Before (old behavior): Data models appeared empty
class Hypothesis(BaseModel):
pass
# After (enhanced): Full data structure visible
@dataclass
class PaperAnalysis: # L34
paper_id: str # L36
executive_summary: str # L37
confidence_score: float # L42
3. dependency_graph.py - Import Analysis
Maps import relationships between modules. Identifies architectural layers and circular dependencies.
# Analyze dependencies (text output)
python .claude/skills/kosmos-xray/scripts/dependency_graph.py kosmos/
# With root package name (recommended)
python .claude/skills/kosmos-xray/scripts/dependency_graph.py kosmos/ --root kosmos
# Focus on specific area
python .claude/skills/kosmos-xray/scripts/dependency_graph.py kosmos/ --focus workflow
# Generate Mermaid diagram for documentation - USE FOR WARM_START.md
python .claude/skills/kosmos-xray/scripts/dependency_graph.py kosmos/ --root kosmos --mermaid
# Combined: Mermaid focused on workflow
python .claude/skills/kosmos-xray/scripts/dependency_graph.py kosmos/ --root kosmos --mermaid --focus workflow
# JSON output
python .claude/skills/kosmos-xray/scripts/dependency_graph.py kosmos/ --json
Recommended Workflow (Use ALL Features)
- Survey first -
mapper.py --summaryto see codebase size and large files - X-ray critical classes -
skeleton.py --priority criticalto see core interfaces WITH FIELDS - Generate architecture diagram -
dependency_graph.py --mermaidfor visual map - Verify imports - Run import checks before documenting entry points
- Read selectively - Only read full implementation when skeleton isn't enough
Best Practices
DO:
- Always use
--priority criticalfirst to understand core architecture - Use
--mermaidoutput for documentation diagrams - Check line numbers when you need to reference specific code
- Use
--privatewhen understanding internal agent behavior - Verify imports before documenting them as entry points
DON'T:
- Read full files when skeleton would suffice (wastes context)
- Ignore large file warnings from mapper.py
- Skip the Pydantic fields - they define the data contracts
- Forget to include line numbers in documentation references
Integration with kosmos_architect Agent
This skill is automatically loaded by the kosmos_architect agent. You can also use it directly for targeted analysis.
# Use the agent for full onboarding documentation (uses ALL features)
@kosmos_architect generate
# Or use individual tools directly
@kosmos-xray Map the workflow directory
Configuration Files
configs/ignore_patterns.json- Directories and files to skipconfigs/priority_modules.json- Module priority levels and patterns
Context Budget Guidelines
| Operation | Typical Tokens | Use When |
|---|---|---|
| mapper.py --summary | ~500 | First exploration |
| mapper.py full | ~2-5K | Understanding structure |
| skeleton.py (1 file) | ~200-500 | Understanding interface |
| skeleton.py --priority critical | ~5K | Core architecture |
| dependency_graph.py text | ~2-3K | Architecture analysis |
| dependency_graph.py --mermaid | ~500 | Documentation diagrams |
| Full file read | Varies | Need implementation details |
For detailed API documentation, see reference.md. For quick command reference, see CHEATSHEET.md.
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