Safe Rewriting Advisor
**Category**: Graph Optimization + Remediation
Best use case
Safe Rewriting Advisor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
**Category**: Graph Optimization + Remediation
Teams using Safe Rewriting Advisor should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/safe-rewriting-advisor/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Safe Rewriting Advisor Compares
| Feature / Agent | Safe Rewriting Advisor | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
**Category**: Graph Optimization + Remediation
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Safe Rewriting Advisor
**Category**: Graph Optimization + Remediation
**Type**: Strategic Edge Removal Analysis
**Language**: Julia
**Status**: Production Ready
**Version**: 1.0.0
**Date**: December 22, 2025
## Overview
Strategic selective edge removal maintaining spectral gap ≥ 0.25. Analyzes edge criticality via betweenness centrality to identify which proof dependencies can be safely removed without breaking system connectivity (Ramanujan property).
## Key Data Structures
```julia
struct EdgeImportance
edge_id::Tuple{Int, Int}
betweenness_centrality::Float64
gap_sensitivity::Float64
redundancy_score::Float64
recommendation::String
end
struct RewritePlan
edges_to_remove::Vector{Tuple{Int, Int}}
edges_to_split::Vector{Tuple{Int, Int}}
cycle_breakers::Vector{String}
expected_gap_before::Float64
expected_gap_after::Float64
safe::Bool
complexity::String
end
```
## Key Functions
- **`compute_edge_importance(adjacency)`**: Betweenness centrality analysis
- **`identify_redundant_edges(edges)`**: Find safe-to-remove edges
- **`generate_rewrite_plan(adjacency, gap)`**: Strategic remediation
- **`generate_rewrite_report(adjacency, gap)`**: Human-readable analysis
## Mathematical Foundation
**Edge Criticality Classification**
```
gap_sensitivity > 80% : CRITICAL - essential for connectivity
40-80% : IMPORTANT - remove only if necessary
< 40% : REDUNDANT - safe to remove
```
Uses betweenness centrality to measure how many paths depend on each edge. Recommends cycle-breaking via intermediate theorems for low-gap systems.
## Usage
```julia
using SafeRewriting
# Analyze current system
plan = generate_rewrite_plan(adjacency, current_gap)
# Check if transformation is safe
if plan.safe && plan.expected_gap_after >= 0.25
println("✓ Safe to apply $(length(plan.edges_to_remove)) edge removals")
println(" Gap projection: $(plan.expected_gap_before) → $(plan.expected_gap_after)")
end
# Get recommendations
report = generate_rewrite_report(adjacency, current_gap)
```
## Integration Points
- Week 4 remediation planning phase
- Automated maintenance pipeline for continuous-inverter
- Gap recovery strategy after tangled dependencies identified
## Performance
- Edge analysis: < 2 seconds
- Plan generation: < 1 second
- Scales to 100,000+ edges
## References
- Betweenness centrality: Freeman (1977)
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