Agent skill
rule-optimize
Workflow for modifying and benchmarking detection rules
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/rule-optimize-ahrav-gossip-rs
SKILL.md
Rule Optimization Workflow
Use after modifying rules in default_rules.yaml (loaded by src/rules/).
Checklist
- Run
cargo testto verify no regressions - Build release:
RUSTFLAGS="-C target-cpu=native" cargo build --release - Benchmark against test repos:
bash
./target/release/scanner-rs ../linux ../gitleaks ../tigerbeetle ../trufflehog - Compare throughput/findings against baseline
- Document anchor/keyword choice if non-obvious (add inline comment)
Pattern Guidelines
When adding or modifying rules:
Anchors
- Prefer structured prefixes (
sgp_,hvs.,AKIA) over service name keywords - Avoid generic patterns like
[a-fA-F0-9]{40}that match git SHAs - Add inline comments explaining non-obvious anchor/keyword choices
Performance
- Test rule isolation:
cargo bench --bench rule_isolation -- <rule_id> - Check for backtracking: avoid
.*followed by greedy quantifiers - Prefer character classes over alternation when possible
Validation
- Ensure validators don't make network calls in hot paths
- Use entropy checks for high-entropy secrets
- Add checksum validation where applicable (AWS keys, etc.)
Baseline Comparison
Before making changes, capture baseline:
# Run 3x and record median throughput
for i in 1 2 3; do
./target/release/scanner-rs ../linux 2>&1 | tail -1
done
After changes, compare:
# Calculate % change
# Acceptable: <2% regression
# Investigate: 2-5% regression
# Block: >5% regression without justification
Related Skills
bench-compare- Criterion benchmark comparisonperf-regression- Full performance regression workflowtest-strategy- Choose testing approach for rule changes
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