Performance Baseline and Tuning

The skrills MCP server has minimal overhead. Validation, analysis, and sync operations are designed to be efficient.

Expected Performance

• Skill discovery: Initial skill directory scan is cached to avoid repeated filesystem access
• Validation: Processes skills in parallel where possible
• Analysis: Token counting is approximate but fast
• Sync: Byte-for-byte file copy is efficient; uses content hashing to skip unchanged files

These figures are from measurements on an M1 Pro system with a typical skill set.

Tuning Recommendations

Cache TTL

Configure SKRILLS_CACHE_TTL_MS or cache_ttl_ms in the manifest to balance freshness with performance:

# Longer TTL for stable skill sets
export SKRILLS_CACHE_TTL_MS=300000  # 5 minutes

Validation Performance

Use filtering options to reduce work:

# Only check for errors
skrills validate --errors-only

# Target specific directory
skrills validate --skill-dir ~/my-skills

Analysis Performance

Filter to focus on relevant skills:

# Only analyze large skills
skrills analyze --min-tokens 1000

When to Investigate

• If validation is slow, check if skill directories contain many files
• If sync is slow, use sync-status to identify large change sets
• If startup is slow, increase cache TTL or reduce number of skill directories