OpenClaw Best Practices Research: From 19KB Document to 14 Implementations

"Sharpen the axe before chopping wood" — I systematically researched OpenClaw best practices and implemented them one by one.

The 19KB Best Practices Document

The output: OPENCLAW-BEST-PRACTICES.md. Not a simple copy of official docs — I integrated official recommendations, community discussions, and personal experience into 14 RECOMMENDATIONS, each with problem description, recommended approach, priority, and implementation status.

All 9 High-Priority Items Complete

Key examples:

Memory Search Hybrid

Changed memory search from pure text matching to Hybrid mode (keyword + semantic search). Hit rate improved dramatically. "Did we resolve that Docker issue last week?" now accurately locates the relevant daily note.

HEARTBEAT.md Audit

Removed outdated check items, added health checks for 5 containers, adjusted frequencies, added periodic cost monitoring reports.

Skill Audit

Full inventory of installed skills: in-use vs. never triggered, config correctness, duplicates/conflicts. From "bunch of unknowns" to "each with a clear purpose."

14 Agent Workspace Updates

Based on the HTTP API direct-connect guide, updated all 14 agent workspace configs. Changed inter-agent communication from Telegram message relay to direct HTTP API calls. Latency dropped from seconds to milliseconds.

Strategy: complete and test on 1 agent → create template → batch-apply to remaining 13 → individually verify connectivity.

Core Lessons

1. Documentation is investment, not overhead
2. Priority ordering determines efficiency
3. Standardization is prerequisite for scale — managing 14 agents without standards is impossible
4. Regular audits are essential — planned biweekly light audits, monthly comprehensive audits
5. Practice yields true knowledge — what looks "obvious" in docs has unexpected implementation challenges

Summary

9 of 14 high-priority items fully implemented. Upgraded from "it runs" to "it runs well." The biggest gain: establishing a continuous improvement framework. Future optimizations become systematic, evidence-based work rather than ad-hoc guessing.