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- Add a comprehensive ST knowledge base document: - openclaw/memory/srs-coroutines.md - Add ST-focused developer skill: - openclaw/skills/st-develop/SKILL.md - openclaw/skills/st-develop/scripts/verify.sh - Add KB workflow skills that support ST documentation quality and learning: - openclaw/skills/kb-review/SKILL.md - openclaw/skills/srs-learn/SKILL.md - Update openclaw/skills/srs-support/SKILL.md to use dynamic SRS_ROOT path resolution, improving portability for KB/source loading. --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: chatgpt-codex-connector[bot] <199175422+chatgpt-codex-connector[bot]@users.noreply.github.com>
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2026-02-06 — Daily Log
Commit Convention
- Commit titles in this workspace should start with:
OpenClaw:
Why Build an AI Knowledge Base for SRS
William explained the three layers needed for AI to effectively work on SRS:
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Knowledge base — Existing docs are written for humans, not AI. Without structured memory, AI can read code and docs but miss the why — the background, design thinking, architecture rationale. The knowledge base is built specifically so AI can truly understand SRS.
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Code structure — The codebase needs to be refined so AI can verify each change. Testable, checkable, AI-friendly structure.
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Code taste — AI should follow the style and conventions of the existing code. (Nice to have, not strictly required for SRS.)
Session: Learning SRS Fundamentals
- William started teaching me about SRS
- Covered: what SRS is, publisher/player workflow, protocols, ecosystem tools
- Discussed memory organization — decided on dedicated knowledge files instead of putting everything in MEMORY.md
- Created
memory/srs-overview.mdfor SRS fundamentals
Memory Structure Decision
MEMORY.md→ small, always loaded, high-level pointersmemory/srs-*.md→ detailed SRS knowledge, accessed via memory_searchmemory/YYYY-MM-DD.md→ daily conversation logs