memory-lancedb-pro
Comprehensive guide for maintaining, debugging, and upgrading the memory-lancedb-pro OpenClaw plugin — an enhanced LanceDB-backed long-term memory system with hybrid retrieval (Vector + BM25), cross-encoder reranking, multi-scope isolation, noise filtering, adaptive retrieval, and a management CLI. Use this skill when: (1) developing new features or fixing bugs in memory-lancedb-pro, (2) modifying the retrieval pipeline (vector search, BM25, RRF fusion, reranking, scoring stages), (3) adding or changing embedding providers, (4) updating scope/access control logic, (5) modifying agent tools or CLI commands, (6) troubleshooting memory quality issues (noise, duplicates, low recall), (7) working on the JSONL session distillation pipeline, (8) migrating data between memory backends, or (9) understanding the plugin's architecture to plan enhancements.
Works with
This skill's source license couldn't be confirmed as safe to mirror here, so it isn't inlined. View the full skill directly on its source repository.
View on GitHubMore Debugging skills
diagnosing-bugs
mattpocock/skills
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
explore-code
lllllllama/rigorpilot-skills
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
safe-debug
lllllllama/rigorpilot-skills
Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.
