hns-lsel-curator
FeaturedLocal Self-Evolution Loop (LSEL) curator — the CLUSTER + drain engine for the GOOS-local PROPOSE→APPLY seam closure (SPEC-LSEL-LOCAL-EVOLUTION-001). Companion-offset drain of .moai/lessons-inbox.jsonl with a drain-side severity filter that drops the ~65% Bash-timeout/sandbox noise, event_key clustering with a frequency gate, and a Generative-Agents-style 1-10 importance score. Candidates stage at .moai/state/lsel/clusters.json. M1 = drain only (NO PROPOSE, NO APPLY, NO memory/ writes).
Install
Quality Score: 99/100
Skill Content
Details
- Author
- modu-ai
- Repository
- modu-ai/moai-adk
- Created
- 11 months ago
- Last Updated
- today
- Language
- Go
- License
- Apache-2.0
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
hns-lsel-applier
Local Self-Evolution Loop (LSEL) APPLY engine — the playback-only consumer of approved decision.json records that drives `.moai/hooks/lsel-apply.sh` for the GOOS-local PROPOSE→APPLY seam closure (SPEC-LSEL-LOCAL-EVOLUTION-001 M3). Reads an approved decision.json, validates the target against the frozen allowlist (.claude/lsel/frozen-allowlist.json), mechanically refuses execution-meta targets lacking a synchronous-approval marker, applies the referenced diff.patch, appends an apply-ledger.jsonl row, and commits one lsel-<proposal-id>-tagged Conventional Commit on the feature branch. M3 scope: APPLY bypass closure only (the frozen Go applier stays frozen — its write-flag at internal/harness/applier.go:22 stays false; REQ-LSEL-003).
sota-llm-engineering
State-of-the-art LLM application engineering rules (mid-2026 baseline) for BUILDING and AUDITING LLM-powered features. Claude should use this skill whenever it is building, modifying, or reviewing anything that calls a language model — chat features, RAG pipelines, agents and tool use, structured extraction, classification, summarization, embeddings/vector search, evals and regression gates, prompt or context engineering, model selection/routing, fine-tuning decisions, or LLM cost/latency/observability work. Trigger keywords: LLM, AI feature, prompt, system prompt, context window, RAG, retrieval, embeddings, vector DB, rerank, chunking, agent, tool use, MCP, multi-agent, evals, golden set, LLM-as-judge, fine-tuning, model selection, routing, structured output, JSON schema, prompt caching, token budget, hallucination, grounding. Covers build-quality only — for prompt-injection/agent-security use sota-code-security rules/08 and sota-sandboxing rules/05.
drain
Run the on-demand drain pass over every spec bundle's Gate deferral entries: evaluate structured GATE(when:) conditions, surface date and free-text gates, report malformed ones, and surface the observations log's unmined state. Read-only; nothing is auto-resolved or auto-dropped.