swmm-rag-memorylisted

# SWMM RAG Memory ## What this skill provides - Query-time retrieval over Agentic SWMM audited run memory. - A lightweight keyword/tag retriever that works without embeddings or a vector database. - A local hybrid retriever that combines keyword matches, deterministic SWMM tags, metadata weighting, and hashed token/character n-gram embeddings. - RAG context packs that can be passed to Codex, OpenClaw, Hermes, or another LLM. - Source citations for each retrieved memory item, including run id, project key, source file, failure patterns, diagnostics, and matched terms. - Retrieval-grounded `failure_advice.{json,md}` for failed or warning runs, without modifying model files. - Explicit `resolution_memory.json` for human-reviewed and benchmark-verified repairs. - Obsidian-compatible Markdown output for saved retrieval notes. This skill reads existing audit and modeling-memory artifacts. It does not run SWMM, modify model inputs, rewrite skills, or claim that retrieved memory proves a modeling conclusion. ## Relationship to `swmm-modeling-memory` `swmm-modeling-memory` summarizes audited runs after experiments have been recorded. `swmm-rag-memory` retrieves the most relevant historical memory for a current question. The intended loop is: 1. Run SWMM or attempt a workflow. 2. Audit the run. 3. Refresh `swmm-modeling-memory`. 4. Ask a current modeling question. 5. Retrieve relevant historical memory with `swmm-rag-memory`. 6. Answer with explicit source boundaries and citati