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semantic-search
Configure, build, and verify dagayn embeddings and hybrid semantic search without losing FTS fallback behavior.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Configure, build, and verify dagayn embeddings and hybrid semantic search without losing FTS fallback behavior.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Register repositories, maintain multi-repo graph freshness, and search across repos with dagayn.
Read a Markdown document with full dependency context — query the dagayn graph first, pre-read referenced docs and code by edge type, then read the body.
Author Markdown documents (READMEs, design docs, RFCs) so dagayn extracts correct dependency edges. Four-stage flow — outline & sort, draft & verify, polish, summary.
Evaluate architecture signals through the unified dagayn dispatcher
Systematically debug issues using graph-powered code navigation
Navigate and understand codebase structure using the knowledge graph
| name | semantic-search |
| description | Configure, build, and verify dagayn embeddings and hybrid semantic search without losing FTS fallback behavior. |
| argument-hint | [query] |
Use this when semantic search quality, embedding setup, or hybrid search status matters.
This packaged skill is mode-neutral. dagayn install rewrites this section with
the selected embedding mode so agents can avoid stale or wasteful search advice.
dagayn tool list_graph_stats_tool
search_mode / per-result source:
dagayn tool semantic_search_nodes_tool --arg query='"auth handler"' --arg detail_level='"minimal"'
search_mode="hybrid" means embeddings and FTS were merged. fts_only
is still valid, but semantic recall is lower. embedding_only means the
vector path ran while FTS was unavailable, and keyword_fallback means the
FTS index was absent and only LIKE matching ran.
name / qualified_name hits
even when search_mode is fts_only.source="both"
or source="embedding".rerank_intent="process_pattern"; this
routes to narrative embeddings when they are available.source to explain why a hit ranked: fts, embedding,
both, or keyword.name or qualified_name, use
that qualified_name for query_graph_tool.query_graph_tool(pattern="file_summary") or a relationship query before
drawing conclusions.query_graph_tool; use traverse_graph_tool only for a bounded
neighborhood after the start node is clear.next_action, follow it before widening the
query.build_or_update_graph_tool(local_embedding="bge-m3")embed_graph_toolbuild_or_update_graph_tool(full_rebuild=True, local_embedding="bge-m3") only when explicitly doing embedding-quality or end-to-end maintenance work.
Before any embedding-enabled full rebuild, state the reason and get explicit
confirmation from the user; do not use it for parser, flow, documentation, or
ordinary implementation verification.dagayn build --local-embedding
dagayn update --local-embedding
dagayn tool embed_graph_tool
semantic_search_nodes_tool query and compare result count,
search_mode, rerank_intent, and whether high-value hits now have
source="embedding" or source="both".fts_only is acceptable for exact symbol/name lookup; do not rebuild
embeddings just to find a precise identifier.keyword_fallback means the FTS index is absent; refresh graph/FTS before
drawing conclusions about search quality.provider_mismatch or missing_vectors in embedding health means the query
did not find matching vectors for the selected provider/text mode. Refresh
embeddings only when the task actually needs fuzzy recall.auto or
llama-server), port, and timeout before changing graph data.query_graph_tool, flow_tool,
review_tool, or source reads.