菜单
Search indexed corpora using semantic (vector) OR full-text (keyword) search via the arc CLI. Use when the user asks to search, find, look up, or query a corpus, collection, knowledge base, codebase, docs, PDFs, or markdown. Covers both conceptual queries and exact-term lookups.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | arc-search |
| description | Search indexed corpora using semantic (vector) OR full-text (keyword) search via the arc CLI. Use when the user asks to search, find, look up, or query a corpus, collection, knowledge base, codebase, docs, PDFs, or markdown. Covers both conceptual queries and exact-term lookups. |
| allowed-tools | Bash(arc:*), Read |
A corpus is dual-indexed: semantic search hits Qdrant, full-text search hits MeiliSearch. Both are first-class — pick the one that matches the query, and use both when unsure.
Use semantic when the query is:
Use text (full-text) when the query is:
When unsure, run BOTH and merge results. Full-text is cheap and often surfaces hits semantic misses (rare tokens, code symbols, exact error messages). Do not default to semantic alone.
arc collection list # show every corpus/collection
arc corpus info MyCorpus # inspect one corpus (both sides)
arc search semantic "QUERY" --corpus NAME [OPTIONS]
Options:
--corpus NAME — repeat for multi-corpus search (--corpus A --corpus B)--limit N — number of results (default small; raise for broad surveys)--offset N — pagination--score-threshold 0.0-1.0 — drop low-confidence hits--filter "key=value" or --filter '{"key":"value"}' — metadata filter--vector-name NAME — pick a specific embedding model (auto-detected otherwise)--json — structured output for parsing-v / --verbose — show scores and metadataarc search text "QUERY" --corpus NAME [OPTIONS]
Options:
--corpus NAME — repeat for multi-corpus search--limit N, --offset N — pagination--filter "key=value" or --filter '{"key":"value"}' — metadata filter--json, -v — same as semanticNote: full-text has no --score-threshold or --vector-name (no embeddings involved).
# Find a function by name — full-text wins
arc search text "parse_frontmatter" --corpus Code
# Conceptual question — semantic wins
arc search semantic "how is the embedding cache invalidated" --corpus Code
# Unknown — run both
arc search semantic "retry backoff" --corpus Docs --limit 5
arc search text "retry backoff" --corpus Docs --limit 5
# Multi-corpus
arc search semantic "auth flow" --corpus Code --corpus Docs
# Filtered (e.g. only python files)
arc search text "TODO" --corpus Code --filter "language=python" --json
The subcommand (semantic | text) MUST come before the query:
arc search semantic "query" --corpus Xarc search "query" --corpus X --semanticarc search --corpus X "query"Dual-index corpus management for combined semantic and full-text search. Use when user mentions corpus, dual indexing, syncing content to both Qdrant and MeiliSearch, checking parity between systems, deleting corpora, or managing content that needs both search types.
Qdrant collection management for semantic search. Use when user mentions managing collections, creating collection, listing collections, verifying collection integrity, exporting or importing collections, or viewing indexed items in Qdrant.
Docker container management for Qdrant and MeiliSearch services. Use when user mentions starting services, stopping services, checking container status, viewing logs, restarting containers, or resetting database services.
MeiliSearch index management for full-text search. Use when user mentions managing indexes, creating index, listing indexes, verifying index health, exporting or importing indexes, or viewing indexed items in MeiliSearch.