| name | meta-knowledge-base-bootstrap |
| description | Bootstrap a domain knowledge base from a single seed (URL / PDF path / git repo / free-text topic): classify source → ingest with the right tool → persist to memory + xlsx index. |
| kind | meta |
| meta_priority | 40 |
| always | false |
| triggers | ["搭建知识库","knowledge base","kb 启动","bootstrap kb"] |
| provenance | {"origin":"opensquilla-original","license":"Apache-2.0"} |
| composition | {"steps":[{"id":"classify","kind":"llm_classify","output_choices":"[Truncated]","with":"[Truncated]"},{"id":"ingest","kind":"skill_exec","skill":"multi-search-engine","depends_on":"[Truncated]"},{"id":"memorize","kind":"tool_call","tool":"memory_save","depends_on":"[Truncated]","tool_args":"[Truncated]"},{"id":"index","skill":"xlsx","depends_on":"[Truncated]","with":"[Truncated]"}]} |
Knowledge Base Bootstrap (Meta-Skill)
Seed a domain knowledge base in one turn. The pipeline classifies the seed
source type (URL / PDF / GIT / TEXT) and ingests it via the
multi-search-engine skill, then persists the report and produces an
index.
| step | kind | skill | what it does |
|---|
| classify | llm_classify | — | label the seed as one of URL / PDF / GIT / TEXT |
| ingest | skill_exec | multi-search-engine | run a DuckDuckGo search (JSON to stdout) |
| memorize | tool_call | — (memory_save) | append the ingestion summary to memory |
| index | agent | xlsx | write kb-index.xlsx with the result table |
The classifier is currently informational only — the ingest step always
calls multi-search-engine. A previous design routed PDF → pdf-toolkit
and GIT → github, but those branches were dropped when the DSL moved to
skill_exec. A follow-up will reintroduce per-classification routing once
the corresponding bundled skills also expose entrypoint: manifests.
Fallback
If the meta-flow fails: run the classifier prompt manually, then invoke
the appropriate ingestion skill, then memory_save the result, then
create the xlsx index with openpyxl.
