meta-knowledge-base-bootstrap

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.

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tessl review fix ./src/opensquilla/skills/exp/meta-knowledge-base-bootstrap/SKILL.md

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.

Repository: opensquilla/opensquilla
Path: src/opensquilla/skills/exp/meta-knowledge-base-bootstrap/SKILL.md
Commit: 10dee00

Last updated: 9 days ago
First committed: 2 months ago

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