wiki-ingest
Wiki Ingest automates the process of reading source documents and weaving their content into your wiki. It identifies key entities, claims, and relationships, then creates or updates wiki pages while maintaining bidirectional cross-references and a source index. Each ingest operation is logged for full traceability.
Wiki Ingest processes source documents and integrates their knowledge into your wiki, updating existing pages and creating new ones as needed.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-02
Wiki Ingest processes source documents and integrates their knowledge into your wiki, updating existing pages and creating new ones as needed. Wiki Ingest automates the process of reading source documents and weaving their content into your wiki. It identifies key entities, claims, and relationships, then creates or updates wiki pages while maintaining bidirectional cross-references and a source index. Each ingest operation is logged for full traceability.
Use it when
- Wiki Ingest extracts entities and relationships from your new source, then automatically creates or updates relevant wiki pages.
- Yes.
Verify before relying
Read SKILL.md below before installing (1 file). Open directory: indexed for reading, not audited.
Install
open-gitagent/opengap/wiki-ingest · repository language: TypeScript
Open directory. Skills are indexed for reading, not audited. Review a skill's body before installing it.
Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I ingest documents into wiki with Wiki Ingest?
Wiki Ingest automates the process of reading source documents and weaving their content into your wiki. You provide raw source material—PDFs, text files, or web content—and Wiki Ingest identifies key entities, claims, and relationships, then creates or updates wiki pages while maintaining bidirectional cross-references and a source index for full traceability.
What happens when I add a source to my knowledge base?
Wiki Ingest extracts entities and relationships from your new source, then automatically creates or updates relevant wiki pages. The skill maintains a source index tracking where each piece of information originated, establishes cross-references between related pages, and logs the entire ingestion operation so you can audit what changed and why.
Can Wiki Ingest batch ingest multiple documents at once?
Yes. Wiki Ingest supports batch ingestion workflows, allowing you to process multiple documents in a single operation. The skill handles document processing, entity extraction, and wiki page creation or updates across all sources, then consolidates the results with unified cross-referencing and provenance tracking.
How does Wiki Ingest organize and catalog knowledge sources?
Wiki Ingest organizes sources with metadata tagging and maintains a searchable catalog within your knowledge base. Each source is indexed with its ingestion date, content summary, and extracted entities. This metadata layer lets you filter, search, and manage your sources while Wiki Ingest tracks all cross-references and maintains source provenance automatically.
Does Wiki Ingest automate wiki updates from new sources?
Yes. Wiki Ingest automates wiki page creation and updates whenever new sources are added to your knowledge base. The skill detects relevant existing pages, merges new information while preserving prior content, updates cross-references, and logs all changes. This eliminates manual wiki maintenance and keeps your knowledge base current with minimal effort.
How does Wiki Ingest maintain wiki cross-references?
Wiki Ingest establishes and maintains bidirectional cross-references between related wiki pages as it processes sources. When entities or relationships appear across multiple documents, the skill links them together and updates those links if source material changes. A source index tracks provenance, so you always know which document supports each claim or connection.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
Wiki Ingest
Process a new source document and integrate its knowledge into the wiki.
Workflow
Step 1: Read the source
Read the full source document from knowledge/. Identify:
- Key entities (people, organizations, concepts, technologies)
- Main claims and findings
- Relationships between entities
- Data points and statistics
- Contradictions with or confirmations of existing knowledge
Step 2: Discuss with the user
Before writing, share a brief summary of key takeaways. Ask: - What aspects to emphasize? - Any entities or concepts to prioritize? - Should this update existing pages or create new ones?
Step 3: Update the wiki
For each significant entity or concept found in the source:
- Check if a wiki page exists — read
memory/wiki/index.md - If page exists — read it, integrate new information, update the "Sources" section, update
(truncated - see the full file via the links below)
File tree — 1 file
examples/llm-wiki/skills/wiki-ingest/SKILL.md
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Related skills
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Transform loose content into a living knowledge base by ingesting articles, documents, or notes into organized wiki pages. The skill automatically extracts concepts, products, and patterns, creates interconnected pages with cross-references, and maintains a searchable index—all while preventing duplicates and keeping your knowledge network coherent.
Second Brain Ingest processes raw source documents into organized wiki pages, automatically identifying and linking entities, concepts, and related topics. It guides you through extracting key takeaways, creating source summaries, and updating your knowledge graph with new information while flagging contradictions with existing content.
LLM Wiki Bootstrap helps you build and operate a personal knowledge base structured around immutable raw sources, a curated wiki layer, and a SCHEMA.md operating contract. Set up preferences, ingest sources, query your wiki, or configure full-text search—the skill handles scaffolding, content compilation, and consistency checks.
Knowledge Retrieval performs semantic search across your ingested document collection using Retrieval-Augmented Generation. It ranks results by relevance and returns text passages with citation metadata, making it ideal for finding domain-specific information in PDFs, reports, and technical documentation.
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