Global Rank · of 601 Skills
knowledge-base-manager AI Agent Skill
View Source: oakoss/agent-skills
MediumInstallation
npx skills add oakoss/agent-skills --skill knowledge-base-manager 32
Installs
Knowledge Base Manager
Overview
Provides a structured methodology for selecting, designing, and governing knowledge bases. Covers architecture decisions (document-based vs entity-based vs hybrid), content curation, quality metrics, versioning strategies, and maintenance governance. Use when choosing a KB architecture, establishing curation workflows, or building governance processes for organizational knowledge.
When NOT to use: Static documentation suffices, fewer than 50 FAQ items cover all questions, or no maintenance resources are available. For implementing retrieval pipelines (chunking, embeddings, vector stores), use the rag-implementer skill. For implementing knowledge graphs (ontology, entity extraction, graph databases), use the knowledge-graph-builder skill.
Quick Reference
| Aspect | Options | Key Considerations |
|---|---|---|
| Architecture | Document-based (RAG), Entity-based (Graph), Hybrid | Match to query patterns; start simple, add complexity when needed |
| Document-based | Vector DB (Pinecone, Weaviate, pgvector) | Best for docs, FAQs, manuals; semantic search; easy to add content |
| Entity-based | Graph DB (Neo4j, ArangoDB) | Best for org charts, catalogs, networks; relationship traversal |
| Hybrid | Both + linking layer | Enterprise, medical, legal; combined queries; highest complexity |
| When to skip KB | Static docs, <50 FAQ items | No maintenance resources, information never changes |
| Implementation | 6 phases | Audit, Curation, Storage, Quality, Versioning, Governance |
| Accuracy target | >90% on test questions | Create 100+ test questions with known correct answers |
| Coverage target | >80% questions answerable | Validate against real user queries continuously |
| Freshness target | <30 days average age | Automated freshness monitoring + scheduled updates |
| Consistency target | >95% conflict-free | Deduplication + single source of truth |
| Query latency | <100ms median | Caching and optimization for common access patterns |
| Storage tech | pgvector, Pinecone, Weaviate, Chroma | pgvector for existing Postgres; Pinecone for managed scale |
| Index types | HNSW, IVFFlat | HNSW for recall; IVFFlat for frequently rebuilt indexes |
| Ingestion pipeline | Load, clean, chunk, embed, store | Chunk at semantic boundaries; 512 tokens max; 10-15% overlap |
| Deduplication | Content hashing, semantic similarity | Hash for exact dupes; cosine similarity >0.95 for semantic dupes |
| Quality testing | Recall@K, MRR, accuracy sampling | 100+ test questions; measure recall@10 >0.8 and MRR >0.7 |
| Drift detection | Embedding distribution monitoring | Track mean shift; alert when >0.1 threshold |
| Versioning | Snapshot, Event-sourced, Git-style | Snapshot for simple; event-sourced for audit; git-style for teams |
| Maintenance | Daily, Weekly, Monthly, Quarterly | Establish schedule from day 1; monitor errors and user feedback |
Common Mistakes
| Mistake | Correct Pattern |
|---|---|
| Ingesting raw data without curation or normalization | Curate, clean, and deduplicate before ingesting; quality over quantity |
| Skipping version control for KB content | Implement versioning from day one with rollback and audit trail |
| Building a KB without validating against user questions | Start with user research and test against real queries for >90% accuracy |
| Choosing hybrid architecture when document-based suffices | Match architecture to actual query patterns; start simple, add complexity when needed |
| Launching without freshness monitoring or update schedules | Set up automated freshness checks and scheduled content reviews |
| No provenance tracking on knowledge entries | Always track source URL, timestamp, author, and confidence score |
| Duplicate information across sources | Establish single source of truth; merge similar entries with conflict resolution rules |
| Perfectionism delaying launch | Launch at 80% coverage and iterate based on real usage data |
Delegation
- Audit existing knowledge sources and classify content types: Use
Exploreagent to inventory documents, assess quality, and identify gaps - Implement end-to-end KB pipeline with storage and retrieval: Use
Taskagent to deploy database, configure search, and run quality checks - Design KB architecture and governance model: Use
Planagent to select between document-based, entity-based, or hybrid approaches
For implementing document retrieval pipelines (chunking, embeddings, vector stores, hybrid search), use the
rag-implementerskill. For implementing knowledge graphs (ontology design, entity extraction, graph databases), use theknowledge-graph-builderskill.
References
- Architecture and Types -- KB types, decision framework, knowledge classification
- Curation and Ingestion -- extraction, cleaning, deduplication, provenance tracking
- Storage and Retrieval -- database selection, interfaces, technology stacks
- Quality Control -- metrics, validation strategies, continuous monitoring
- Versioning -- snapshot, event-sourced, and git-style approaches
- Governance -- maintenance schedules, roles, change processes
Installs
Security Audit
View Source
oakoss/agent-skills
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How to use this skill
Install knowledge-base-manager by running npx skills add oakoss/agent-skills --skill knowledge-base-manager in your project directory. Run the install command above in your project directory. The skill file will be downloaded from GitHub and placed in your project.
No configuration needed. Your AI agent (Claude Code, Cursor, Windsurf, etc.) automatically detects installed skills and uses them as context when generating code.
The skill enhances your agent's understanding of knowledge-base-manager, helping it follow established patterns, avoid common mistakes, and produce production-ready output.
What you get
Skills are plain-text instruction files — not executable code. They encode expert knowledge about frameworks, languages, or tools that your AI agent reads to improve its output. This means zero runtime overhead, no dependency conflicts, and full transparency: you can read and review every instruction before installing.
Compatibility
This skill works with any AI coding agent that supports the skills.sh format, including Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider, and other tools that read project-level context files. Skills are framework-agnostic at the transport level — the content inside determines which language or framework it applies to.
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