hermes-skills/ai-brain-kb/SKILL.md

name, description, version, author, license, platforms, metadata

name	description	version	author	license	platforms	metadata
ai-brain-kb	Manage the ai_brain_kb Qdrant collection — add documents, search, and remove. Central knowledge base for all AI/ML learnings, pipeline details, and research.	1.0.0	Hermes Agent	MIT	linux	hermes tags related_skills qdrant knowledge-base ai-brain search rag save-q-memory qdrant-collection-management

AI Brain KB — Qdrant Knowledge Base Manager

Manage the ai_brain_kb Qdrant collection — the central knowledge base for all AI/ML learnings, pipeline details, research, and decisions.

Storage Location

Setting	Value
Qdrant	http://10.0.0.22:6333
Collection	ai_brain_kb
Embedding	Ollama (snowflake-arctic-embed2)
MCP Tool	mcp__better_qdrant__*

What Goes Here

Everything AI/ML related that should be searchable across sessions:

• Pipeline plans, state, and architecture decisions
• Story prompts and scene descriptions
• Research results (deep research, better-search outputs)
• Model configurations, LoRA chains, render settings
• Bug diagnoses and fixes
• Stock material research
• Prompt engineering guides
• Hardware/infrastructure details for AI workloads

Commands

Add Documents

Add a file (markdown, text, JSON) to the knowledge base. The file is chunked and embedded automatically.

mcp__better_qdrant__add_documents(
  collection="ai_brain_kb",
  embeddingService="ollama",
  filePath="/absolute/path/to/file.md"
)

Chunking: Default 500 chars with 50 char overlap. Works for .md, .txt, .json, .py files.

After adding: Confirm chunk count to user.

Search

Semantic search across all knowledge in the collection.

mcp__better_qdrant__search(
  collection="ai_brain_kb",
  embeddingService="ollama",
  query="your search query",
  limit=10
)

Tips:

• Use natural language queries — "LTX artifact causes" not "ltx artifact"
• Results include score, title, URL (if applicable), summary, and key claims
• Higher limit = more context but more tokens

Remove Documents

Delete individual documents by their source path (if tracked) or delete the entire collection and rebuild.

Remove entire collection (nuclear option):

mcp__better_qdrant__delete_collection(collection="ai_brain_kb")

Note: There is no per-document delete in the current MCP tool. To remove specific content, delete the collection and re-add only the files you want to keep.

List All Collections

See what collections exist on the Qdrant instance:

mcp__better_qdrant__list_collections()

Workflow: Save Session Learnings

After a significant session (new research, bug fix, pipeline change):

1. Identify new/changed files — what markdown docs were created or updated?
2. Add to ai_brain_kb — use add_documents for each file
3. Confirm — report chunk counts to user
4. Clean up — if old topic-specific collections exist, merge and delete them

Workflow: Research a Topic

When starting work on an AI/ML topic:

1. Search ai_brain_kb first — what do we already know?
2. If gaps found — dispatch deep-research or better-search
3. Save results — add the research output file to ai_brain_kb
4. Proceed — now you have full context

Pitfalls

• File paths must be absolute — the MCP tool resolves from the Hermes host filesystem.
• Large files chunk automatically — 500 char chunks. Very large files (100K+ chars) may produce many chunks; consider summarizing first.
• No per-document delete — the MCP tool only supports collection-level delete. Plan your adds accordingly.
• Embedding model must be running — Ollama with snowflake-arctic-embed2 must be available on the Qdrant host (10.0.0.22:11434).
• Collection name is exact — ai_brain_kb, not ai-brain-kb or ai_brain.
• Search is semantic, not keyword — phrase queries naturally. "How to fix LTX artifacts" works better than "ltx artifact fix".
• Don't create topic-specific collections — everything goes into ai_brain_kb. The user's rule: one brain, one collection.

Related Skills

• save-q-memory — Manual save to the memories collection (personal/behavioral memory, not knowledge base)
• qdrant-collection-management — Collection-level operations: consolidation, migration, dedup, registry