hermes-skills/ltx23-kb/SKILL.md

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

name	description	version	author	license	platforms	metadata
ltx23-kb	Record and retrieve LTX 2.3 knowledge in the ai_brain_kb Qdrant collection (the brain), tagged ltx23.	2.0.0	Hermes Agent	MIT	linux	hermes tags related_skills qdrant knowledge-base ltx video ltx23 ai-brain-kb ai-brain-kb qdrant-collection-management ltx-video-pipeline

LTX 2.3 Knowledge — stored in ai_brain_kb

This skill no longer owns a separate collection. LTX 2.3 knowledge lives in the one brain, ai_brain_kb, tagged ltx23. The former ltx23_kb collection was never created in Qdrant (verified 2026-08-09: GET /collections/ltx23_kb → Collection 'ltx23_kb' doesn't exist!), so there is nothing to migrate and no historical content is lost by this change.

mcp__better_qdrant__add_documents is FORBIDDEN against ai_brain_kb — it does a bare-vector upsert with no bm25 sparse slot and no payload (doc_type/title/tags/trust/host), so the point is invisible to typed and keyword search. Every write goes through python3 /home/n8n/bin/ai_brain_kb.py add ... and nothing else. Reads against ai_brain_kb are unrestricted.

Storage Location

Setting	Value
Qdrant	http://10.0.0.22:6333
Collection	ai_brain_kb (the brain — one collection, no exceptions)
Dimensions	1024
Distance	Cosine
Embedding	Ollama snowflake-arctic-embed2 on mini, 10.0.0.30 (the fleet's only embedder — keep load minimal and batched)
Write interface	python3 /home/n8n/bin/ai_brain_kb.py add — the ONLY correct one
Read interface	ai_brain_kb.py search / list / facet, or mcp__better_qdrant__search (read-only)
Scoping convention	--tool ltx plus --tags "ltx23,..."

What Goes Here

Everything LTX 2.3 related that should be searchable across sessions:

• Known issues — bugs, artifacts, model limitations, workarounds (--type issue)
• Resolutions / fixes — patches, config changes that solved problems (--type finding)
• Prompt suggestions — effective prompt patterns, structures, word limits (--type prompt)
• New modules — community modules, extensions, LoRAs, pipelines (--type model / --type tool)
• Official source tracking — Lightricks GitHub releases, changelogs, docs (--type research)
• Community findings — HuggingFace discussions, Reddit, Discord insights (--type finding)
• Render settings — proven configs (steps, guidance, resolution, CFG) (--type setting)
• Pipeline decisions — architecture choices, model chain wiring (--type decision)

Commands

Add knowledge (the only write path)

python3 /home/n8n/bin/ai_brain_kb.py add \
  --type finding \
  --title "LTX 2.3 — <short, specific title>" \
  --file /absolute/path/to/findings.md \
  --tool ltx \
  --stage t2v \
  --trust official \
  --tags "ltx23,ltx,<topic>" \
  --importance 0.6

• --content "..." instead of --file for short entries.
• --file chunks large files itself — no MCP 120 s timeout, no size ceiling.
• Valid --type values: research|finding|decision|issue|workflow|technique|model|setting|tool|asset|prompt|host.
• --url for the primary source, --path for an on-disk or NAS artifact.

Search

# hybrid (dense + BM25) — the default, best for natural-language questions
python3 /home/n8n/bin/ai_brain_kb.py search --query "LTX 2.3 temporal consistency fix" --limit 10

# scope to LTX content
python3 /home/n8n/bin/ai_brain_kb.py search --query "prompt ceiling" --tag ltx23

# pure keyword / exact-string (filenames, error strings, model names) — no embedding call
python3 /home/n8n/bin/ai_brain_kb.py search --query "ltxv-097-dev-fp8.safetensors" --mode bm25

# by type
python3 /home/n8n/bin/ai_brain_kb.py search --query "artifacts" --type issue --tag ltx23

list, facet, recent and --mode bm25 do not call the embedder — prefer them when you only need to enumerate or keyword-match (mini CARE rule).

Delete

python3 /home/n8n/bin/ai_brain_kb.py delete --doc-id <doc_id> --yes

Workflow: Save Research Findings

After researching LTX 2.3 (new release, bug fix, community finding):

1. Dedup first — search --query "<topic>" --tag ltx23. ≥0.85 skip · 0.70–0.84 add only if meaningfully new · <0.70 always add.
2. Write findings to a markdown file in ~/workspace/general/ or a dedicated LTX workspace.
3. Add via the helper — ai_brain_kb.py add --type <t> --tool ltx --tags "ltx23,..." --file <path>.
4. Confirm — the helper prints the doc_id and chunk count; report both.
5. Verify — re-find it with search --mode bm25 --query "<distinctive phrase>". A hit only on hybrid and not on bm25 is the bare-upsert signature and means the write was wrong.

Workflow: Research an LTX Issue

1. Search the brain first — search --query "<issue>" --tag ltx23, then without the tag for broader AI-video context.
2. If gaps found — dispatch web search for official sources (Lightricks GitHub, HuggingFace).
3. Save results — add the research output with the helper, tagged ltx23.
4. Proceed — now you have full context.

Official Sources

Primary sources to monitor (for cron-based updates):

• GitHub: https://github.com/Lightricks/LTX-Video — official repo, releases, issues
• HuggingFace: https://huggingface.co/Lightricks/LTX-Video — model weights, model cards
• HuggingFace Community: https://huggingface.co/Lightricks — organization page

Pitfalls

• Never mcp__better_qdrant__add_documents — bare-vector upsert; the point becomes invisible to typed and keyword search. The helper is the only correct write interface.
• Per-document delete EXISTS — ai_brain_kb.py delete --doc-id <id> --yes. Never mcp__better_qdrant__delete_collection(collection="ai_brain_kb") — that destroys the entire brain, not one document.
• File paths must be absolute.
• Large files chunk automatically — the helper handles chunking; no need to pre-split.
• Embedding model must be running — Ollama with snowflake-arctic-embed2 on mini (10.0.0.30), the fleet's only embedder. Keep writes batched.
• Scope with tags, not collections — ltx23 tag + --tool ltx. Do not create a new collection for a topic; the brain is one collection.
• Search is hybrid — dense + BM25. Use --mode bm25 for exact strings, natural language for concepts.
• Prompt ceiling — LTX 2.3 official limit is 200 words (Lightricks GitHub README). Community extends to 150-300 words for 10s clips. One main action per 2-3 seconds of video.

Related Skills

• ai-brain-kb — the authoritative skill for the brain; read it first
• qdrant-collection-management — collection-level operations for OTHER collections
• ltx-video-pipeline — LTX Video pipeline on 10.0.0.202
• local-ai-media-generation — Plan and evaluate local AI media generation pipelines