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tools-update-cron: sync 2026-08-16 — 16 skill(s) updated

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Hermes Agent
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---
name: ltx23-kb
description: "Manage the ltx23_kb Qdrant collection for LTX 2.3 knowledge."
version: 1.0.0
description: "Record and retrieve LTX 2.3 knowledge in the ai_brain_kb Qdrant collection (the brain), tagged ltx23."
version: 2.0.0
author: Hermes Agent
license: MIT
platforms: [linux]
metadata:
hermes:
tags: [qdrant, knowledge-base, ltx, video, ltx23]
tags: [qdrant, knowledge-base, ltx, video, ltx23, ai-brain-kb]
related_skills: [ai-brain-kb, qdrant-collection-management, ltx-video-pipeline]
---
# LTX 2.3 Knowledge Base — Qdrant Collection Manager
# LTX 2.3 Knowledge — stored in `ai_brain_kb`
Manage the `ltx23_kb` Qdrant collection — the dedicated knowledge base for LTX 2.3 video generation. Tracks known issues, resolutions, prompt suggestions, new modules, official source updates, and community findings.
> **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 historical content is lost by this change.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 | `ltx23_kb` |
| Collection | `ai_brain_kb` (the brain — one collection, no exceptions) |
| Dimensions | 1024 |
| Distance | Cosine |
| Embedding | Ollama (snowflake-arctic-embed2) |
| MCP Tool | `mcp__better_qdrant__*` |
| 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
- **Resolutions** — fixes, patches, config changes that solved problems
- **Prompt suggestions** — effective prompt patterns, structures, word limits
- **New modules** — community modules, extensions, LoRAs, pipelines
- **Official source tracking** — Lightricks GitHub releases, changelogs, docs
- **Community findings** — HuggingFace discussions, Reddit, Discord insights
- **Render settings** — proven configs (steps, guidance, resolution, CFG)
- **Pipeline decisions** — architecture choices, model chain wiring
- **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 Documents
### Add knowledge (the only write path)
Add a file (markdown, text, JSON) to the knowledge base. The file is chunked and embedded automatically.
```
mcp__better_qdrant__add_documents(
collection="ltx23_kb",
embeddingService="ollama",
filePath="/absolute/path/to/file.md"
)
```bash
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
```
**Chunking:** Default 500 chars with 50 char overlap. Works for .md, .txt, .json, .py files.
- `--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
Semantic search across all LTX 2.3 knowledge.
```bash
# 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
```
mcp__better_qdrant__search(
collection="ltx23_kb",
embeddingService="ollama",
query="your search query",
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
```
**Tips:**
- Use natural language queries — "LTX 2.3 temporal consistency fix" not "ltx artifact"
- Results include score, title, summary, and key claims
- Higher limit = more context but more tokens
`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).
### List All Collections
### Delete
```
mcp__better_qdrant__list_collections()
```bash
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. **Write findings to a markdown file** in `~/workspace/general/` or a dedicated LTX workspace
2. **Add to ltx23_kb** — use `add_documents` for the file
3. **Confirm** — report chunk count to user
4. **Cross-reference** — search `ai_brain_kb` and `local-ai-video-research` for related context
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
When troubleshooting or exploring LTX 2.3:
1. **Search ltx23_kb first** — what do we already know?
2. **Search ai_brain_kb** — broader AI video context
3. **If gaps found** — dispatch web search for official sources (Lightricks GitHub, HuggingFace)
4. **Save results** — add the research output to ltx23_kb
5. **Proceed** — now you have full context
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
@@ -108,18 +128,25 @@ Primary sources to monitor (for cron-based updates):
## 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 at 10.0.0.30:11434 (mini).
- **Collection name is exact** — `ltx23_kb`, not `ltx-23-kb` or `ltx23`.
- **Search is semantic, not keyword** — phrase queries naturally. "How to fix LTX temporal artifacts" works better than "ltx artifact fix".
- **Don't mix with ai_brain_kb** — `ltx23_kb` is scoped to LTX 2.3 specifically. Broader AI video knowledge goes to `ai_brain_kb`.
- **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.
- **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` — Central AI/ML knowledge base (broader scope)
- `qdrant-collection-management` — Collection-level operations: consolidation, migration, dedup, registry
- `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