hermes-skills/save/SKILL.md

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

name	description	version	author	license	platforms	metadata
save	Use when user types 'save'. Write to Brain (Cognee) and/or Vault (Qdrant ai_vault_kb).	1.3.0	Hermes Agent	MIT	linux	hermes tags related_skills memory brain vault cognee qdrant save cognee-brain ai-vault-kb

Save — write to Brain and/or Vault

Triggered when the user types "save". Route the current session's content to the right store and write it.

Routing

• Brain (Cognee) = durable facts, decisions, preferences, relationships, status changes, corrections. Short atomic statements with time context.
• Vault (Qdrant ai_vault_kb) = longer context, notes, research, summaries, excerpts — for semantic "find similar" recall.
• Both = when something is a clean fact AND rich context: clean version to Brain, fuller version to Vault.
• Be conservative. Only store what's worth remembering. Don't write every message.

---

BRAIN — Cognee (Kuzu + LanceDB on brain 10.0.0.23)

The brain is Cognee, reachable via the auto-discovered MCP tools mcp__cognee__* (no CLI needed). Endpoints: API http://10.0.0.23:8080, MCP http://10.0.0.23:8001/mcp.

Write (permanent memory)

Use the mcp__cognee__remember tool. ALWAYS pass dataset_name="homelab-stack".

• data = the text to store (atomic facts, one per statement where possible).
• Omit session_id — that makes it permanent memory (add + cognify, builds the graph).
• remember returns status: completed synchronously — the graph is built, no polling.

Read

Use the mcp__cognee__recall tool. query (required), datasets="homelab-stack", optional search_type (HYBRID_COMPLETION default; CHUNKS for LLM-free retrieval).

Write rules

• One dataset: homelab-stack. Never create a second dataset.
• No group_id, no triplet, no temporal supersession. Cognee uses datasets. To correct a fact, remember the corrected statement; Cognee merges entities by name.
• Don't re-ingest the same content twice — remember is not idempotent.
• Embed dim is 1024 (qwen3-embedding:0.6b on mini) — never let it default to 3072.

Verify the write

• recall a distinctive phrase from the content; a correct answer proves the graph has it.
• docker logs cognee-backend should show zero api.openai.com / ProviderConfigMismatch hits.

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VAULT — Qdrant ai_vault_kb

CLI: python3 /home/n8n/bin/ai_vault_kb.py (zero deps, urllib only). Qdrant http://10.0.0.22:6333, collection ai_vault_kb. Embeddings: snowflake-arctic-embed2 on 10.0.0.30:11434.

Write (the ONLY sanctioned path)

python3 /home/n8n/bin/ai_vault_kb.py add --type <t> --title "..." --content "..." [flags]

Long documents: --file /abs/path/report.md instead of --content (auto-chunked, one shared doc_id). --json prints {"doc_id":…, "chunks":N}.

Flag	Meaning
--type (required)	tool setting workflow host model technique issue decision research asset prompt finding
--title (required)	headline a future search reads
--content / --file	body text, or a file to chunk
--stage	story script character keyframe t2v i2v upscale interpolate tts lipsync music assembly publish infra
--tool --host --path --url --version	provenance; --host is the box the fact is about
--status	active candidate deprecated broken planned (default active)
--trust	official github community social (default official)
--tags	comma-separated; exact-match keyword index — put slug, filename, error code here
--importance	0.0–1.0
--doc-id	append more chunks to an existing document

Unknown vocabulary values warn but are accepted — the schema is faceted, not strict.

Dedup-first (mandatory before every write)

python3 /home/n8n/bin/ai_vault_kb.py search --query "<the thing you are about to save>"

• score ≥ 0.85 — already recorded, skip
• 0.70–0.84 — add only if meaningfully new
• < 0.70 — always add

Host records

--type host, one stable --doc-id per box. Re-add with the same --doc-id to update/append a dated chunk.

Verify the write landed (both legs)

D=<doc_id from --json>
python3 /home/n8n/bin/ai_vault_kb.py search --query "<distinctive phrase>" --mode bm25 --doc-id $D
python3 /home/n8n/bin/ai_vault_kb.py list --type <the type you used> --doc-id $D

Zero hits on the BM25 leg means something other than ai_vault_kb.py wrote it.

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Pitfalls

• Brain writes are not idempotent — don't re-ingest the same content twice.
• Vault writes MUST go through ai_vault_kb.py — never a bare Qdrant upsert or mcp__better_qdrant__add_documents (missing bm25/doc_type makes the point unfindable). Never hand-roll raw Qdrant HTTP for a write.
• Collection name is exact — ai_vault_kb, not ai-vault-kb. Don't create topic-specific collections.
• On correction: update Brain (remember the corrected fact) and store the correction.
• fact_store and the memories collection are not the vault — research findings, model configs, prompt guides, infra facts go here.
• A search that returns nothing is a real answer — say "not in the vault" and go research it.