Hermes

Skill S3YED/appie-kit/packages/cognify/integrations/hermes

Use when you need durable memory over documents — ingest files/notes into a typed knowledge graph and recall facts with their relationships. Build a knowledge base from PDFs, markdown, or pasted text, then ask grounded questions.From its SKILL.md

Install

npx -y skills add S3YED/appie-kit --skill hermes

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

6 stars6 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.2 KB, 552 tokens by cl100k_base, as published. Nobody here has run it

Cognify (Hermes skill)

Give yourself a knowledge graph. ingest documents, recall facts plus how they connect. Backed by ChromaDB + networkx locally (no external services), or TurboVec + Neo4j for a shared graph.

Setup (once per box)

pip install 'cognify-kg[local]'          # or [claude] to also use Claude as extractor
export ANTHROPIC_API_KEY=...             # Claude extractor (auto-detected)
# or: export OPENROUTER_API_KEY=...      # any OpenAI-compatible model
export COGNIFY_DATA_DIR="$HOME/.cognify" # where the graph lives

Use it from the shell (simplest)

# ingest a file, a folder, or piped text — pick a tenant to isolate this agent's data
cognify ingest /path/to/handbook.pdf --tenant myagent --namespace docs
cognify ingest-dir ~/notes --glob '**/*.md' --tenant myagent --cache
echo "free text to remember" | cognify ingest - --tenant myagent

# recall: returns chunks + connected entities/relations as grounded context
cognify recall "who owns onboarding and what tool do they use?" --tenant myagent
cognify stats --tenant myagent

Parse the JSON from recall and use the entities/relations/chunks as context for your answer.

Use it over HTTP (for a shared graph or a long-running agent)

cognify-serve &      # 127.0.0.1:8799  (set COGNIFY_BACKEND=neo4j for a shared graph)
curl -s localhost:8799/ingest -d '{"path":"/docs/policy.md","tenant":"myagent"}' -H 'content-type: application/json'
curl -s localhost:8799/recall -d '{"query":"refund policy?","tenant":"myagent"}' -H 'content-type: application/json'

Rules

Always pass a stable --tenant for this agent so your memory stays isolated from other agents on the box.
Ingesting calls the LLM once per chunk (cost). Use --cache on ingest-dir so re-runs skip unchanged files.
For a fleet-shared graph use COGNIFY_BACKEND=neo4j with NEO4J_* set; for a private per-box graph use the default local backend.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.