How do I install the Cognify skill?

Run npx skillmds@latest add s3yed/cognify-3 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Cognify safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Cognify?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Cognify free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Cognify?

S3YED (@s3yed) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Cognify by s3yed — Agent Skill

Name: Cognify
Author: S3YED

Cognify

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.

S3YED 326c810 2.2 KB Updated Sep 17, 2026

File contents

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.

---name: cognify-3description: 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.---# Cognify (Hermes skill)Give yourself a knowledge graph. `ingest` documents, `recall` facts plus how theyconnect. Backed by ChromaDB + networkx locally (no external services), orTurboVec + Neo4j for a shared graph.## Setup (once per box)```bashpip install 'cognify-kg[local]' # or [claude] to also use Claude as extractorexport ANTHROPIC_API_KEY=... # Claude extractor (auto-detected)# or: export OPENROUTER_API_KEY=... # any OpenAI-compatible modelexport COGNIFY_DATA_DIR="$HOME/.cognify" # where the graph lives```## Use it from the shell (simplest)```bash# ingest a file, a folder, or piped text — pick a tenant to isolate this agent's datacognify ingest /path/to/handbook.pdf --tenant myagent --namespace docscognify ingest-dir ~/notes --glob '**/*.md' --tenant myagent --cacheecho "free text to remember" | cognify ingest - --tenant myagent# recall: returns chunks + connected entities/relations as grounded contextcognify recall "who owns onboarding and what tool do they use?" --tenant myagentcognify stats --tenant myagent```Parse the JSON from `recall` and use the `entities`/`relations`/`chunks` ascontext for your answer.## Use it over HTTP (for a shared graph or a long-running agent)```bashcognify-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.

Frequently asked questions