create-rule
Create Cursor rules for persistent AI guidance. Use when the user wants to create a rule, add coding standards, set up project conventions, configure…
Knowledge graph memory backend powered by Cognee. Query, status check, dataset management, and backend switching between Brain and Cognee. Triggers on: 'cognee', 'knowledge graph', 'graph memory', 'switch memory', 'memory backend'.
$ npx -y skills add coco-research/coco --skill cognee --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/cogneeContext preview
The summary Claude sees to decide when to auto-load this skill.
Knowledge graph memory backend powered by Cognee. Query, status check, dataset management, and backend switching between Brain and Cognee. Triggers on: 'cognee', 'knowledge graph', 'graph memory', 'switch memory', 'memory backend'.
name: cognee description: "Use when the user says 'cognee', asks about knowledge graph or graph memory, wants to switch memory backends, or needs a status check, dataset init, or graph view. Covers Cognee endpoints plus the Brain fallback."
A persistent knowledge graph memory system powered by [Cognee](https://github.com/coco-research/cognee). Stores entities, decisions, events, and relationships as graph nodes with embeddings, enabling semantic search across projects and sessions.
COGNEE="http://localhost:8000" # default; override with COGNEE_BASE_URL
# Status check
curl -s "$COGNEE/health" | jq .
# List datasets
curl -s "$COGNEE/api/v1/datasets" | jq .
# Create a dataset for this project
curl -s -X POST "$COGNEE/api/v1/datasets" \
-H "Content-Type: application/json" \
-d '{"name": "my-project"}' | jq .
# Quick semantic search
curl -s -X POST "$COGNEE/api/v1/search" \
-H "Content-Type: application/json" \
-d '{"query": "what did we decide about authentication", "search_type": "FEELING_LUCKY", "top_k": 10}' | jq .Check if Cognee is reachable and show available datasets.
COGNEE="${COGNEE_BASE_URL:-http://localhost:8000}"
echo "=== Cognee Status ==="
HEALTH=$(curl -s -o /dev/null -w "%{http_code}" "$COGNEE/health" 2>/dev/null)
if [ "$HEALTH" = "200" ]; then
echo "Server: RUNNING at $COGNEE"
echo ""
echo "Datasets:"
curl -s "$COGNEE/api/v1/datasets" | jq -r '.[] | " • \(.name) (\(.id))"'
else
echo "Server: NOT REACHABLE"
echo ""
echo "Start Cognee:"
echo " pip install cognee && cognee server start"
fiCreate a Cognee dataset for the current project.
**Procedure:**
1. Ask the user for the **dataset name** (default: current directory name, slugified). 2. Check if a dataset with that name already exists:
curl -s "$COGNEE/api/v1/datasets" | jq -r '.[].name'
3. If it exists: "Dataset 'X' already exists. Using it." → skip creation. 4. If not: create it:
curl -s -X POST "$COGNEE/api/v1/datasets" \
-H "Content-Type: application/json" \
-d "{\"name\": \"$DATASET_NAME\"}" | jq .5. Confirm:
COGNEE INITIALIZED
==================
Dataset: {name} ({id})
Endpoint: $COGNEE
Next: Use /cognee-store to push knowledge, /cognee-recall to search.Switch which memory backend is primary for this project.
**Options:**
**Behavior:** This sets a preference. Skills that support both backends will check this preference and route accordingly. Default behavior without explicit switch: Brain for local queries, Cognee for cross-project and semantic search.
Show the knowledge graph for a specific entity or the current dataset.
# Get dataset ID first DATASET_ID=$(curl -s "$COGNEE/api/v1/datasets" | jq -r '.[] | select(.name=="my-project") | .id') # View graph curl -s "$COGNEE/api/v1/datasets/$DATASET_ID/graph" | jq .
Cognee and Brain coexist. They are not mutually exclusive:
┌─────────────────────────────────────┐ │ Coco Agent │ ├─────────────────────────────────────┤ │ /cognee-recall │ /brain │ │ /cognee-store │ /brain-update │ ├─────────────────────────────────────┤ │ Cognee (graph) │ Brain (SQLite) │ │ localhost:8000 │ project_brain.db │ └─────────────────────────────────────┘
Before any Cognee operation, check the health endpoint. If unreachable:
Default dataset name = project directory slug. For example:
User can override. Ask once, remember the mapping.
Unlike Brain (one DB per project folder), Cognee datasets are namespaced. An agent working across multiple projects can query multiple datasets in a single search:
curl -s -X POST "$COGNEE/api/v1/search" \
-H "Content-Type: application/json" \
-d '{"query": "authentication decisions", "datasets": ["project-a", "project-b"], "search_type": "FEELING_LUCKY"}' | jq .Cognee must be installed and running:
pip install cognee cognee server start
Verify: `curl http://localhost:8000/health`
CoCo Super Intelligence is the orchestration layer that turns Claude Code, Cursor, or Codex into an engineering department: a routed advisory board, 185 skills, 280 commands, persistent state. Local. Open-core — MIT core; Super Intelligence is proprietary, own-use.
Repo: coco-research/coco
Create Cursor rules for persistent AI guidance. Use when the user wants to create a rule, add coding standards, set up project conventions, configure…
Guides users through creating effective Agent Skills for Cursor. Use when the user wants to create, write, or author a new skill, or asks about skill…
Create custom subagents for specialized AI tasks. Use when the user wants to create a new type of subagent, set up task-specific agents, configure code…
Convert 'Applied intelligently' Cursor rules (.cursor/rules/*.mdc) and slash commands (.cursor/commands/*.md) to Agent Skills format (.cursor/skills/). Use…
Modify Cursor/VSCode user settings in settings.json. Use when the user wants to change editor settings, preferences, configuration, themes, font size, tab…
Train and optimize AI agents using Microsoft's Agent Lightning framework with reinforcement learning. Use when setting up agent training, instrumenting agents…