coco-research/coco/cognee-store

Name: cognee-store
Author: coco-research

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coco-research/coco
systems/cognee/skills/cognee-store/SKILL.md
Updated 4 Oct 2026
Version 1.0.01 version recorded

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Use when the user says 'cognee store', 'push to cognee', 'save to graph', 'remember this' or 'log this decision', or when session decisions must be stored. Writes entities, decisions and events over POST /api/v1/remember.

Install

npx vskill@latest install coco-research/coco/cognee-store

SKILL.md

/cognee-store — Push Knowledge to the Graph

Stores structured knowledge into Cognee's knowledge graph. Functions as the write path for Coco's memory layer — maps entities, decisions, events, and relationships to graph nodes and edges with embeddings for later semantic retrieval.

Quick Reference

bash

COGNEE="${COGNEE_BASE_URL:-http://localhost:8000}"
DATASET="my-project"
 
# Store a text fact (auto-cognifies)
curl -s -X POST "$COGNEE/api/v1/remember" \
  -F "datasetName=$DATASET" \
  -F 'data={"entity": {"type": "decision", "text": "Use JWT for API auth", "date": "2026-06-30", "decided_by": "dana", "context": "Stateless, works with existing infra"}}' \
  -F "run_in_background=false" | jq .
 
# Store file-based knowledge
curl -s -X POST "$COGNEE/api/v1/remember" \
  -F "datasetName=$DATASET" \
  -F "data=@/path/to/decision-log.md" \
  -F "run_in_background=false" | jq .
 
# Cognify existing data (process + build graph)
curl -s -X POST "$COGNEE/api/v1/cognify" \
  -H "Content-Type: application/json" \
  -d '{"datasets": ["my-project"]}' | jq .

Data Format

All knowledge is stored as text, structured for Cognee's graph extraction. Use these formats:

Entities

ENTITY: {name} | TYPE: {person|team|system|module|org_unit|document}
DESCRIPTION: {one-line description}
METADATA: {key: value, ...}

Decisions

DECISION: {text} | DATE: {YYYY-MM-DD}
DECIDED_BY: {name}
CONTEXT: {why this was decided, alternatives considered}
IMPACT: {what changes as a result}

Events

EVENT: {title} | DATE: {YYYY-MM-DD} | TYPE: {meeting|call|email|milestone|deploy}
SUMMARY: {what happened}
PARTICIPANTS: {comma-separated names}
OUTCOMES: {decisions made, action items}

Relationships

RELATIONSHIP: {entity_a} -> {entity_b} | TYPE: {member_of|owns|depends_on|reports_to|blocks|administers|scoped_to}
CONTEXT: {why this relationship exists}

Tasks

TASK: {description} | STATUS: {open|in_progress|blocked|waiting|done|cancelled}
PRIORITY: {1 (highest) - 5 (lowest)}
ASSIGNED_TO: {name}
BLOCKED_BY: {task or entity reference}

/cognee-store:update — End-of-Session Flush

This is the most important command. When invoked, the agent MUST thoroughly review the entire conversation and write everything learned to Cognee. This is a forcing function — do not skip anything.

Procedure

Step 1: Check Cognee availability

bash

COGNEE="${COGNEE_BASE_URL:-http://localhost:8000}"
curl -s -o /dev/null -w "%{http_code}" "$COGNEE/health"

If not 200: "Cognee is not running. Start with cognee server start." → offer to use /brain-update instead.

Step 2: Verify dataset exists

bash

curl -s "$COGNEE/api/v1/datasets" | jq -r '.[].name'

If the project dataset doesn't exist: "No dataset found for this project. Run /cognee init first."

Step 3: Scan the full conversation

Go through every message from top to bottom. Extract:

Category	What to look for
New entities	Any person, team, role, system, module mentioned for the first time
New relationships	Connections discovered: X owns Y, A reports to B
New decisions	Anything decided, agreed, confirmed, resolved, or ruled out
New events	Meetings, calls, emails read, milestones, deployments
New tasks	Action items, to-dos, next steps, follow-ups
Task updates	Existing tasks that changed status
Entity updates	New info about existing entities

Step 4: Present summary

COGNEE STORE SUMMARY
====================
Dataset:        my-project
 
New entities:      3 (Alice Chen [person], PlatformHub [module], Auth Service [system])
New decisions:     2 (Use JWT for API auth, Rate-limit at gateway level)
New events:        1 (Architecture review call Jun 30)
New tasks:         4 (Set up JWT middleware, Configure rate limiter, ...)
Task updates:      2 (task #3 → blocked, task #5 → in_progress)
New relationships: 1 (Auth Service depends_on PlatformHub)
Entity updates:    1 (Alice Chen: added backend lead role)
 
Total items to store: 13

Step 5: Wait for confirmation

Ask: "Write all to Cognee? [Y/n/adjust]"

Step 6: Execute writes

On confirmation, format each item according to the data formats above and send as a single batch:

bash

COGNEE="${COGNEE_BASE_URL:-http://localhost:8000}"
 
# Build the payload as a multiline text document
cat > /tmp/cognee-store-batch.txt << 'STORE_EOF'
ENTITY: Alice Chen | TYPE: person
DESCRIPTION: Backend lead on PlatformHub
METADATA: {role: "backend lead", team: "Engineering"}
 
ENTITY: PlatformHub | TYPE: module
DESCRIPTION: Central platform for managing external access
 
ENTITY: Auth Service | TYPE: system
DESCRIPTION: Authentication and authorization service
 
DECISION: Use JWT for API auth | DATE: 2026-06-30
DECIDED_BY: dana
CONTEXT: Stateless, works with existing infrastructure. Considered session tokens but JWT more scalable.
IMPACT: All API endpoints will validate JWT tokens
 
DECISION: Rate-limit at gateway level | DATE: 2026-06-30
DECIDED_BY: dana
CONTEXT: Prefer gateway-level rate limiting over per-service to avoid duplication
IMPACT: API gateway configuration needs updating
 
EVENT: Architecture review call | DATE: 2026-06-30 | TYPE: call
SUMMARY: Reviewed authentication and rate-limiting architecture
PARTICIPANTS: dana, alex
OUTCOMES: JWT chosen for auth, rate-limiting at gateway
 
RELATIONSHIP: Auth Service -> PlatformHub | TYPE: depends_on
CONTEXT: Auth service validates tokens before requests reach PlatformHub
 
TASK: Set up JWT middleware | STATUS: open
PRIORITY: 1
ASSIGNED_TO: Alice Chen
 
TASK: Configure rate limiter at gateway | STATUS: open
PRIORITY: 2
ASSIGNED_TO: Alice Chen
 
TASK: Update API docs with auth headers | STATUS: open
PRIORITY: 3
 
TASK: Add monitoring for rate-limit hits | STATUS: open
PRIORITY: 4
STORE_EOF
 
# Send batch
curl -s -X POST "$COGNEE/api/v1/remember" \
  -F "datasetName=$DATASET" \
  -F "data=@/tmp/cognee-store-batch.txt" \
  -F "run_in_background=false" | jq .
 
rm /tmp/cognee-store-batch.txt

Step 7: Report

COGNEE STORE COMPLETE
=====================
Dataset:    my-project
Stored:     13 items (3 entities, 2 decisions, 1 event, 4 tasks, 2 updates, 1 relationship)
Cognified:  yes
Graph:      updated with new nodes and edges
 
Recall with: /cognee-recall "what did we decide about authentication"

Behavior Rules

Auto-write (no confirmation needed)

Entity sync from external sources (MCP, APIs)
Task status updates
Event logging from emails and meetings
Changelog entries

Confirm-write (propose to user first)

New decisions
New relationships between entities
Bulk end-of-session flush (/cognee-store:update)

Inline store (single items during conversation)

When a decision is made or important info surfaces mid-conversation, offer a lightweight store:

> "Should I store this decision in Cognee? [store / skip]"

If "store": format as single item and send via /remember.

Dedup

Before storing, check if similar content already exists by doing a quick search:

bash

curl -s -X POST "$COGNEE/api/v1/search" \
  -H "Content-Type: application/json" \
  -d "{\"query\": \"$SEARCH_TEXT\", \"datasets\": [\"$DATASET\"], \"search_type\": \"FEELING_LUCKY\", \"top_k\": 5}" | jq .

If high-confidence match found (>80% similarity), note it and skip: "Similar content already exists in graph. Skipping duplicate."

Batch efficiency

Group all items into a single /remember call rather than sending individual requests. Cognee processes the batch and builds graph connections between items automatically.

Cognee vs Brain: When to use which for storing

Scenario	Use
Quick local decision log	Brain (SQLite, instant)
Cross-project entity linking	Cognee (graph edges span datasets)
Semantic search needed later	Cognee (embeddings enable fuzzy recall)
Offline / no Cognee running	Brain (zero dependencies)
Session context for auto-recall	Cognee (session-aware search)
Both (belt and suspenders)	Store to both

Security

Security scanPassed, 95 of 100Pattern checks on the skill's files.
Model reviewPassed

See the full security report

Quality evaluation

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Source

SKILL.md in the repositoryCheckingsystems/cognee/skills/cognee-store/SKILL.md

Popularity

Repository stars: 427
Repository forks: 12
7-day trend score: 35

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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