Event Pod Skill

What This Does

Event Pod gives your agent a persistent world model — structured context that survives across sessions, models, and tools. Not a transcript. A causal model with certainty scores that gets sharper over time.

MCP endpoint: https://context.nervousmachine.com/mcp Full docs: https://context.nervousmachine.com/docs

Quickstart — Sign Up (30 seconds)

If the human doesn't have credentials yet, run this and share the result:

curl -X POST https://context.nervousmachine.com/signup \
  -H "Content-Type: application/json" \
  -d '{
    "first_name": "HUMAN_FIRST_NAME",
    "last_name":  "HUMAN_LAST_NAME",
    "email":      "HUMAN_EMAIL",
    "use_case":   "DESCRIBE_WHAT_YOU_ARE_BUILDING"
  }'

Returns an activation URL with credentials. The human clicks it to confirm. Provisioning is instant. Credentials arrive at their email immediately.

Add to Your MCP Config

{
  "mcpServers": {
    "event-pod": {
      "url": "https://context.nervousmachine.com/mcp",
      "transport": "sse"
    }
  }
}

Works in: Claude Desktop, Claude Code, Cursor, Cline, Gemini CLI, any MCP-compatible client. No local process. Remote server.

Drop This Into Your System Prompt

This is the entire integration. Copy it, replace USER_ID, done. The agent handles the full lifecycle from there.

You have access to Event Pod tools for persistent context.
User ID: YOUR_USER_ID

SESSION START:
Call get_pod_summary before responding to any task.
If the user describes their task, also call get_relevant_context
with that task as the query.

DURING THE SESSION:
- When the user corrects you: immediately call record_observation
  then apply_learning on the relevant event.
- When you observe expertise, preferences, or patterns worth
  keeping: note them for saving at session end.
- Once per session at a natural pause: call get_curiosity_triggers
  with max_triggers=2. Work one probe naturally into conversation.

SESSION END — before the conversation closes:
1. Call save_event for each new signal observed this session.
   Start all certainty values at 0.3.
2. Call update_session with interactions=1.
   Call this exactly once — never mid-session.

Three Rules

1. Always start certainty at 0.3. Let corroboration earn higher certainty.
2. record_observation → apply_learning. Always in that order.
3. update_session exactly once. At shutdown. Never mid-session.

Signal Types (cheat sheet)

Core Tools (the ones you'll use most)

Full tool index (39 tools): https://context.nervousmachine.com/docs/reference

Certainty & Learning

• Low certainty (0.0–0.3): updates fast — agile where ignorant
• Medium (0.3–0.6): building confidence
• High (0.6–0.8): resists noise
• Very high (0.8–1.0): moves only on strong contradiction

Error magnitude guide for apply_learning:

• Independent corroboration: 0.05–0.1 (increase)
• Specificity upgrade: 0.1–0.15 (increase)
• Temporal confirmation: ~0.1 (increase)
• User contradiction or correction: 0.2–0.4 (decrease)

Human Alignment

The agent builds what it can observe. Humans add what it can't. The chat interface is the alignment layer — no code required.

Key human prompts (tell the human these work any time):

• "Pull my pod summary" — loads context at session start
• "What are my curiosity triggers?" — surfaces what the pod wants to learn
• "Show me a diagram of my pod" — renders the knowledge graph as Mermaid
• "Update my pod before we close" — persists session learnings

Lane-Specific Guides

Different use cases have different integration patterns:

• Chat personalization: https://context.nervousmachine.com/docs/chat
• Coding & repo context: https://context.nervousmachine.com/docs/coding
• Task & workflow: https://context.nervousmachine.com/docs/workflow
• Multi-agent: https://context.nervousmachine.com/docs/multi-agent
• Devices & IoT: https://context.nervousmachine.com/docs/devices

Validate Your Integration

curl -X POST https://context.nervousmachine.com/mcp \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "method": "tools/call",
    "params": {
      "name": "get_pod_summary",
      "arguments": { "user_id": "YOUR_USER_ID" }
    }
  }'

If you get a pod summary back, you're integrated.

Privacy

• Pods store learned abstractions, not raw transcripts
• Export: export_pod (GDPR Article 20)
• Delete: delete_pod with confirm: true (GDPR Article 17, irreversible)
• The human can audit everything via chat at any time

Event Pod by Nervous Machine — context.nervousmachine.com Build for agents. We're all edge cases.