nate-jones-second-brain（内特·琼斯第二大脑）

Nate Jones Second Brain

When intelligence is abundant, context becomes the scarce resource. This skill is context architecture — a persistent, searchable knowledge layer that turns your agent into a personal knowledge manager.

Two opinionated primitives:

• Supabase — your database, and so much more. PostgreSQL + pgvector. Stores thoughts, people, projects, ideas, and tasks as structured data with vector embeddings. REST API built in. Your data, your infrastructure. Models come and go; your context persists. And once you have a Supabase project, you've unlocked the foundation for everything else you'll want to build — the Second Brain is just the beginning.
• OpenRouter — your AI gateway. One API key, every model. Embeddings and LLM calls for classification and routing. Swap models by changing a string. Future-proof by design.

Everything else — how you capture thoughts, how you retrieve them, what you build on top — is application layer. The skill covers the foundation.

If the tables don't exist yet, see {baseDir}/references/setup.md

Building Blocks

These are the operational concepts behind the system. Understanding them helps you operate correctly.

Full conceptual framework: {baseDir}/references/concepts.md

Five Tables

Every table has semantic search via its own match_* function. Cross-table search via search_all.

Routing Rules

When a thought is classified:

If confidence < 0.6, don't route. Leave in thoughts, tell user.

Quick Start

Capture a thought (full pipeline)

# 1. Embed
EMBEDDING=$(curl -s -X POST "https://openrouter.ai/api/v1/embeddings" \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "openai/text-embedding-3-small", "input": "Sarah mentioned she is thinking about leaving her job to start consulting"}' \
  | jq -c '.data[0].embedding')

# 2. Classify (run in parallel with step 1)
METADATA=$(curl -s -X POST "https://openrouter.ai/api/v1/chat/completions" \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "openai/gpt-4o-mini", "response_format": {"type": "json_object"}, "messages": [{"role": "system", "content": "Extract metadata from the captured thought. Return JSON with: type (observation/task/idea/reference/person_note), topics (1-3 tags), people (array), action_items (array), dates_mentioned (array), confidence (0-1), suggested_route (people/projects/ideas/admin/null), extracted_fields (structured data for destination table)."}, {"role": "user", "content": "Sarah mentioned she is thinking about leaving her job to start consulting"}]}' \
  | jq -r '.choices[0].message.content')

# 3. Store in thoughts (the Receipt)
curl -s -X POST "$SUPABASE_URL/rest/v1/thoughts" \
  -H "apikey: $SUPABASE_SERVICE_ROLE_KEY" \
  -H "Authorization: Bearer $SUPABASE_SERVICE_ROLE_KEY" \
  -H "Content-Type: application/json" \
  -H "Prefer: return=representation" \
  -d "[{\"content\": \"Sarah mentioned she is thinking about leaving her job to start consulting\", \"embedding\": $EMBEDDING, \"metadata\": $METADATA}]"

# 4. Route based on classification (if confidence >= 0.6)

Full pipeline with routing logic: {baseDir}/references/ingest.md

Semantic search (single table)

QUERY_EMBEDDING=$(curl -s -X POST "https://openrouter.ai/api/v1/embeddings" \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "openai/text-embedding-3-small", "input": "career changes"}' \
  | jq -c '.data[0].embedding')

curl -s -X POST "$SUPABASE_URL/rest/v1/rpc/match_thoughts" \
  -H "apikey: $SUPABASE_SERVICE_ROLE_KEY" \
  -H "Authorization: Bearer $SUPABASE_SERVICE_ROLE_KEY" \
  -H "Content-Type: application/json" \
  -d "{\"query_embedding\": $QUERY_EMBEDDING, \"match_threshold\": 0.5, \"match_count\": 10, \"filter\": {}}"

Cross-table search

curl -s -X POST "$SUPABASE_URL/rest/v1/rpc/search_all" \
  -H "apikey: $SUPABASE_SERVICE_ROLE_KEY" \
  -H "Authorization: Bearer $SUPABASE_SERVICE_ROLE_KEY" \
  -H "Content-Type: application/json" \
  -d "{\"query_embedding\": $QUERY_EMBEDDING, \"match_threshold\": 0.5, \"match_count\": 20}"

Returns table_name, record_id, label, detail, similarity, created_at from all tables.

List active projects

curl -s "$SUPABASE_URL/rest/v1/projects?status=eq.active&select=name,next_action,notes&order=updated_at.desc" \
  -H "apikey: $SUPABASE_SERVICE_ROLE_KEY" \
  -H "Authorization: Bearer $SUPABASE_SERVICE_ROLE_KEY"

List pending tasks

curl -s "$SUPABASE_URL/rest/v1/admin?status=eq.pending&select=name,due_date,notes&order=due_date.asc" \
  -H "apikey: $SUPABASE_SERVICE_ROLE_KEY" \
  -H "Authorization: Bearer $SUPABASE_SERVICE_ROLE_KEY"

Ingest Pipeline

When content arrives from any source:

1. Embed the text via OpenRouter (1536-dim vector)
2. Classify via OpenRouter LLM (type, topics, people, confidence, suggested route)
3. Log in thoughts (the Receipt — always, regardless of routing)
4. Bounce check — if confidence < 0.6, stop here
5. Route to structured table based on type (the Sorter)
6. Confirm to the user what was captured and where it was filed

Full pipeline details: {baseDir}/references/ingest.md

Metadata Schema

Every thought gets classified with:

References

• Conceptual framework: {baseDir}/references/concepts.md
• First-time setup: {baseDir}/references/setup.md
• Database schema (SQL): {baseDir}/references/schema.md
• Ingest pipeline details: {baseDir}/references/ingest.md
• Retrieval operations: {baseDir}/references/retrieval.md
• OpenRouter API patterns: {baseDir}/references/openrouter.md

Env Vars

Security Notes

Why service_role key? Supabase provides two keys: anon (public, respects RLS) and service_role (full access, bypasses RLS). This skill uses service_role because:

• This is a single-user personal knowledge base, not a multi-tenant app
• Your agent IS the trusted server-side component
• The RLS policy restricts access to service_role only — the most restrictive option
• Using the anon key would require loosening RLS to allow anonymous access to your thoughts, which is worse

Data sent to OpenRouter: All captured text (thoughts, names, action items) is sent to OpenRouter for embedding and classification. This is inherent to the design — you need AI to understand meaning. Don't capture highly sensitive information unless you accept OpenRouter's data handling policies.

Key handling: Store SUPABASE_SERVICE_ROLE_KEY and OPENROUTER_API_KEY securely. Never commit them to public repos. Rotate periodically. In OpenClaw, store them in openclaw.json under skills.entries or as environment variables.

Built by Limited Edition Jonathan • natebjones.com