nate-jones-second-brain

name	nate-jones-second-brain
description	Set up and operate a personal knowledge system using Supabase (pgvector) and OpenRouter. Five structured tables — thoughts (inbox log), people, projects, ideas, admin — with AI-powered classification, confidence-based routing, and semantic search across all categories. Captures thoughts from any source, classifies them via LLM, routes them to the right table (the Sorter), rejects low-confidence classifications (the Bouncer), and logs everything (the Receipt). Two opinionated primitives — Supabase for persistent context architecture, OpenRouter as the AI gateway — that unlock unlimited applications on top. The foundation layer for a personal knowledge system. By Limited Edition Jonathan • natebjones.com
metadata	{"openclaw":{"requires":{"env":["SUPABASE_URL","SUPABASE_SERVICE_ROLE_KEY","OPENROUTER_API_KEY"]},"homepage":"https://natebjones.com"}}

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.

Block	What It Does	Implementation
Drop Box	One frictionless capture point	Everything goes to thoughts first
Sorter	AI classification + routing	LLM classifies type, then routes to structured table
Form	Consistent data contracts	Each table has a defined schema
Filing Cabinet	Source of truth per category	people, projects, ideas, admin tables
Bouncer	Confidence threshold	confidence < 0.6 = don't route, stay in inbox
Receipt	Audit trail	thoughts row logs what came in, where it went
Tap on the Shoulder	Proactive surfacing	Daily digest queries (application layer)
Fix Button	Agent-mediated corrections	Move records between tables on user request

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

Five Tables

Table	Role	Key Fields
thoughts	Inbox Log / audit trail	content, embedding, metadata (type, topics, people, confidence, routed_to)
people	Relationship tracking	name (unique), context, follow_ups, tags, embedding
projects	Work tracking	name, status, next_action, notes, tags, embedding
ideas	Insight capture	title, summary, elaboration, topics, embedding
admin	Task management	name, due_date, status, notes, embedding

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

Routing Rules

When a thought is classified:

Type	Route	Action
person_note	people	Upsert: create person or append to existing context
task	admin	Insert new task (status=pending)
idea	ideas	Insert new idea
observation	none	Stays in thoughts only
reference	none	Stays in thoughts only

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:

Field	Type	Values
type	string	observation, task, idea, reference, person_note
topics	string[]	1-3 short topic tags (always at least one)
people	string[]	People mentioned (empty if none)
action_items	string[]	Implied to-dos (empty if none)
dates_mentioned	string[]	Dates in YYYY-MM-DD format (empty if none)
source	string	Where it came from: slack, signal, cli, manual, etc.
confidence	float	LLM classification confidence (0-1). The Bouncer uses this.
routed_to	string	Which table the thought was filed into (null if unrouted)
routed_id	string	UUID of the record in the destination table (null if unrouted)

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

Variable	Service
SUPABASE_URL	Supabase project REST base URL
SUPABASE_SERVICE_ROLE_KEY	Supabase auth (full access)
OPENROUTER_API_KEY	OpenRouter API key

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