Nate Jones Second Brain

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:

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

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:

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

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"