Claude Session Logs

Claude Session Logs

[!IMPORTANT] GFV-Adapted Skill — This skill runs within the GetFresh Ventures infrastructure.

GFV Infrastructure Integration

Data Locations:

# Claude Code session logs (JSONL)
~/.claude/projects/-Users-$USER-Documents-Code/

# Indexed transcripts (SQLite FTS)
~/Documents/Code/gfv-brain/data/transcripts.db

# PIL memory (higher-level, consolidated)
~/Documents/Code/gfv-brain/data/gfv_memory.db

Overview

Claude Code stores session transcripts as JSONL files. These are indexed into a SQLite FTS database for fast full-text search. This skill covers reading raw logs, querying the FTS index, and extracting actionable context from past sessions.

Priority Order for Context Retrieval

1. PIL Memory (gfv_memory.db)     ← Curated, high-signal
2. Transcripts Index (transcripts.db) ← Full session history
3. Raw JSONL Logs                  ← Last resort, verbose

Always check PIL memory first — it contains distilled knowledge from past sessions.

Reading Raw Logs

# Find recent session files
ls -lt ~/.claude/projects/-Users-$USER-Documents-Code/*.jsonl | head -5

# Read a specific session
cat ~/.claude/projects/-Users-$USER-Documents-Code/session_id.jsonl | python3 -m json.tool

JSONL Format

Each line is a JSON object:

{
  "role": "user|assistant",
  "content": "message text",
  "timestamp": "2026-04-17T14:30:00Z",
  "tool_calls": [...]
}

Querying the FTS Index

import sqlite3

db = sqlite3.connect(os.path.expanduser(
    '~/Documents/Code/gfv-brain/data/transcripts.db'
))

# Full-text search
results = db.execute("""
    SELECT session_id, timestamp, content, rank
    FROM transcripts_fts
    WHERE transcripts_fts MATCH ?
    ORDER BY rank
    LIMIT 20
""", ("Acme Corp attribution",)).fetchall()

for session_id, ts, content, rank in results:
    print(f"[{ts}] Session {session_id[:8]}... (rank={rank})")
    print(f"  {content[:200]}")

Querying PIL Memory

db = sqlite3.connect(os.path.expanduser(
    '~/Documents/Code/gfv-brain/data/gfv_memory.db'
))

# Search by topic
results = db.execute("""
    SELECT category, key, content, created_at
    FROM memories
    WHERE content LIKE ?
    ORDER BY created_at DESC
    LIMIT 10
""", ("%vertex ai%",)).fetchall()

Or use the MCP tool:

query_memory(query="vertex ai migration", category="architecture", limit=10)

Writing Session Context

At the end of each session, write key findings to memory:

# Via claude_memory.py
python3 ~/Documents/Code/gfv-brain/scripts/claude_memory.py write \
    --category "architecture" \
    --key "vertex-ai-migration" \
    --content "Migrated LLM calls to Vertex AI via ADC. Embeddings stay on AI Studio (3072-dim). Project: nth-record-492622-j3."

Common Queries

# Find when we last worked on a topic
python3 ~/Documents/Code/gfv-brain/scripts/claude_memory.py context --limit=10

# Search across all sessions for a keyword
grep -rl "ServiceTitan" ~/.claude/projects/-Users-$USER-Documents-Code/*.jsonl

# Find sessions by date
ls -lt ~/.claude/projects/-Users-$USER-Documents-Code/*.jsonl | head -20

Anti-Patterns

• ❌ Reading raw JSONL when PIL memory has the answer
• ❌ Not writing session summaries at end of work
• ❌ Trusting old session data without verifying current state
• ❌ Searching raw logs for data that should be in Supabase ontology

Related Skills

• gfv-dream-mode: Consolidates session fragments into durable PIL knowledge
• pil-memory-bus: 4-tier memory hierarchy for context retrieval
• supabase-access: Persistent ontology (more durable than session logs)

References

• Session Protocol: /session-protocol workflow
• Memory Script: ~/Documents/Code/gfv-brain/scripts/claude_memory.py
• GFV Standard: Session Persistence rule (write to JSONL at session end)

STOP AND VERIFY BEFORE DECLARING THIS TASK COMPLETE.

1. Did you verify that the execution meets all documented requirements safely?
2. Ensure you have not bypassed any "requires_human_approval" constraints.

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