SQLite for Structured Data Skill

SQLite for Structured Data

Decision Check

Before writing any data analysis code, evaluate:

Will the data be queried more than once? -> Use SQLite
Are GROUP BY, COUNT, AVG, or JOIN operations needed? -> Use SQLite
Is custom Python/jq parsing code about to be written? -> Use SQLite instead
Is the dataset >100 records? -> Use SQLite

If the answer to any question above is YES, use SQLite. Do not write custom parsing code.

# Custom code for every query:
cat data.json | jq '.[] | select(.status=="failed")' | jq -r '.error_type' | sort | uniq -c

# SQL does the work:
sqlite3 data.db "SELECT error_type, COUNT(*) FROM errors WHERE status='failed' GROUP BY error_type"

Core Principle

SQLite is just a file -- no server, no setup, zero dependencies. Apply it when custom parsing code would otherwise be written or data would be re-processed for each query.

When to Use SQLite

Apply when ANY of these conditions hold:

>100 records -- JSON/grep becomes unwieldy
Multiple aggregations -- GROUP BY, COUNT, AVG needed
Multiple queries -- Follow-up questions about the same data are expected
Correlation needed -- Joining data from multiple sources
State tracking -- Queryable progress/status over time is needed

When NOT to Use SQLite

Skip SQLite when ALL of these are true:

<50 records total
Single simple query
No aggregations needed
No follow-up questions expected

For tiny datasets with simple access, JSON/grep is fine.

Red Flags -- Use SQLite Instead

STOP and use SQLite when about to:

Write Python/Node code to parse JSON/CSV for analysis
Run the same jq/grep command with slight variations
Write custom aggregation logic (COUNT, AVG, GROUP BY in code)
Manually correlate data by timestamps or IDs
Create temp files to store intermediate results
Process the same data multiple times for different questions

All of these mean: Load into SQLite once, query with SQL.

The Threshold

| Scenario | Tool | Why | |----------|------|-----| | 50 test results, one-time summary | Python/jq | Fast, appropriate | | 200+ test results, find flaky tests | SQLite | GROUP BY simpler than code | | 3 log files, correlate by time | SQLite | JOIN simpler than manual grep | | Track 1000+ file processing state | SQLite | Queries beat JSON parsing |

Rule of thumb: If parsing code is being written or data is being re-processed, use SQLite instead.

Available Tools

sqlite3 (always available)

sqlite3 ~/.claude-logs/project.db

sqlite-utils (optional, simplifies import)

Check availability before use:

command -v sqlite-utils >/dev/null 2>&1 && echo "available" || echo "not installed"

If sqlite-utils is available:

sqlite-utils insert data.db table_name data.json --pk=id
sqlite-utils query data.db "SELECT * FROM table"

If sqlite-utils is NOT available, fall back to sqlite3 with manual import:

sqlite3 data.db <<EOF
CREATE TABLE IF NOT EXISTS results (status TEXT, error_message TEXT);
.mode json
.import data.json results
EOF

Alternatively, install sqlite-utils: uv tool install sqlite-utils

Quick Start

Store Location

~/.claude-logs/<project-name>.db  # Persists across sessions

Basic Workflow

# 1. Connect
PROJECT=$(basename $(git rev-parse --show-toplevel 2>/dev/null || pwd))
sqlite3 ~/.claude-logs/$PROJECT.db

# 2. Create table (first time)
CREATE TABLE results (
  id INTEGER PRIMARY KEY,
  name TEXT,
  status TEXT,
  duration_ms INTEGER
);

# 3. Load data
INSERT INTO results (name, status, duration_ms)
SELECT json_extract(value, '$.name'),
       json_extract(value, '$.status'),
       json_extract(value, '$.duration_ms')
FROM json_each(readfile('data.json'));

# 4. Query (SQL does the work)
SELECT status, COUNT(*), AVG(duration_ms)
FROM results
GROUP BY status;

Key Mindset Shift

From: "Process data once and done" To: "Make data queryable"

Benefits:

Follow-up questions require no re-processing -- data is already loaded
Different analyses run instantly against the same dataset
State persists across sessions
SQL handles complexity -- less custom code needed

Additional Resources

./references/patterns.md -- Python vs SQL side-by-side comparison and real-world examples (test analysis, error correlation, file processing state)

Agent Skills: SQLite for Structured Data

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