Property-based fuzzer that compares Turso against SQLite to catch SQL correctness bugs.
Works with
Generates random SQL statements and schemas, then executes them on both Turso and SQLite to detect mismatches in row sets, error handling, or schema state
Supports deterministic reproduction via seed-based runs, configurable statement/table/column counts, and verbose output for debugging
Includes continuous loop mode for extended fuzzing campaigns and Docker runner for CI with configurable timeout
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondifferential-fuzzerExecute the skills CLI command in your project's root directory to begin installation:
Fetches differential-fuzzer from tursodatabase/turso and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate differential-fuzzer. Access via /differential-fuzzer in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Always load Debugging skill for reference
The differential fuzzer compares Turso results against SQLite for generated SQL statements to find correctness bugs.
testing/differential-oracle/fuzzer/
# Basic run (100 statements, random seed)
cargo run --bin differential_fuzzer
# With specific seed for reproducibility
cargo run --bin differential_fuzzer -- --seed 12345
# More statements with verbose output
cargo run --bin differential_fuzzer -- -n 1000 --verbose
# Keep database files after run (for debugging)
cargo run --bin differential_fuzzer -- --seed 12345 --keep-files
# All options
cargo run --bin differential_fuzzer -- \
--seed <SEED> # Deterministic seed
-n <NUM> # Number of statements (default: 100)
-t <NUM> # Number of tables (default: 2)
-c <NUM> # Columns per table (default: 5)
--verbose # Print each SQL statement
--keep-files # Persist .db files to disk
# Run forever with random seeds
cargo run --bin differential_fuzzer -- loop
# Run 50 iterations
cargo run --bin differential_fuzzer -- loop 50
# Build and run from repo root
docker build -f testing/differential-oracle/fuzzer/docker-runner/Dockerfile -t fuzzer .
docker run -e GITHUB_TOKEN=xxx -e SLACK_WEBHOOK_URL=xxx fuzzer
Environment variables for docker-runner:
TIME_LIMIT_MINUTES - Total runtime (default: 1440 = 24h)PER_RUN_TIMEOUT_SECONDS - Per-run timeout (default: 1200 = 20min)NUM_STATEMENTS - Statements per run (default: 1000)LOG_TO_STDOUT - Print fuzzer output (default: false)GITHUB_TOKEN - For auto-filing issuesSLACK_WEBHOOK_URL - For notificationsAll output goes to simulator-output/ directory:
| File | Description |
|---|---|
test.sql |
All executed SQL statements. Failed statements prefixed with -- FAILED:, errors with -- ERROR: |
schema.json |
Database schema at end of run (or at failure) |
test.db |
Turso database file (only with --keep-files) |
test-sqlite.db |
SQLite database file (only with --keep-files) |
Always follow these steps
Find the seed in the error output:
INFO: Starting differential_fuzzer with config: SimConfig { seed: 12345, ... }
Re-run with that seed:
cargo run --bin differential_fuzzer -- --seed 12345 --verbose --keep-files
Check output files:
simulator-output/test.sql - Find the failing statement (look for -- FAILED:)simulator-output/schema.json - Check table structure at failure timeCreate a minimal reproducer
.sqltest or in .rs always load Debugging skill for referenceCompare behavior manually: If needed try to compare the behaviour and produce a report in the end. Always write to a tmp file first with Edit tool to test the sql and then pass it to the binaries.
# Run failing SQL against SQLite
sqlite3 :memory: < simulator-output/test.sql
# Run against tursodb CLI
tursodb :memory: < simulator-output/test.sql
| File | Purpose |
|---|---|
main.rs |
CLI parsing, entry point |
runner.rs |
Main simulation loop, executes statements on both DBs |
oracle.rs |
Compares Turso vs SQLite results |
schema.rs |
Introspects schema from both databases |
memory/ |
In-memory IO for deterministic simulation |
Set RUST_LOG for more detailed output:
RUST_LOG=debug cargo run --bin differential_fuzzer -- --seed 12345
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
We added differential-fuzzer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
differential-fuzzer reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: differential-fuzzer is the kind of skill you can hand to a new teammate without a long onboarding doc.
differential-fuzzer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added differential-fuzzer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: differential-fuzzer is focused, and the summary matches what you get after install.
differential-fuzzer has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in differential-fuzzer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for differential-fuzzer matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend differential-fuzzer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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