differential-fuzzer

tursodatabase/turso · updated Apr 8, 2026

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$npx skills add https://github.com/tursodatabase/turso --skill differential-fuzzer
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summary

Property-based fuzzer that compares Turso against SQLite to catch SQL correctness bugs.

  • 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
skill.md

Differential Fuzzer

Always load Debugging skill for reference

The differential fuzzer compares Turso results against SQLite for generated SQL statements to find correctness bugs.

Location

testing/differential-oracle/fuzzer/

Running the Fuzzer

Single Run

# 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

Continuous Fuzzing (Loop Mode)

# Run forever with random seeds
cargo run --bin differential_fuzzer -- loop

# Run 50 iterations
cargo run --bin differential_fuzzer -- loop 50

Docker Runner (CI/Production)

# 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 issues
  • SLACK_WEBHOOK_URL - For notifications

Output Files

All 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)

Reproducing Errors

Always follow these steps

  1. Find the seed in the error output:

    INFO: Starting differential_fuzzer with config: SimConfig { seed: 12345, ... }
    
  2. Re-run with that seed:

    cargo run --bin differential_fuzzer -- --seed 12345 --verbose --keep-files
    
  3. Check output files:

    • simulator-output/test.sql - Find the failing statement (look for -- FAILED:)
    • simulator-output/schema.json - Check table structure at failure time
  4. Create a minimal reproducer

  5. Compare 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
    

Understanding Failures

Oracle Failure Types

  1. Row set mismatch - Turso returned different rows than SQLite
  2. Turso errored but SQLite succeeded - Turso rejected valid SQL
  3. SQLite errored but Turso succeeded - Turso accepted invalid SQL
  4. Schema mismatch - Tables/columns differ after DDL

Warning (non-fatal)

  • Unordered LIMIT mismatch - LIMIT without ORDER BY may return different valid rows

Key Source Files

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

Tracing

Set RUST_LOG for more detailed output:

RUST_LOG=debug cargo run --bin differential_fuzzer -- --seed 12345
how to use differential-fuzzer

How to use differential-fuzzer on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add differential-fuzzer
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/tursodatabase/turso --skill differential-fuzzer

The skills CLI fetches differential-fuzzer from GitHub repository tursodatabase/turso and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/differential-fuzzer

Reload or restart Cursor to activate differential-fuzzer. Access the skill through slash commands (e.g., /differential-fuzzer) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.744 reviews
  • Anika Martin· Dec 4, 2024

    We added differential-fuzzer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Dev Wang· Dec 4, 2024

    differential-fuzzer reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Liam Jain· Nov 23, 2024

    Keeps context tight: differential-fuzzer is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Liam Martinez· Oct 14, 2024

    differential-fuzzer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Oshnikdeep· Sep 21, 2024

    We added differential-fuzzer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Li Liu· Sep 21, 2024

    Solid pick for teams standardizing on skills: differential-fuzzer is focused, and the summary matches what you get after install.

  • Min Chawla· Sep 21, 2024

    differential-fuzzer has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Daniel Jackson· Sep 5, 2024

    Useful defaults in differential-fuzzer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Sakshi Patil· Sep 1, 2024

    Registry listing for differential-fuzzer matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Li Wang· Aug 24, 2024

    I recommend differential-fuzzer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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