transaction-correctness▌
tursodatabase/turso · updated Apr 8, 2026
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WAL mechanics, checkpointing, concurrency, and recovery in Turso's SQLite implementation.
- ›Turso uses WAL (Write-Ahead Logging) exclusively with in-memory WAL index instead of SQLite's shared memory file, supporting one writer and concurrent readers without blocking
- ›Checkpointing transfers WAL pages back to the main database in four modes: PASSIVE (non-blocking), FULL (waits for readers), RESTART (resets WAL), and TRUNCATE (truncates WAL file)
- ›Each connection maintains private page ca
Transaction Correctness Guide
Turso uses WAL (Write-Ahead Logging) mode exclusively.
Files: .db, .db-wal (no .db-shm - Turso uses in-memory WAL index)
WAL Mechanics
Write Path
- Writer appends frames (page data) to WAL file (sequential I/O)
- COMMIT = frame with non-zero db_size in header (marks transaction end)
- Original DB unchanged until checkpoint
Read Path
- Reader acquires read mark (mxFrame = last valid commit frame)
- For each page: check WAL up to mxFrame, fall back to main DB
- Reader sees consistent snapshot at its read mark
Checkpointing
Transfers WAL content back to main DB.
WAL grows → checkpoint triggered (default: 1000 pages) → pages copied to DB → WAL reused
Checkpoint types:
- PASSIVE: Non-blocking, stops at pages needed by active readers
- FULL: Waits for readers, checkpoints everything
- RESTART: Like FULL, also resets WAL to beginning
- TRUNCATE: Like RESTART, also truncates WAL file to zero length
WAL-Index
SQLite uses a shared memory file (-shm) for WAL index. Turso does not - it uses in-memory data structures (frame_cache hashmap, atomic read marks) since multi-process access is not supported.
Concurrency Rules
- One writer at a time
- Readers don't block writer, writer doesn't block readers
- Checkpoint must stop at pages needed by active readers
Recovery
On crash:
- First connection acquires exclusive lock
- Replays valid commits from WAL
- Releases lock, normal operation resumes
Turso Implementation
Key files:
- WAL implementation - WAL implementation
- Page management, transactions
Connection-Private vs Shared
Per-Connection (private):
Pager- page cache, dirty pages, savepoints, commit stateWalFile- connection's snapshot view:max_frame/min_frame- frame range for this connection's snapshotmax_frame_read_lock_index- which read lock slot this connection holdslast_checksum- rolling checksum state
Shared across connections:
WalFileShared- global WAL state:frame_cache- page-to-frame index (replaces.shmfile)max_frame/nbackfills- global WAL progressread_locks[5]- read mark slots (TursoRwLock with embedded frame values)write_lock- exclusive writer lockcheckpoint_lock- checkpoint serializationfile- WAL file handle
DatabaseStorage- main.dbfileBufferPool- shared memory allocation
Correctness Invariants
- Durability: COMMIT record must be fsynced before returning success
- Atomicity: Partial transactions never visible to readers
- Isolation: Each reader sees consistent snapshot
- No lost updates: Checkpoint can't overwrite uncommitted changes
References
How to use transaction-correctness on Cursor
AI-first code editor with Composer
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 transaction-correctness
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches transaction-correctness from GitHub repository tursodatabase/turso and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate transaction-correctness. Access the skill through slash commands (e.g., /transaction-correctness) 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.
List & Monetize Your Skill
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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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★30 reviews- ★★★★★Zara Harris· Dec 28, 2024
transaction-correctness has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Dhruvi Jain· Dec 16, 2024
Registry listing for transaction-correctness matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Kiara Taylor· Dec 8, 2024
Solid pick for teams standardizing on skills: transaction-correctness is focused, and the summary matches what you get after install.
- ★★★★★Lucas Brown· Nov 27, 2024
Registry listing for transaction-correctness matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Oshnikdeep· Nov 7, 2024
Solid pick for teams standardizing on skills: transaction-correctness is focused, and the summary matches what you get after install.
- ★★★★★Ganesh Mohane· Oct 26, 2024
I recommend transaction-correctness for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kiara Johnson· Oct 18, 2024
Useful defaults in transaction-correctness — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Amina Sharma· Sep 25, 2024
transaction-correctness is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Kiara Brown· Sep 21, 2024
Solid pick for teams standardizing on skills: transaction-correctness is focused, and the summary matches what you get after install.
- ★★★★★Sakshi Patil· Sep 5, 2024
Keeps context tight: transaction-correctness is the kind of skill you can hand to a new teammate without a long onboarding doc.
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