async-io-model▌
tursodatabase/turso · updated Apr 8, 2026
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Cooperative async patterns using explicit state machines, completions, and re-entrancy safeguards for Turso's I/O model.
- ›Core types: IOResult<T> (returns Done or IO requiring re-call) and Completion for tracking individual operations
- ›CompletionGroup aggregates multiple completions into one, with nesting and cancellation support
- ›State machine pattern encodes progress in enum variants to safely handle re-entry across yield points
- ›Critical pitfall: mutating shared state before y
Async I/O Model Guide
Turso uses cooperative yielding with explicit state machines instead of Rust async/await.
Core Types
pub enum IOCompletions {
Single(Completion),
}
#[must_use]
pub enum IOResult<T> {
Done(T), // Operation complete, here's the result
IO(IOCompletions), // Need I/O, call me again after completions finish
}
Functions returning IOResult must be called repeatedly until Done.
Completion and CompletionGroup
A Completion tracks a single I/O operation:
pub struct Completion { /* ... */ }
impl Completion {
pub fn finished(&self) -> bool;
pub fn succeeded(&self) -> bool;
pub fn get_error(&self) -> Option<CompletionError>;
}
To wait for multiple I/O operations, use CompletionGroup:
let mut group = CompletionGroup::new(|_| {});
// Add individual completions
group.add(&completion1);
group.add(&completion2);
// Build into single completion that finishes when all complete
let combined = group.build();
io_yield_one!(combined);
CompletionGroup features:
- Aggregates multiple completions into one
- Calls callback when all complete (or any errors)
- Can nest groups (add a group's completion to another group)
- Cancellable via
group.cancel()
Helper Macros
return_if_io!
Unwraps IOResult, propagates IO variant up the call stack:
let result = return_if_io!(some_io_operation());
// Only reaches here if operation returned Done
io_yield_one!
Yields a single completion:
io_yield_one!(completion); // Returns Ok(IOResult::IO(Single(completion)))
State Machine Pattern
Operations that may yield use explicit state enums:
enum MyOperationState {
Start,
WaitingForRead { page: PageRef },
Processing { data: Vec<u8> },
Done,
}
The function loops, matching on state and transitioning:
fn my_operation(&mut self) -> Result<IOResult<Output>> {
loop {
match &mut self.state {
MyOperationState::Start => {
let (page, completion) = start_read();
self.state = MyOperationState::WaitingForRead { page };
io_yield_one!(completion);
}
MyOperationState::WaitingForRead { page } => {
let data = page.get_contents();
self.state = MyOperationState::Processing { data: data.to_vec() };
// No yield, continue loop
}
MyOperationState::Processing { data } => {
let result = process(data);
self.state = MyOperationState::Done;
return Ok(IOResult::Done(result));
}
MyOperationState::Done => unreachable!(),
}
}
}
Re-Entrancy: The Critical Pitfall
State mutations before yield points cause bugs on re-entry.
Wrong
fn bad_example(&mut self) -> Result<IOResult<()>> {
self.counter += 1; // Mutates state
return_if_io!(something_that_might_yield()); // If yields, re-entry will increment again!
Ok(IOResult::Done(()))
}
If something_that_might_yield() returns IO, caller waits for completion, then calls bad_example() again. counter gets incremented twice (or more).
Correct: Mutate After Yield
fn good_example(&mut self) -> Result<IOResult<()>> {
return_if_io!(something_that_might_yield());
self.counter += 1; // Only reached once, after IO completes
Ok(IOResult::Done(()))
}
Correct: Use State Machine
enum State { Start, AfterIO }
fn good_example(&mut self) -> Result<IOResult<()>> {
loop {
match self.state {
State::Start => {
// Don't mutate shared state here
self.state = State::AfterIO;
return_if_io!(something_that_might_yield());
}
State::AfterIO => {
self.counter += 1; // Safe: only entered once
return Ok(IOResult::Done(()));
<How to use async-io-model on Cursor
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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 async-io-model
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches async-io-model 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 async-io-model. Access the skill through slash commands (e.g., /async-io-model) 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
Submit your Claude Code skill and start earning
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
4.6★★★★★37 reviews- ★★★★★James White· Dec 28, 2024
async-io-model has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Amina Torres· Dec 20, 2024
Useful defaults in async-io-model — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Nikhil Torres· Dec 16, 2024
async-io-model reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Isabella Tandon· Nov 19, 2024
Useful defaults in async-io-model — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Daniel Jain· Nov 7, 2024
Registry listing for async-io-model matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★James Anderson· Oct 26, 2024
async-io-model fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Isabella Thompson· Oct 10, 2024
async-io-model is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Xiao Perez· Oct 2, 2024
Keeps context tight: async-io-model is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Aditi Martin· Sep 9, 2024
Registry listing for async-io-model matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Oshnikdeep· Sep 5, 2024
Useful defaults in async-io-model — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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