Cooperative async patterns using explicit state machines, completions, and re-entrancy safeguards for Turso's I/O model.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionasync-io-modelExecute the skills CLI command in your project's root directory to begin installation:
Fetches async-io-model 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 async-io-model. Access via /async-io-model 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
0
total installs
0
this week
18.1K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
18.1K
stars
Turso uses cooperative yielding with explicit state machines instead of Rust async/await.
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.
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:
group.cancel()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)))
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!(),
}
}
}
State mutations before yield points cause bugs on re-entry.
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).
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(()))
}
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(()));
<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
pproenca/dot-skills
mattpocock/skills
async-io-model has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in async-io-model — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
async-io-model reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in async-io-model — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for async-io-model matched our evaluation — installs cleanly and behaves as described in the markdown.
async-io-model fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
async-io-model is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: async-io-model is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for async-io-model matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in async-io-model — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
showing 1-10 of 37