Effect-TS is a functional TypeScript library providing typed effects, structured concurrency, and a robust runtime. This skill covers correct usage patterns and addresses common misconceptions from LLM-generated content.
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node --versioneffect-tsExecute the skills CLI command in your project's root directory to begin installation:
Fetches effect-ts from kastalien-research/thoughtbox-dot-claude and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate effect-ts. Access via /effect-ts in your agent's command palette.
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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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Effect-TS is a functional TypeScript library providing typed effects, structured concurrency, and a robust runtime. This skill covers correct usage patterns and addresses common misconceptions from LLM-generated content.
import { Effect, Layer, Context, Fiber, Schedule, Cache, Scope } from "effect";
import { Schema, JSONSchema } from "@effect/schema";
Core Type Signature:
Effect<Success, Error, Requirements>
// ↑ ↑ ↑
// | | └── Dependencies (provided via Layers)
// | └── Expected errors (typed, must be handled)
// └── Success value
LLM outputs often contain incorrect APIs. Use this table to correct them:
| Wrong (common in AI outputs) | Correct |
|---|---|
Effect.cachedWithTTL(...) |
Cache.make({ timeToLive: Duration }) |
Effect.cachedInvalidateWithTTL(...) |
cache.invalidate(key) / cache.invalidateAll() |
Effect.match(...) |
Effect.either + Either.match, or Effect.catchTag |
| "thread-local storage" | "fiber-local storage" via FiberRef |
| JSON Schema Draft 2020-12 | @effect/schema generates Draft-07 |
| fibers are "cancelled" | fibers are "terminated" or "interrupted" |
| all queues have back-pressure | only bounded queues; sliding/dropping do not |
--only=production |
--omit=dev (npm 7+) |
Effect distinguishes between:
E channel, must be handled// Expected error - typed
const fetchUser = (id: string): Effect.Effect<User, UserNotFoundError | NetworkError> => ...
// Handle expected errors
const handled = pipe(
fetchUser("123"),
Effect.catchTag("UserNotFoundError", (e) => Effect.succeed(defaultUser)),
Effect.catchTag("NetworkError", (e) => Effect.retry(schedule))
);
// Unexpected errors (defects) - captured by runtime
Effect.catchAllDefect(program, (defect) =>
Console.error("Unexpected error", defect)
);
Fibers are lightweight virtual threads with native interruption:
// Fork a fiber
const fiber = yield* Effect.fork(longRunningTask);
// Interrupt it (e.g., when MCP client disconnects)
yield* Fiber.interrupt(fiber);
// Structured concurrency: child fibers auto-terminate with parent
const parent = Effect.gen(function* () {
yield* Effect.fork(backgroundTask); // Auto-interrupted when parent ends
yield* mainTask;
});
// Daemon fibers outlive their parent
yield* Effect.forkDaemon(longLivedBackgroundTask);
// First to succeed wins; other is automatically interrupted
const result = yield* Effect.race(
fetchFromCache,
fetchFromDatabase
);
// Process 50 documents with max 5 concurrent
const results = yield* Effect.all(documents.map(processDoc), {
concurrency: 5 // NOT a "worker pool" - limits concurrent tasks
});
// Bounded - applies back-pressure (offer suspends when full)
const bounded = yield* Queue.bounded<string>(100);
// Dropping - discards new items when full (no back-pressure)
const dropping = yield* Queue.dropping<string>(100);
// Sliding - discards oldest items when full (no back-pressure)
const sliding = yield* Queue.sliding<string>(100);
Layers construct services without leaking dependencies:
// Define a service
class Database extends Context.Tag("Database")<
Database,
{ query: (sql: string) => Effect.Effect<Result> }
>() {}
// Create layer (dependencies handled at construction)
const DatabaseLive = Layer.effect(
Database,
Effect.gen(function* () {
const config = yield* Config; // Dependency injected here
return {
query: (sql) => Effect.tryPromise(() => runQuery(sql, config))
};
})
);
// Provide to program
const runnable = program.pipe(Effect.provide(DatabaseLive));
// For testing - swap implementation
const DatabaseTest = Layer.succeed(Database, {
query: () => Effect.succeed(mockResult)
});
const program = pipe(
Effect.tryPromise(() => openConnection()),
Effect.ensuring(Console.log("Cleanup")) // Runs on success, failure, OR interrupt
);
const withConnection = Effect.acquireUseRelease(
Effect.tryPromise(() => db.connect()), // Acquire
(conn) => Effect.tryPromise(() => conn.query("SELECT *")), // Use
(conn) => Effect.promise((✓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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
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4.6★★★★★75 reviews- WWilliam Sharma★★★★★Dec 16, 2024
effect-ts is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- RRen Rao★★★★★Dec 16, 2024
effect-ts reduced setup friction for our internal harness; good balance of opinion and flexibility.
- HHana Dixit★★★★★Dec 4, 2024
Registry listing for effect-ts matched our evaluation — installs cleanly and behaves as described in the markdown.
- RRahul Santra★★★★★Nov 23, 2024
I recommend effect-ts for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- HHana Kapoor★★★★★Nov 23, 2024
Keeps context tight: effect-ts is the kind of skill you can hand to a new teammate without a long onboarding doc.
- SSoo Bansal★★★★★Nov 19, 2024
I recommend effect-ts for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- SSophia Gill★★★★★Nov 15, 2024
Useful defaults in effect-ts — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- NNoah Gupta★★★★★Nov 15, 2024
I recommend effect-ts for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- KKaira Khanna★★★★★Nov 7, 2024
effect-ts reduced setup friction for our internal harness; good balance of opinion and flexibility.
- NNoor Agarwal★★★★★Nov 7, 2024
effect-ts is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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