Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/pauljphilp/effectpatterns/effect-patterns-schedulingnpx skills add PaulJPhilp/EffectPatterns --skill effect-patterns-schedulinggit clone --depth 1 https://github.com/PaulJPhilp/EffectPatternsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00024 | $0.02636 |
| Opus 5 | $0.00012 | $0.01318 |
| Sonnet 5 | $0.00005 | $0.00527 |
| Haiku 4.5 | $0.00002 | $0.00264 |
Grade A, and why
effect-patterns-scheduling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 452 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Effect-TS Patterns: Scheduling
This skill provides 3 curated Effect-TS patterns for scheduling. Use this skill when working on tasks related to:
- scheduling
- Best practices in Effect-TS applications
- Real-world patterns and solutions
🟢 Beginner Patterns
Retry Failed Operations
Rule: Use Effect.retry with a Schedule to handle transient failures gracefully.
Good Example:
import { Effect, Schedule, Data } from "effect"
// ============================================
// 1. Define error types
// ============================================
class NetworkError extends Data.TaggedError("NetworkError")<{
readonly message: string
}> {}
class RateLimitError extends Data.TaggedError("RateLimitError")<{
readonly retryAfter: number
}> {}
class NotFoundError extends Data.TaggedError("NotFoundError")<{
readonly resource: string
}> {}
// ============================================
// 2. Simulate a flaky API call
// ============================================
let callCount = 0
const fetchData = Effect.gen(function* () {
callCount++
yield* Effect.log(`API call attempt ${callCount}`)
// Simulate intermittent failures
if (callCount < 3) {
return yield* Effect.fail(new NetworkError({ message: "Connection timeout" }))
}
return { data: "Success!", attempts: callCount }
})
// ============================================
// 3. Basic retry - fixed attempts
// ============================================
const withBasicRetry = fetchData.pipe(
Effect.retry(Schedule.recurs(5)) // Retry up to 5 times
)
// ============================================
// 4. Retry with delay
// ============================================
const withDelayedRetry = fetchData.pipe(
Effect.retry(
Schedule.spaced("500 millis").pipe(
Schedule.intersect(Schedule.recurs(5))
)
)
)
// ============================================
// 5. Retry only specific errors
// ============================================
const fetchWithErrors = (shouldFail: boolean) =>
Effect.gen(function* () {
if (shouldFail) {
// Randomly fail with different errors
const random = Math.random()
if (random < 0.5) {
return yield* Effect.fail(new NetworkError({ message: "Timeout" }))
} else if (random < 0.8) {
return yield* Effect.fail(new RateLimitError({ retryAfter: 1000 }))
} else {
return yield* Effect.fail(new NotFoundError({ resource: "user:123" }))
}
}
return "Data fetched!"
})
// Only retry network and rate limit errors, not NotFoundError
const retryTransientOnly = fetchWithErrors(true).pipe(
Effect.retry({
schedule: Schedule.recurs(3),
while: (error) =>
error._tag === "NetworkError" || error._tag === "RateLimitError",
})
)
// ============================================
// 6. Retry with exponential backoff
// ============================================
const withExponentialBackoff = fetchData.pipe(
Effect.retry(
Schedule.exponential("100 millis", 2).pipe( // 100ms, 200ms, 400ms...
Schedule.intersect(Schedule.recurs(5)) // Max 5 retries
)
)
)
// ============================================
// 7. Run and observe
// ============================================
const program = Effect.gen(function* () {
yield* Effect.log("Starting retry demo...")
// Reset counter
callCount = 0
const result = yield* withBasicRetry
yield* Effect.log(`Final result: ${JSON.stringify(result)}`)
})
Effect.runPromise(program)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 452 lines · 24 tokens per session scan A 747ee94b8151
effect-patterns-scheduling is a skill published in the GitHub repository PaulJPhilp/EffectPatterns (795 stars, last pushed 2mo ago), licensed MIT. It adds 24 tokens to every session and 2,636 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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