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 skills add mpsuesser/pi-effect-harness --skill effect-ai-providergit clone --depth 1 https://github.com/mpsuesser/pi-effect-harnessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mpsuesser/pi-effect-harness/effect-ai-provider)<a href="https://agentmods.dev/skills/mpsuesser/pi-effect-harness/effect-ai-provider"><img src="https://agentmods.dev/badge/skills/mpsuesser/pi-effect-harness/effect-ai-provider/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mpsuesser/pi-effect-harness/effect-ai-provider"><img src="https://agentmods.dev/badge/skills/mpsuesser/pi-effect-harness/effect-ai-provider.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00052 | $0.04781 |
| Opus 5 | $0.00026 | $0.02390 |
| Sonnet 5 | $0.00010 | $0.00956 |
| Haiku 4.5 | $0.00005 | $0.00478 |
Grade A, and why
effect-ai-provider 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 10d 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 — 630 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Effect AI Provider
Configure AI provider layers for language model integration using Effect's AI ecosystem.
When to Use This Skill
Use this skill when:
- Integrating AI language models (Anthropic, OpenAI, OpenRouter, etc.) into Effect applications
- Setting up multi-provider AI architectures with ExecutionPlan fallback
- Implementing stateful chat conversations with context history
- Managing AI provider configuration and API keys securely
- Composing AI capabilities with other Effect services
Import Patterns
CRITICAL: Always use namespace imports. Use { } destructured imports for the effect package barrel exports.
// From the "effect" barrel — destructured
import {
Config,
Effect,
ExecutionPlan,
Layer,
Ref,
Schema,
Context,
Stream
} from 'effect';
// From "effect/unstable/ai" — namespace imports
import {
AiError,
Chat,
LanguageModel,
Model,
Prompt,
Tool,
Toolkit
} from 'effect/unstable/ai';
// Or individually:
import * as LanguageModel from 'effect/unstable/ai/LanguageModel';
import * as Chat from 'effect/unstable/ai/Chat';
import * as Model from 'effect/unstable/ai/Model';
import * as Prompt from 'effect/unstable/ai/Prompt';
import * as AiError from 'effect/unstable/ai/AiError';
// Anthropic
import { AnthropicClient, AnthropicLanguageModel } from '@effect/ai-anthropic';
// OpenAI
import {
OpenAiClient,
OpenAiClientGenerated,
OpenAiLanguageModel,
OpenAiSchema,
OpenAiTool
} from '@effect/ai-openai';
// OpenRouter
import {
OpenRouterClient,
OpenRouterLanguageModel
} from '@effect/ai-openrouter';
// HTTP client (required by all providers)
import { FetchHttpClient } from 'effect/unstable/http';
Provider Layer Pattern
Every provider exposes two constructors:
model(modelId, config?)— returns aModel.Model(preferred forExecutionPlanandEffect.provide)layer({ model, config? })— returns a rawLayer<LanguageModel.LanguageModel, never, Client>
-- Model constructor (preferred)
ProviderLanguageModel.model :: (modelId, config?) → Model.Model<providerName, LanguageModel, Client>
-- Layer constructor
ProviderLanguageModel.layer :: { model, config? } → Layer LanguageModel Client
-- Client layer
ProviderClient.layerConfig :: { apiKey } → Layer Client HttpClient
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.
- 10d ago First seen · 630 lines · 52 tokens per session scan A 25f63bb06722
effect-ai-provider is a skill published in the GitHub repository mpsuesser/pi-effect-harness (24 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 4,781 once invoked, about $0.0003 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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Effect-TS patterns for Concurrency. Use when working with concurrency in Effect-TS applications.