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-promptgit 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-prompt)<a href="https://agentmods.dev/skills/mpsuesser/pi-effect-harness/effect-ai-prompt"><img src="https://agentmods.dev/badge/skills/mpsuesser/pi-effect-harness/effect-ai-prompt/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-prompt"><img src="https://agentmods.dev/badge/skills/mpsuesser/pi-effect-harness/effect-ai-prompt.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 | $0.00036 | $0.04366 |
| Opus 5 | $0.00018 | $0.02183 |
| Sonnet 5 | $0.00007 | $0.00873 |
| Haiku 4.5 | $0.00004 | $0.00437 |
Grade A, and why
effect-ai-prompt 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 — 735 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Effect AI Prompt Construction
Master the Effect AI Prompt API for building type-safe conversations with language models.
Import Patterns
CRITICAL: Always use namespace imports:
import * as Prompt from 'effect/unstable/ai/Prompt';
import * as Response from 'effect/unstable/ai/Response';
import { pipe } from 'effect';
When to Use This Skill
- Constructing messages for language model requests
- Building multi-turn conversation history
- Adding system instructions to prompts
- Integrating tool calls and results into conversations
- Converting streaming responses to prompt history
- Managing file/image attachments in messages
- Implementing custom chat interfaces
Conceptual Model
-- Message hierarchy
type Message = SystemMessage | UserMessage | AssistantMessage | ToolMessage
type Part =
| TextPart
| ReasoningPart
| FilePart
| ToolCallPart
| ToolResultPart
| ToolApprovalRequestPart
| ToolApprovalResponsePart
-- Composition
Prompt.make :: RawInput → Prompt
Prompt.concat :: (Prompt, RawInput) → Prompt
Prompt.setSystem :: (Prompt, String) → Prompt
-- History transformation
fromResponseParts :: ReadonlyArray<Response.Part> → Prompt
Message Types
Each message has role and content. Content is an array of Part objects.
System Messages
import * as Prompt from 'effect/unstable/ai/Prompt';
// String content only
const system = Prompt.makeMessage('system', {
content: 'You are a helpful assistant specialized in mathematics.'
});
// Shorthand constructor
const systemShorthand = Prompt.systemMessage({
content: 'You are a helpful assistant specialized in mathematics.'
});
// System message with options
const systemWithOptions = Prompt.makeMessage('system', {
content: 'You are an expert coder.',
options: {
anthropic: { cache_control: { type: 'ephemeral' } }
}
});
User Messages
// Text-only user message
const userText = Prompt.makeMessage("user", {
content: [
Prompt.makePart("text", { text: "What is 2+2?" })
]
})
// Shorthand constructor
const userShorthand = Prompt.userMessage({
content: [
Prompt.makePart("text", { text: "What is 2+2?" })
]
})
// Multimodal user message (text + file)
const userMultimodal = Prompt.makeMessage("user", {
content: [
Prompt.makePart("text", { text: "What's in this image?" }),
Prompt.makePart("file", {
mediaType: "image/jpeg",
fileName: "photo.jpg",
data: new Uint8Array([...])
})
]
})
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 · 735 lines · 36 tokens per session scan A 8398ce7e0f5f
effect-ai-prompt is a skill published in the GitHub repository mpsuesser/pi-effect-harness (24 stars, last pushed 3mo ago), licensed MIT. It adds 36 tokens to every session and 4,366 once invoked, about $0.0002 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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A sofagent plugin that loads company constraints through several prompt-building layers when the agent starts.
gpt-image-2-style-library
Choose GPT-Image2 / gpt-image-2 visual styles and industrial prompt templates from the awesome-gpt-image-2 style library. Use when an agent needs to create, rewrite, classify, or improve image-generation prompts with repository-backed templates, categories, style tags, scene tags, pitfalls, and example cases.
temprouter
Call tempRouter — a payable, end-to-end-encrypted LLM inference endpoint on MPP (Tempo). Pay per inference in pathUSD stablecoin AFTER your agent verifies (Intel DCAP) that the prompt runs inside a real Phala Intel TDX enclave that can't read it. Use when a prompt holds secrets/PII (API keys sk-…, private keys 0x……
llm-integration
LLM Integration: Anthropic Claude API, OpenAI, streaming, tool use, prompt caching, structured output, fallback strategies, cost optimization.
prompt-engineering
Prompt Engineering: Template patterns, chain-of-thought, few-shot learning, prompt versioning.