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 celigo/ai --skill configuring-ai-agentsgit clone --depth 1 https://github.com/celigo/aiWrote 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/celigo/ai/configuring-ai-agents)<a href="https://agentmods.dev/skills/celigo/ai/configuring-ai-agents"><img src="https://agentmods.dev/badge/skills/celigo/ai/configuring-ai-agents.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk warn
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 | $0.00064 | $0.05294 |
| Opus 5 | $0.00032 | $0.02647 |
| Sonnet 5 | $0.00013 | $0.01059 |
| Haiku 4.5 | $0.00006 | $0.00529 |
Grade A, and why
configuring-ai-agents 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 7d 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 — 320 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Configuring AI Agents
An AI agent is an LLM-powered import step that processes records through an AI model instead of writing them to an external system. Records flow in, the model processes them according to instructions, and structured output flows back into the pipeline.
AI agents handle four concerns:
- Prompt design -- the system instruction that defines the model's behavior, goals, and constraints (up to 50 KB). The prompt receives each record as context and must produce output that downstream steps can consume
- Structured output --
json_schemaoutput format forces the model to return data conforming to a JSON Schema, enabling reliable field extraction for mapping.textreturns free-form responses.blobreturns binary data (image generation) - Tool use -- the model can call web search, MCP server tools, Celigo Tool resources, or image generation during processing. Tools extend the model's capabilities beyond its training data
- Response mapping -- extract fields from the model's response back into the record for downstream steps. Configured on the flow's
pageProcessors[]entry, but planned when building the agent. The response is available via_json. Response mapping uses Transformation 1.0 syntax (extract/generate pairs)
AI agents do not require a _connectionId unless using BYOK (bring your own key). Without one, platform-managed credentials are used.
Used across flows, APIs, and tools.
Two Types of AI Import
AI Agent Imports
Invoke an LLM for classification, extraction, summarization, translation, or generation. Two providers:
- OpenAI (
provider: "openai") -- GPT models via the OpenAI Responses API. Supports reasoning effort control, structured JSON output, web search, MCP tools, Celigo Tools, and image generation. - Gemini (
provider: "gemini") -- Google Gemini models via LiteLLM proxy. Supports thinking config, Google Search grounding, URL context, file search, MCP tools, and Celigo Tools.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 320 lines · 64 tokens per session scan A 40447f45464e
configuring-ai-agents is a skill published in the GitHub repository celigo/ai (3 stars, last pushed 3d ago), licensed MIT. It adds 64 tokens to every session and 5,294 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 2026-08-31.
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