Anvia Pipeline Skill

Use this skill when the work is a fixed sequence of steps, not a model-driven loop: transforms, parsing, scoring, fan-out/fan-in, batch runs, or pipelines with one bounded agent or extractor stage inside.

Pipeline vs agent

• Reach for Pipeline when you can name the steps upfront. Reach for Agent (see the anvia-agent skill) when the model must decide what to do next.
• The common failure is an agent with rising maxTurns doing a fixed sequence — rewrite that as a pipeline with one .agent() stage instead.

Process

1. Model the flow as stages (references/steps-compose.md) — step, compose, parallel, batch.
2. Add model stages only where judgment is needed (references/agents-extract.md).
3. Run scripts/check-pipeline.sh from the app root before claiming done.

Minimal slice

import { Pipeline } from "@anvia/core/pipeline";
import { z } from "zod";

const normalizeIncident = new Pipeline({ id: "normalize-incident", inputSchema: z.string() })
  .step({ id: "trim", run: ({ input }) => input.trim() })
  .step({ id: "collapse-whitespace", run: ({ input }) => input.replace(/\s+/g, " ") })
  .step({
    id: "summarize",
    run: ({ input }) => ({
      normalized: input,
      wordCount: input.split(" ").length,
      priority: input.toLowerCase().includes("outage") ? "high" : "normal",
    }),
  });

const result = await normalizeIncident.run({ input: "  Checkout outage reported.  " });
console.log(result.output);

Output

Keep stages small, named, and typed — each stage's output is the next stage's input. Point to the relevant reference file instead of pasting its contents into chat.