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Use with AI agents

agentl pipelines are plain YAML plus small Python functions, which makes them easy for coding agents (Claude Code, Cursor, Copilot, ChatGPT) to write and review.

Give the agent the docs

  • llms.txt: index of every page, following llmstxt.org.
  • llms-full.txt: the whole documentation in one file. Paste it or point the agent at the URL.
  • AGENTS.md: conventions for agents that work on agentl itself.

In your own project, add a line to your CLAUDE.md or AGENTS.md:

Data pipelines use agentl (https://agentl.com.br/llms-full.txt). Validate with `agentl validate <file>`.

Prompts

Copy, fill the parts in <angle brackets>, and paste. Each prompt asks the agent to run agentl validate, so mistakes surface before the first run.

Migrate a pandas script

Migrate this pandas script to an agentl pipeline. Docs: https://agentl.com.br/llms-full.txt

Rules:
- Keep my transform logic in pandas, inside a function decorated with
  agentl.pandas.dataframe, in steps.py next to the pipeline.
- Replace pd.read_* with agentl reader connectors (csv.read, json.read, excel.read, file.read)
  and DataFrame.to_sql with postgres.write (append, replace, upsert) or sqlite.write (append,
  replace). Use mode: upsert when rows have
  a natural key, and mode: replace for aggregates that are recomputed in full.
- Readers return CSV values as strings: cast types inside the function.
- Credentials only as ${ENV_VAR}.
- Run `agentl validate pipeline.yaml` (export placeholder values for ${VARS}) and fix any error.

<paste the script>

Create a pipeline from a description

Write an agentl pipeline (pipeline.yaml) that <reads X from Y>, <transforms it how>
and <writes to Z>. Docs: https://agentl.com.br/llms-full.txt

Use built-in connectors where they exist (`agentl connectors` lists them). Put custom
Python in steps.py and reference it as `fn: steps:<name>`. Secrets only as ${ENV_VAR}.
Relative paths resolve from the directory where agentl runs.
Add a notify rule for run.failed. Run `agentl validate pipeline.yaml` and
`agentl diagram pipeline.yaml`, then show me both outputs.

Add an agent step that validates a column

In <pipeline.yaml>, add an agent step after <step id> that checks the column <name>
and flags records that look wrong. Docs: https://agentl.com.br/agents/

- Declare an `output:` schema with the original key, the checked value, `valid: bool`
  and `reason: str?`.
- Write the instruction as a short, specific task, with one example of a bad value.
- Keep the provider in `llm:` and start with `provider: echo` so I can test offline. Echo cannot
  invent values, so make the new fields optional (`bool?`, `str?`) for the offline run.
- Run `agentl validate` and `agentl run --show <new step id>`.

Review a pipeline

Review <pipeline.yaml> against the agentl docs (https://agentl.com.br/llms-full.txt).
Look for: secrets written in plain text, writes that duplicate rows on re-run (append without
a key), agent steps without an output schema, steps that could be a built-in connector instead
of custom code, and missing failure notifications. List findings, most severe first.

Tips

  • Ask for agentl validate and agentl diagram in every prompt: they are cheap and catch most mistakes.
  • Keep agent steps for the fuzzy parts (classify, extract, normalize free text). Deterministic work belongs in connectors and functions, where it is faster and free.
  • Review generated YAML like any code before it touches production data.