Configure YOLO and strategic Agent Profiles
Configure custom agent profiles in Warp to control planning depth, autonomy, and execution speed — demonstrated with YOLO and Strategic examples.
This tutorial explains how Agent Profiles in Warp influence behavior, autonomy, and planning when coding with AI — demonstrated through the NFL Predictor app example.
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Define the project
I want to create an app that scrapes NFL data from the past decade, processes stats like team scores and player performance, and predicts future wins.
The prompt specifies:
- Data sources and constraints
- Dependencies and CLI commands
- Implementation details and deliverables
Role & Goal You are my AI coding copilot inside Warp. Create a production-ready Python project that ingests 2015–2025 NFL data to power future win projections. Specifically: acquire week-level player and team stats, acquire game schedules + final scores (to determine weekly winners), and assemble a clean analytics dataset I can build models on later. Prefer stable/public data sources over brittle HTML scraping. Where scraping is unavoidable, respect robots.txt, add rate-limiting, and make scraping pluggable/optional. Primary data sources: nflverse/nflreadr static files for weekly player stats and schedules (CSV/Parquet over HTTPS). Tech constraints: Python 3.11+, no notebooks in the main flow. Deterministic, idempotent pipelines. Strong typing (pydantic) + docstrings. Parquet as the storage format; small sample CSVs for quick checks. CLI via Typer (warp run … friendly). Logging (structlog), retry/backoff (tenacity), polite rate-limits. Zero secrets required for core pipeline. Deliverables: A fully initialized repo with the scaffold above. Implemented CLI + modules to download/ingest 2015–2025 data, compute/normalize fantasy PPR, produce winners by week, and write Parquet outputs. One sample run in the README showing commands and example output counts. If successful, run full 2015–2025. Print a summary table (by season: games, players, weeks) at the end. -
Configure the Strategic Agent
Base Model: GPT‑5 (for reasoning)
Planning Model: Claude 4 Opus (for detailed breakdowns)Action Permission Apply code diffs Agent decides Read files Agent decides Create plans Always allow Execute commands Always ask Behavior:
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The agent starts by asking clarifying questions:
“Do you want me to scrape both player stats and schedules or just one first?”
“Where should raw data be stored — locally or in a database?” -
It builds a 14-step plan covering setup, dependencies, validation modules, and pipelines.
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When the agent requests NFL schedule URLs, the chosen source returns 404 errors.
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Execution halts — showing that the Strategic profile prioritizes verification over progress.
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Configure the YOLO Agent
Permissions:
Action Permission Apply diffs / read files Always allow Create plans Never Execute commands Always allow Behavior:
- The YOLO agent skips detailed planning.
- It produces a 10-step plan that covers essentials only:
- Initialize project
- Build CLI
- Ingest player data
- Compute scores and transformations
- Instead of using unstable schedule URLs, it focuses on reliable player endpoints — completing a functional data pipeline.
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Compare outcomes
| Aspect | Strategic Agent | YOLO Agent |
|---|---|---|
| Planning | Detailed (14 steps) | Minimal (10 steps) |
| Interaction | Clarifications required | Autonomous |
| Speed | Slower due to checks | Faster iteration |
| Output | Stalled on invalid URLs | Working player dataset + summary table |