project-development_skill

Overview

This skill helps you plan and execute LLM-powered projects, from validating task-model fit to designing batch pipelines and agent architectures. It provides a practical methodology for prototyping, staging pipelines, cost estimation, and iterative agent-assisted development. Use it to choose between single-agent and multi-agent approaches and to structure projects for repeatable, debuggable runs.

How this skill works

It starts with a manual prototype: test one representative input against the target model to validate capability and output format. If the prototype succeeds, design a staged pipeline (acquire → prepare → process → parse → render) where the LLM stage is isolated and expensive. Use the file system as the state machine for idempotency and caching. Iterate with agent-assisted development to generate, test, and refine components while estimating token costs and monitoring failures.

When to use it

• Starting a new project that might use LLMs
• Evaluating whether a task fits an LLM vs traditional code
• Designing batch pipelines with structured outputs
• Choosing single-agent or multi-agent architectures
• Estimating costs and timelines for LLM-heavy work

Best practices

• Always run a manual prototype on one representative example before automating
• Structure pipelines as discrete, idempotent, cacheable stages
• Use the file system for per-item state and easy debugging
• Specify exact output formats with examples and build tolerant parsers
• Start with minimal architecture and add complexity only when necessary
• Estimate tokens and costs early; include a buffer for retries

Example use cases

• Batch analysis pipeline: fetch datasets, prepare prompts, run LLM calls, parse structured JSON, render reports
• Agent-assisted development: ask an agent to scaffold components, test failures, and iterate on prompts and parsers
• Architectural reduction: replace many specialized tools with a minimal set (e.g., shell + SQL) to simplify maintenance
• Multi-agent pattern: split a large task into sub-agents when context windows or specialization are required
• Cost-tracking for large runs: estimate tokens per item, multiply by item count, and monitor actual spend