AI Quote Generator

Problem Background

Pricing quotes for hardware, software, engineering, or consulting projects are not just arithmetic; price evidence, item structure, and commercial wording often drift apart. Manual price checks, line-item splitting, and template reuse can miss items, and it is hard to test different bid price ranges under scoring weights.

How It Works

The skill breaks quote generation into reviewable steps:
- Inputs: define the target with parameters such as --product, --spec, --quantity, and --mode.
- Market anchoring: search recent price ranges using --freshness to ground unit prices and ranges.
- Item decomposition: split the offering into hardware, software, installation, debugging, training, support, and other reviewable lines.
- Mode handling: commercial produces a client quote, bid estimates an optimal range from --budget and --price-weight, and cost calculates item and total margins after --cost is supplied.
- Structured output: generate a market summary, line-item quote, fee rollup, margin analysis, or bid recommendation suitable for proposals or bid review packets.

Boundaries and Notes

It relies on search plus LLM reasoning, so market results depend on retrieval quality, time window, and regional pricing context. Cost margins reflect the supplied cost only and do not capture hidden delivery risk. Bid recommendations are range estimates, not a substitute for commercial, financial, or legal review.