What Should I Ride — Data skill for Claude Code
Bicycle tire recommendation engine built on a two-stage Claude reasoning pipeline.
How to install What Should I Ride
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open ericcgladstone-maker/what-should-i-ride and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What What Should I Ride does
Bicycle tire recommendation engine built on a two-stage Claude reasoning pipeline. Returns ranked recommendations with per-tire confidence scoring, a paired front and rear suggestion, and a mechanic memo.
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README
What Should I Ride?
An AI-powered bicycle tire recommendation engine built on a two-stage Claude reasoning pipeline. Riders describe their situation in natural language; the system returns structured, ranked recommendations with per-tire confidence scoring, a paired front/rear suggestion, and a plain-text mechanic memo.
Live demo: deployed as a Netlify static site — no server, no database, no build step.
Architecture Overview
The application is a single React 18 component rendered from a modular file structure. No JSX, no bundler, no transpilation. It deploys by dropping a folder.
index.html Entry point — loads dependencies and modules in order
css/main.css All styles. CSS variables in :root for easy theming.
js/config.js Constants: API config, image paths, option lists, PRODUCT_CATEGORY
js/prompts.js The four AI system prompts — auditable and editable independently
js/archetypes.js 12 buyer archetypes, form configs, carousel prompt library
js/helpers.js Stateless utilities and sub-components (no side effects)
js/app.js React App component — all state and rendering
vendor/ React 18.2.0 and ReactDOM 18.2.0 UMD production builds (vendored locally)
images/ 16 static assets (hero images + archetype tiles)
Load order matters: each file depends on variables declared before it. `app.js` is last and calls `ReactDOM.createRoot` to mount.
React and ReactDOM are vendored locally in `vendor/` — no CDN dependency at runtime.
The AI Pipeline
Every recommendation runs two sequential API calls to Claude.
Stage 1 — Diagnostic Pre-Pass (`DIAG_PROMPT`)
Receives raw rider input. Returns structured JSON:
| Field | Purpose |
|---|---|
realProblem |
The actual mechanical/performance problem, or decision type |
characteristicVector |
3-5 tire characteristics needed, in priority order |
surfacePrior |
What named location/trail/race implies about terrain and local tir |
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