# Product Search

> Constraint-verified product hunting across retailers — hard spec in, dimension-verified shortlist out, with a floor on how many sources get checked before any "no match exists" verdict. Trigger with /product-search <item + constraints>, or when the user asks to find furniture, baby gear, home goods, or any physical product against real constraints ("find a coffee table under 48in that fits X", "hunt for a crib that works in the nursery").

- Skill: `davemaynard/product-search` (Agent Skill)
- Install (CLI): `npx skillmds@latest add davemaynard/product-search`
- Raw SKILL.md: https://api.skillmd.com/api/skills/davemaynard/product-search/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: davemaynard (https://skillmd.com/u/davemaynard)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/davemaynard/product-search

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# Product Search — the N-source sweep

**Requires:** web fetching. The retailer ladder escalates to a browser when a site
blocks plain fetches, so a Playwright MCP server (or any browser-automation tool)
makes the sweep far more reliable. Without one, record blocked sites as BLOCKED and
widen the retailer list instead.

Born from a real failure: "nothing matches" declared after two retailers; the
pushed-back sweep of fourteen found real candidates. This skill exists
so the first pass is the rigorous one.

## 1. Pin the spec before searching

- Split **hard constraints** (dimensions with tolerance, budget ceiling, must-have
  features, safety certs for baby gear) from **soft preferences** (style, material,
  color). Write both down first; the hard set decides pass/fail, the soft set ranks.
- Pull the user's *actual* context, not the aspirational version: if they keep a
  notes file of real measurements and standing constraints (a `MEASUREMENTS.md` or
  `CONSTRAINTS.md`: owned pieces, style registry), read it before inventing numbers.

## 2. The retailer ladder

- **Floor: 8 retailers checked before any verdict.** Default list, adjusted per
  category: Amazon, Target, Wayfair, IKEA, West Elm, CB2, Crate & Barrel, Article,
  AllModern, Pottery Barn (swap in category specialists — e.g. babylist/Carter's-tier
  for baby gear, REI-tier for outdoor).
- Escalation per site: WebFetch → Playwright (`browser_navigate` + `browser_evaluate`)
  when fetch is blocked → record as BLOCKED and move on. Known bot-blockers needing
  Playwright or worse: **Wayfair, RH, CB2**.
- Wide sweeps go to **parallel subagents** (2–3 retailers each, returning candidates
  as structured rows) — never a long serial crawl with no findings surfaced.
- Track every retailer's outcome: `candidates / none-fit / blocked`.

## 3. Verify before shortlisting

- Dimensions come from the **actual product page** (spec table), never a category
  listing or search snippet; note price, variant, and stock state.
- A candidate without verified dimensions doesn't make the table. Convert units once,
  carefully; compare against the tolerance, not the nominal number.

## 4. The verdict

- **Lead with the move**: best candidate first — one sentence, price, why it fits.
- Then the shortlist table: retailer · product · dims vs. constraint · price · link.
- Then the coverage log: N retailers checked, who blocked, who had nothing.
- "No match exists" is only sayable with the full coverage log attached — and even
  then it's "checked N, none fit; nearest misses were A (off by x″) and B (over
  budget by $y)", which gives the user something to relax rather than a dead end.