# Analyzing Dtc Stores > Use when the user provides a DTC or ecommerce store URL and asks for a teardown, breakdown, brand analysis, competitor teardown, investor memo, store audit, deep dive, or 'what's going on with [brand]'. Produces an investor-grade markdown teardown report covering brand, market, unit economics, supply chain, channel mix, marketing, reviews, agentic-commerce readiness, risks, and a falsifiable verdict. Triggers: 'dtc teardown', 'brand teardown', 'store teardown', 'competitor teardown', 'analyze this store', 'investor memo on [brand]', 'break down [store url]'. Do NOT use for SEO-only audits, design-system extraction, lead-gen scraping, or general web scraping with no brand/investor focus. - Skill: `agent-engineer-master/analyzing-dtc-stores` (Agent Skill, multi-file: 22 files) - Install (CLI): `npx skillmds@latest add agent-engineer-master/analyzing-dtc-stores` - Raw SKILL.md: https://api.skillmd.com/api/skills/agent-engineer-master/analyzing-dtc-stores/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: Agent-Engineer-Master (https://skillmd.com/u/agent-engineer-master) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/agent-engineer-master/analyzing-dtc-stores --- # Analyzing DTC Stores Produces an investor-grade teardown of a DTC brand from its public URL. Read-only — never writes to the store, sends email, or posts anywhere. ## Inputs - **Required:** `store_url` — brand's primary storefront. - **Optional:** `depth` — `quick` | `standard` (default) | `deep`. - **Optional:** `focus` — free-text bias (e.g. "supply chain", "acquisition target fit"). - **Optional:** `--no-save` — return the report inline and skip saving. ## Process ### Step 1 — Intake 1. Parse `store_url`, `depth`, `focus`, `--no-save`. 2. Derive `brand_slug` from the domain (e.g. `brandname.com` → `brand-name`). 3. Copy `assets/report-template.md` to a working draft. Do not save to the final path yet. ### Step 2 — Recon sweep (scripted) Run `scripts/recon.py --url ` to fetch homepage, robots.txt, sitemap, JSON-LD, and detect platform + apps (Shopify/Klaviyo/Gorgias/Recharge/Triple Whale signatures). Output cached in `.cache/recon-[slug].json`. Feeds the Tech Stack and Agentic Readiness sections. ### Step 3 — Fan-out research (scripted + reasoning) Load `references/sources-playbook.md` — required reading, it maps each report section to prescribed sources. Run every applicable script. Fail gracefully: if a source is unreachable, log `[source unavailable]` in the report's Sources section and continue — never halt. **Mandatory scripts (all depths):** - `scripts/meta_ad_library.py --brand ` — active ad count, creative lifespan distribution. - `scripts/importyeti_lookup.py --brand ` — supplier + country-of-origin + shipment volume. **Mandatory — the #1 skipped source.** - `scripts/similarweb_lookup.py --domain ` — traffic with the 50K/mo accuracy floor flag. - `scripts/store_leads_lookup.py --domain ` — Shopify plan, app stack, revenue bracket. - `scripts/reviews_scan.py --brand --domain ` — Trustpilot + Amazon + YouTube review URLs. - `scripts/reddit_search.py --brand ` — Reddit sentiment via OpenAI web search (requires OPENAI_API_KEY). Returns structured JSON: thread_count, sentiment, top_praise_theme, top_complaint_theme. "0 threads" is a valid finding — log it in §10. **Additional for `standard` and `deep`:** - `scripts/amazon_bsr.py --brand ` — BSR + est. monthly unit sales for top SKUs. For the reasoning steps (brand story, competitive strategic group, verdict framing) use WebSearch + WebFetch directly. ### Step 4 — Unit economics Read `references/unit-economics-benchmarks.md` for category COGS / CAC / LTV ranges. Run `scripts/unit_econ.py --category --aov --cac ` to produce a CM1 → CM2 → CM3 waterfall. Every figure must carry stated assumptions and a `~ (est., method: …)` prefix. ### Step 5 — Draft the report Fill the 16-section template in order. Principles: - **Prefer "Insufficient public data" over speculation.** A 2-line section with the sources checked beats 8 lines of filler. - **Cite inline** with `[1][2]` — the Sources section at the end is the numbered index. - **Every numeric claim is either cited or prefixed `~` with method shown.** Non-negotiable. - **Competitors must come from independent discovery**, not the brand's own "vs" page. If the brand lists competitors on its site, exclude them until resurfaced via search intent or category overlap. - **Show the CM waterfall, not just gross margin.** Apparel especially — returns collapse CM2. - **SimilarWeb under 50K/mo = directional only.** Quote a range, not a point estimate; triangulate with ad count + review velocity + headcount. For `depth=deep`: after the draft, present three alternative verdict framings (bull, bear, contrarian) and let the user pick before finalising. ### Step 6 — Self-check (scripted, mandatory) Run `scripts/validate_report.py --path `. The script fails loud with a remediation list if: - Any of the 16 sections is missing or empty. - Any numeric claim is unsourced and not prefixed `~`. - The Sources section is empty or has fewer than 5 entries. - The Verdict section lacks a numerical "what would change my mind" anchor. - Any banned hype word appears (`revolutionary`, `disruptive`, `game-changing`, `cutting-edge`, `seamless`, `unparalleled`). - The ImportYeti check is missing from the Sources section (even a "no records found" entry counts). Fix every failure and re-run. Do not proceed to Step 7 until the validator exits 0. ### Step 7 — Save + return Run `scripts/save_report.py --path --slug ` (honors `--no-save`). Returns the final path. Then output to chat: final path + a 5-bullet executive summary (≤120 words total). ### Step 8 — Closing feedback gate Ask the user exactly once: *"Any corrections to this teardown before I log it?"* Route the response: - Behavioural feedback (tone, framing, depth preference) → append to `references/learnings.md`. - Factual exception (platform quirk, blocked source) → append to `references/edge-cases.md`. - "Never again" → add a rule to this SKILL.md. - Approval with no changes → copy final report to `assets/approved-examples/`. ## Report structure (16 sections, exact order) 0. Research confidence & source map 1. Executive summary (5 bullets, ≤120 words) 2. Brand & positioning 3. Product & value proposition 4. Market & category (TAM/SAM, growth, tailwinds, regulatory) 5. Competitive landscape (5–10 competitors in a table; name the strategic group) 6. Pricing & unit economics (CM1/CM2/CM3 waterfall, CAC, payback — show the math) 7. Supply chain & sourcing (ImportYeti, FDA, LinkedIn, job ads) 8. Channel mix (DTC, Amazon, marketplaces, wholesale, international) 9. Marketing & traffic (Meta Ad Library, Similarweb, creative lifespan, SEO) 10. Customer voice & sentiment (review volumes, themes, NPS proxy) 11. Tech stack 12. Agentic / AI readiness (JSON-LD density, agentic storefront signals, checkout friction) 13. Risks & red flags (incl. manufacturer-direct threat, tariff exposure) 14. Financial summary & valuation (EV/Sales comps, buyer-type implications) 15. Investor verdict (bull case, bear case, numerical "change my mind" anchor) 16. Sources (numbered, with access dates) Style: crisp, evidence-led, numerate, slightly sceptical. Voice of a senior analyst writing for a partner with 10 minutes. Length: 1,500–3,000 words for `standard`. ## Gotchas - **Symptom:** report states "$X ARR" without a source. **Cause:** triangulation mistaken for fact. **Fix:** every revenue figure cites a filing/interview/press release, or prefix `~ (est., method: …)` and show the math. - **Symptom:** competitor list matches the brand's own "vs" page. **Cause:** used brand marketing as source of truth. **Fix:** build the competitor set from search intent + category overlap; exclude any competitor named on the brand's own site until independently surfaced. - **Symptom:** unit economics cite gross margin only. **Cause:** stopped at CM1. **Fix:** always show the CM1 → CM2 → CM3 waterfall with stated assumptions; apparel especially — 24–30% return rates collapse CM2. - **Symptom:** SimilarWeb precise visitor count for a small brand. **Cause:** ignored the 50K/mo accuracy floor. **Fix:** below 50K/mo, quote a range and label "directional only"; triangulate with ad count, review velocity, LinkedIn headcount. - **Symptom:** no ImportYeti entry in Sources. **Cause:** forgot the mandatory check — the single most commonly skipped DTC source. **Fix:** Step 3 blocks until `importyeti_lookup.py` has run; a "no shipment records found" result still counts. - **Symptom:** verdict hedges ("interesting brand, execution risk at scale"). **Cause:** analyst avoided commitment. **Fix:** verdict must contain a numerical anchor (e.g. "investable below $80M valuation") AND a "what would change my mind" line. Validator fails otherwise. - **Symptom:** Meta-reported ROAS cited without qualification. **Cause:** ignored post-iOS14 attribution collapse. **Fix:** treat brand-reported Meta ROAS as directional; note the ATT caveat; prefer MER or incrementality-adjusted estimates. - **Symptom:** blended CAC cited instead of new-customer CAC. **Cause:** blended metric flatters because returning-customer spend is in the denominator. **Fix:** separate the two; new-customer CAC is the scaling metric investors care about. ## Rules - SKILL.md is the routing layer. Domain knowledge lives in `references/` — load on demand, don't embed. - `references/sources-playbook.md` is required reading at Step 3. Not optional. - `scripts/validate_report.py` is a hard gate at Step 6. Not optional. - ImportYeti check is mandatory even on `quick` depth — a "no records found" entry is an acceptable result, silence is not. - Never call this skill "complete" until the validator exits 0. - Read-only always. Never POST to the store, send email, or publish. - Banned hype words in output: `revolutionary`, `disruptive`, `game-changing`, `cutting-edge`, `seamless`, `unparalleled`. ---