Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Agent-Engineer-Master/skill-engineer --skill analyzing-dtc-storesgit clone --depth 1 https://github.com/Agent-Engineer-Master/skill-engineerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agent-engineer-master/skill-engineer/analyzing-dtc-stores)<a href="https://agentmods.dev/skills/agent-engineer-master/skill-engineer/analyzing-dtc-stores"><img src="https://agentmods.dev/badge/skills/agent-engineer-master/skill-engineer/analyzing-dtc-stores/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agent-engineer-master/skill-engineer/analyzing-dtc-stores"><img src="https://agentmods.dev/badge/skills/agent-engineer-master/skill-engineer/analyzing-dtc-stores.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00164 | $0.02386 |
| Opus 5.5 | $0.00066 | $0.00954 |
| Sonnet 5.5 | $0.00033 | $0.00477 |
| Haiku 4.5 | $0.00016 | $0.00239 |
Grade C, and why
analyzing-dtc-stores scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- Built with Agent Engineer Master — get your own production-ready skill: www.agentengineermaster.com/skill-engineer --> How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Parse
store_url,depth,focus,--no-save. - Derive
brand_slugfrom the domain (e.g.brandname.com→brand-name). - Copy
assets/report-template.mdto a working draft. Do not save to the final path yet.
Step 2 — Recon sweep (scripted)
Run scripts/recon.py --url <store_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 <brand>— active ad count, creative lifespan distribution.scripts/importyeti_lookup.py --brand <brand>— supplier + country-of-origin + shipment volume. Mandatory — the #1 skipped source.scripts/similarweb_lookup.py --domain <domain>— traffic with the 50K/mo accuracy floor flag.scripts/store_leads_lookup.py --domain <domain>— Shopify plan, app stack, revenue bracket.scripts/reviews_scan.py --brand <brand> --domain <domain>— Trustpilot + Amazon + YouTube review URLs.scripts/reddit_search.py --brand <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.
What ships with it
21 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/report-template.md 1.9 KB
- evals/evals.json 1.8 KB
- evals/judge.md 2.4 KB
- evals/rubric.md 5.0 KB
- references/edge-cases.md 539 B
- references/learnings.md 548 B
- references/sources-playbook.md 7.2 KB
- references/unit-economics-benchmarks.md 3.6 KB
- references/worked-example.md 4.5 KB
- scripts/_openai_search.py 2.0 KB runs code
- scripts/amazon_bsr.py 3.5 KB runs code
- scripts/importyeti_lookup.py 4.6 KB runs code
- scripts/meta_ad_library.py 11 KB runs code
- scripts/recon.py 4.4 KB runs code
- scripts/reddit_search.py 5.3 KB runs code
- scripts/reviews_scan.py 2.4 KB runs code
- scripts/save_report.py 1.6 KB runs code
- scripts/similarweb_lookup.py 4.8 KB runs code
- scripts/store_leads_lookup.py 5.2 KB runs code
- scripts/unit_econ.py 3.4 KB runs code
- scripts/validate_report.py 4.5 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 137 lines · 164 tokens per session scan C 50358de689f8
analyzing-dtc-stores is a skill published in the GitHub repository Agent-Engineer-Master/skill-engineer (8 stars, last pushed 3mo ago), licensed MIT. It adds 164 tokens to every session and 2,386 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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professional
Polished, business-ready design with modern typography, structured layouts, and a trustworthy visual identity.