trentmkelly/DrugHub-scrape

DrugHub Market Snapshot, September 2026

A complete, text-only capture of the public listing, vendor, and review pages of DrugHub, a Monero-only darknet market operating since 2023. Everything here was visible to any visitor without an account. Doesn't include any images.

Collected 16-17 September 2026. Enriched with model-derived labels (typesafe/jev-1.13) on 19 September 2026; see the listing_enrichment table and the Enrichment section below.

What's in it

Table	Rows	Description
listings	22,621	One row per listing page: title, category, description, refund policy, prices, stock, shipping, unit, minimum order, vendor stats as shown on the listing.
shipping_options	57,982	Shipping methods per listing with XMR and fiat prices.
bulk_options	82,135	Bulk price tiers per listing.
vendors	9,418	One row per vendor page fetched (profile plus each review page). Profile stats, seven average ratings, about text. Filter page_r = 0 for one row per vendor (1,598).
reviews	423,003	Every review on the site: listing title, date, and seven percentage scores.
vendor_pgp_keys	1,676	PGP public keys as published on vendor profiles.
vendor_listing_titles	35,793	Every listing title each vendor has been reviewed on, including delisted ones.
rosters, roster_entries	91 / 145,422	Snapshots of the sidebar vendor roster with feedback percentages, deduplicated by content.
category_counts, category_count_entries	16 / 1,888	Snapshots of the category tree with listing counts.
listing_enrichment	22,621	Model-derived labels per listing (language, substance, form, purity claim, and 12 other fields). See "Enrichment" below.
fetch_log	35,402	Every HTTP fetch made, including failures, with timestamp, status, and content hash. Page bodies are not included.

Every parsed row carries a fetch_id that points into fetch_log, and a fetched_at unix timestamp.

Coverage

• Listings: 22,621 of the 22,641 discovered; the other 20 were removed by the site during the crawl. 434 are digital goods with no shipping destination.
• Vendors: 1,598 of 1,601 on the roster; 3 profiles returned 404.
• Reviews: all review pages of all vendors, July 2023 to 17 September 2026. 1,416 vendors have at least one review.

Listings by category

Category	Listings	Subcategories
Drugs	20,429	67
Forgeries / Counterfeits	1,540	4
Services	415	11
Software	228	5
Defense / Counter Intel	9	4

Largest subcategories: Prescription Drugs (3,080), Cannabis - Buds and Flowers (3,079), Steroids (1,353), Benzos - Pills (1,337), Stimulants - Cocaine (964), Watches (838), Dissociatives - Ketamine (794), Psychedelics - LSD (617).

Shipping origins are led by the United States (6,093), United Kingdom (4,286), India (2,616), Germany (2,216), China (1,897), and Australia (1,267). Prices are quoted in XMR plus one of USD, EUR, GBP, AUD, or CAD; the site's own exchange rates at fetch time are on every row (xmr_usd, xmr_eur, ...).

Reviews by year

Year	Reviews
2023	23
2024	12,515
2025	150,520
2026 (to 17 Sep)	259,945

Things to know before using it

• Prices are as displayed. Nothing has been converted. Use the per-row exchange rates or your own historical series.
• Reviews have no identifier and no text. They are numeric scores plus a date, listed newest first. Their position on a page can shift as new reviews arrive, so a small number may be duplicated across page boundaries. Reviews with identical (vendor, listing title, date, scores) are common and mostly genuine; the data has not been deduplicated.
• Reviews reference listing titles, not ids. Joining on (vendor, title) matches about 63% of reviews to a current listing. The rest are on delisted, renamed, or private listings.
• Relative dates are stored as shown ("21 months ago", "Today"). Resolve them against fetched_at.
• vendors is one row per page, so profile stats repeat across a vendor's review pages. Filter page_r = 0 unless you want the review pages.
• Vendor ratings are near-uniform (80% of vendors at 98-100%). Cancellation and dispute counts carry more signal.
• Free text is raw. Descriptions keep their original line breaks and any decorative characters. The site enforces a 50-character minimum and roughly a 10,000-character maximum.
• Category is the market's own tree, not a normalised taxonomy. About 10% of listings are outside the Drugs category.

Enrichment (model-derived, not ground truth)

listing_enrichment holds one row per listing with labels produced by typesafe/jev-1.13, a classification-only model that picks from options we define and returns a probability distribution. Every enumeration includes an escape option (other_listed_class, not_stated, unknown, ...), and each choice column has a companion <field>_p with the probability of the chosen option. answers_json carries the full distributions. Treat these as annotations to be thresholded, not facts.

Field	Type	What it answers
language	choice	Primary language of the description (ISO code, mixed, other, unknown)
substance	choice	Substance sold, from a per-subcategory vocabulary (Drugs only)
product_type	choice	Product kind for non-drug categories
form	choice	Physical form (pill, powder, crystal, flower, vape, blister_pack, ...)
purity_claim	choice	Stated purity/potency bucket (none, under_50, 50_79, 80_94, 95_plus)
lab_tested	0-1	Claims lab testing
pharma_claim	choice	Presented as genuine pharmaceutical, replica/pressed, or not applicable
counterfeit_risk	0-1	Imitates a pharmaceutical product (branded press, fake blister, RC substitution)
quantity_stated	choice	Whether the price maps to a stated quantity
wholesale	0-1	Bulk/wholesale orientation
stealth_described	0-1	Packaging/stealth described
tracking	choice	Tracked, untracked, both, not stated
fe_required	0-1	Vendor asks for Finalize Early / early escrow release
external_contact	choice	Mentions an outside channel (see caveat)
cross_market	0-1	Mentions history on another darknet market
harm_reduction	0-1	Contains dosing guidance or safety warnings
fentanyl_mention	choice	none, claims_free, contains, warning
text_style	choice	terse, handwritten, marketing, template, other

Notes:

• prompt_version is 2 for most rows and 3 for Prescription Drugs, Peptides, Steroids, and Benzos - Pills, where the substance vocabulary was expanded after a first pass. All other questions are identical across versions.
• substance vocabularies were built from title-token frequencies per subcategory. Prescription Drugs uses therapeutic-class buckets for the long tail; about 12% of that subcategory still lands in other_listed_class.
• external_contact has low precision: its other_platform value often reflects a mention of another site or market rather than an actual handle.
• The model saw the title, category, unit, origin, and the first 2,500 characters of the description.