Treg Page

treg-page — research first, then fill in the spec

Both page types are templates fed by one dict entry. No HTML is written by hand: the route in api.py renders titles, prices, provider rows, reliability and schema from catalog_store at request time. Your job is the copy, and the copy is only as good as the research behind it.

Read marketing/pseo-ship-plan.md for what to write next and docs/context/interface/seo.md for how the pages work. Never invent a number: if it is not in the catalog, it does not go on the page.

The order matters. Do not skip step 1.

1. Find the words buyers use (measure, never guess)

The catalog's vocabulary is not the buyer's. "People enrichment" is our word; "email finder" is theirs. Measure before titling anything:

uv run --frozen treg call google-ads "v22/customers/2277522568:generateKeywordHistoricalMetrics" \
  --method POST --data '{"keywords":["email finder","email finder api","linkedin email finder"],
  "geoTargetConstants":["geoTargetConstants/2840"],"language":"languageConstants/1000",
  "keywordPlanNetwork":"GOOGLE_SEARCH"}'

Google Ads is an own-key tool, so this costs nothing. Use the tool-scoped form (treg call google-ads <path>); the bare endpoint id is ambiguous across three registered accounts. Try 12 to 20 phrasings: the job in our words, the job in the buyer's words, <thing> api, <thing> pricing, <platform> scraper, and the agent-prefixed forms. Keep whatever clears ~100/month; a term with 0 volume must not become an H1.

Known from earlier passes: buyers type scraper, <platform> api pricing and <x> mcp server. They do not type "api alternative", and they do not type "how to with chatgpt" (measured 0 to 20/month across 30+ phrasings). Do not target those.

2. Find the real difficulty (agent-reach on Reddit and X)

This is what makes the page worth linking to. Use the agent-reach skill; both platforms go through OpenCLI (twitter-cli's search endpoint 404s regularly):

agent-reach doctor --json          # confirm reddit + twitter backends
mkdir -p /tmp/ar-<job> && cd /tmp/ar-<job>
for q in "<job in buyer words>" "<job> accuracy" "<job> not working" "<provider a> vs <provider b>" \
         "how do you <job>" "<job> cost at scale"; do
  opencli reddit  search "$q" -f yaml > "r-$(echo $q | tr ' /' '__').yaml"
  opencli twitter search "$q" -f yaml > "x-$(echo $q | tr ' /' '__').yaml"
done

Six to eight queries gives ~180 posts. Delegate the reading to a subagent so the raw dump never enters the main context; ask it for:

• the 8 to 12 most quotable questions or complaints, verbatim and under 25 words, each with platform, score and URL;
• the distinct pain themes with a post count each;
• for each theme, what a page can honestly say (and "no good answer" where it cannot);
• any post where someone tried to do this with ChatGPT, Claude or an agent.

Filter vendor astroturf. Posts that arc from "burning credits" to "my boss is asking questions" to a named tool, identical posts across subreddits, and posts in a vendor's own sub are marketing. The last research pass found four such clusters. Exclude them; quote organic posts only.

Then map each theme to a voices entry: (heading, verbatim quote, who, url, what this page can honestly do about it). Where the honest answer is "no comparison table can tell you this", say so and offer the cheap experiment instead. That candour is the page's credibility.

3. Check what the catalog can actually do

uv run --frozen treg catalog search "<the job in plain words>"
uv run --frozen treg catalog get <endpoint-id>          # params, cost, verified, siblings

Every capability id you name must exist (tests/test_agent_pages.py enforces it). A job the catalog cannot do does not get a page. If the research surfaced a job we cannot serve, add it to the gap list in marketing/pseo-ship-plan.md instead of writing around it.

4. Write the entry

Use-case page — add to USE_CASE_PAGES in src/treg/agent_pages.py, keyed by (category slug, job slug). The route picks the FORM from the data, so you do not:

Fields: label (must match its row in USE_CASES exactly), sentence (the H1: the buyer's term from step 1, not ours), title ({n}, {cheapest}, {agent} interpolate), lede, prompt (one, starting "Using treg, …"), prompt_why (4 × (lead, one line)), result_noun, what_is_heading, what_is, notes (3, from provider docs and catalog notes, the part nobody can regenerate), voices from step 2, faq (4), related (labels that exist on the menu).

Agent page — add to AGENTS. Everything but ChatGPT uses the universal install: paste set up treg — https://treg.to/llms.txt into that client's chat, and hand it the team token (header X-Treg-Token) only if it asks. That is what the dashboard's own onboarding shows, so the steps must match it. Screenshot optional; ship without the slot rather than with a placeholder.

5. Verify

uv run --frozen python -m pytest -q tests/test_agent_pages.py
uv run --frozen python -m treg          # then open the page and READ it in a browser

Check by eye: the H1 carries a measured term · the first sentence of every section answers its heading · prices and dates come from the catalog · no empty section · the quotes link out.

House rules

• No em-dashes in page copy. A test fails on them. The setup line is the one exception.
• treg.to, never bare treg, in titles, H1s and anchors.
• Never claim treg.to chooses or fails over between providers. It compares; the agent picks, or the reader tells it how.
• Observed reliability stats may be published per vendor (Jason, 2026-08-21) with the "live traffic, not a controlled benchmark" caveat. The section hides itself when there is no traffic.
• Say the price is the provider's own rate with $0.000 markup, and mark own-account jobs FREE.
• Update docs/context/interface/seo.md in the same commit as the page.