seo-keyword-eval

name	seo-keyword-eval
description	Evaluate SEO keyword demand and competition with measured data before choosing a blog post title or topic for open-infer.org. Use when drafting a new post, comparing title candidates, deciding if a trending topic is worth covering, or reviewing Search Console results.

SEO Keyword Evaluation

Titles are keyword claims. Before choosing one, measure both sides of the market: demand (are people searching this?) and supply (who already ranks for it?). Never rank title candidates by gut feeling — every step below produces a number you can compare.

Why this works for this site

open-infer.org wins on content gaps: deep technical topics where demand exists but third-party content is near zero. Validated examples:

• green-ctx post: "green context" had NVIDIA docs and nothing else. One post → ~40 impressions/2wk from a cold site, the only non-brand traffic.
• DSpark (July 2026): 9 days after release, Google Trends showed dspark at parity with the years-old category term speculative decoding, while autocomplete for "dspark" still returned unrelated brands — demand had outrun supply. That mismatch is the signal to publish.

Chasing established head terms ("llm inference optimization") loses to framework docs and review sites. Skip those.

Step 1 — Measure demand

Three free, scriptable signals. Run all three; they measure different things.

Google Trends — relative heat and trajectory. Always include one established category term as the yardstick:

uv run --with pytrends --no-project python - <<'PY'
from pytrends.request import TrendReq
pt = TrendReq(hl='en-US', tz=0)
pt.build_payload(['CANDIDATE_TERM', 'CATEGORY_BASELINE'], timeframe='today 3-m')
print(pt.interest_over_time().tail(10).to_string())
PY

Values are relative (0–100 within the compared set), not absolute volume. Read the ratio to the baseline and the direction (climbing / decaying).

Google Autocomplete — has demand solidified into query variants?

curl -s "https://suggestqueries.google.com/complete/search?client=firefox&q=TERM"

• Many topical variants → mature demand, mature competition.
• No topical variants but Trends shows volume → window open: the term exists in search boxes but no content has claimed the completions yet.

Hacker News (Algolia) — leading indicator; HN heat precedes Google search volume by days to weeks:

curl -s "https://hn.algolia.com/api/v1/search?query=TERM&tags=story"

Hundreds of points / comments on a launch story = a search wave is coming. The comment threads are also your FAQ source — the questions asked there are the queries people will type next week.

Keyword Planner / Ahrefs volume data lags 2–3 months. For terms younger than that, their "0 volume" is meaningless — ignore them.

Step 2 — Measure supply

Manual, five minutes:

1. Google the exact phrase you want to rank for (e.g. "dspark benchmark"). Classify the top 10: news announcements / official docs / independent technical content. All-announcements = content-type gap → strong signal.
2. Search intitle:"exact phrase" — how many pages have claimed the phrase in their title? Near zero = unclaimed.

Step 3 — Compose the title

Rules derived from the measurements:

• Pair one emerging term with one established category phrase. The emerging term rides the wave; the category phrase is the long-term floor if the wave dies. Both must appear as complete phrases — "speculative decoder" does not match "speculative decoding".
• One H2 per long-tail variant you found in autocomplete / HN comments ("X vs Y", "what is X", "run X"). Each H2 is a future search entry point.
• Site title style: Descriptive Statement: Subtitle (see existing posts).
• Frontmatter description is the meta description — write it for the searcher, front-load the keywords.

Step 4 — Timing

Emerging-term windows are measured in weeks. The window closes when the official framework docs (vLLM / SGLang / vendor) claim the term. If Trends shows the spike already decaying, publishing this week still captures the full tail; publishing next month captures nothing.

After publishing, post to r/LocalLLaMA and HN — backlinks from those threads are what move a new domain's ranking.

Step 5 — Verify with Search Console (ground truth)

~28 days after publishing, check GSC query data:

• Impressions confirm demand estimates; position >20 with impressions means Google matched the page but the content is too thin — strengthen it.
• Queries you rank for but didn't target = free topic ideas for the next post.

Feed real numbers back into this file when they confirm or refute a call — the two examples at the top should not stay the only ones.