seo-keyword-eval

# 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: ```bash 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? ```bash 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: ```bash 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.