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 ZensInk/zens-ink-seo-package --skill keyword-difficultygit clone --depth 1 https://github.com/ZensInk/zens-ink-seo-packageWrote 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/zensink/zens-ink-seo-package/keyword-difficulty)<a href="https://agentmods.dev/skills/zensink/zens-ink-seo-package/keyword-difficulty"><img src="https://agentmods.dev/badge/skills/zensink/zens-ink-seo-package/keyword-difficulty/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/zensink/zens-ink-seo-package/keyword-difficulty"><img src="https://agentmods.dev/badge/skills/zensink/zens-ink-seo-package/keyword-difficulty.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 | $0.00101 | $0.00731 |
| Opus 5.5 | $0.00040 | $0.00292 |
| Sonnet 5.5 | $0.00020 | $0.00146 |
| Haiku 4.5 | $0.00010 | $0.00073 |
Grade A, and why
keyword-difficulty scanned grade A with 0 findings 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 2d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Keyword difficulty from SERP structure
Most KD scores are paywalled link metrics. This reads the actual SERP and answers the only question that matters: are there winnable seats, and what type of page wins them?
When to use
- "Can I rank for 'best crm for startups'?"
- "How competitive is this keyword?"
- "Should I write this article or skip it?"
When NOT to use
- Head terms dominated by Wikipedia/major brands — the tool will tell you, but the answer is already no.
- The user needs a DR-style number to put in a report — use
domain_rating(rank-tracking skill) for DR lookups instead.
Setup
pip install zens-ink
Set SERPER_API_KEY in .env (free tier: 2,500 searches/month at
serper.dev).
Commands
python3 -m zens_ink.kd "open source seo tools"
python3 -m zens_ink.kd "开源 seo 工具" --zh
python3 -m zens_ink.kd "best crm" --json # machine-readable
python3 -m zens_ink.kd "best crm" --markdown # self-contained report
How to read the results
The analysis is built on three fingerprints of a locked SERP:
- Same-name official domains in top 3 — navigational lock, hard to enter.
- One domain family holding 2+ seats — brand sitelinks, Google treats it as the canonical answer.
- Platform ecosystem density — Reddit/YouTube/GitHub stacking means Google wants UGC/forum content for this query.
2+ fingerprints = brand-locked keyword. The output also after removing official and platform-fixed seats — that is the number that decides go/no-go, not the raw top-10 strength.
Decision rule: if the winnable-seat difficulty is moderate and intent
matches the user's page type, write it. If every seat is brand-locked, pivot
to the long-tail variant (feed the seed back through the keyword-research
skill with --expand).
Next steps
- Verify what Google actually rewards: the
search-intentskill runs the SERP page-type breakdown. - Batch-scored a list already? The
competitor-gapskill finds what rivals rank for that the user does not.
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.
- 2d ago First seen · 84 lines · 101 tokens per session scan A 1246a3597024
keyword-difficulty is a skill published in the GitHub repository ZensInk/zens-ink-seo-package (53 stars, last pushed 3d ago), licensed MIT. It adds 101 tokens to every session and 731 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-15.
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