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 swan-gtm/gtm-skills --skill seo-intelgit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote 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/swan-gtm/gtm-skills/seo-intel)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/seo-intel"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/seo-intel/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/swan-gtm/gtm-skills/seo-intel"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/seo-intel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 69 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00173 | $0.01963 |
| Opus 5 | $0.00086 | $0.00981 |
| Sonnet 5 | $0.00035 | $0.00393 |
| Haiku 4.5 | $0.00017 | $0.00196 |
Grade A, and why
seo-intel 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 13d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO intel
Runs whenever an SEO question needs a live-data answer: what to rank for, what is technically broken, what competitors cover that you don't, whether AI assistants cite you. Produces one report per play, with every claim tied to an observed search result or extracted page.
Ground rules
- Only observed evidence. Every difficulty score, gap, and finding must trace to a real search result or a page you actually extracted. Never quote search volume, domain authority, or traffic numbers you did not measure — if a metric is unavailable, say "not measurable" instead of inventing it.
- Source URL on every row. Every keyword opportunity, content gap, and AI citation in a report carries the URL that proves it.
- Partial data is reported as partial. If a platform was unreachable or a crawl came back incomplete, score from what returned and state the coverage gap. Never backfill.
Route the request
| The user wants | Play | Read first |
|---|---|---|
| Keywords to target, topic clusters, difficulty | Keyword research | references/serp-and-keywords.md |
| Current positions, ranking movement | Rank check | references/serp-and-keywords.md |
| Technical/on-page health, "SEO audit" | Site audit | references/site-audit-checks.md |
| Topics competitors cover that they don't | Content gap | references/content-gaps.md |
| A competitor's on-page playbook at scale | Competitor reverse-engineering | references/content-gaps.md |
| Brand presence in AI answers, AEO/GEO | AI visibility | references/ai-visibility.md |
If the request is generic ("help me with SEO"), ask which of these they need — don't guess. After finishing one play, suggest the natural next one (audit → keyword research → content gap → rank check), carrying forward the domains, keyword lists, and crawl data already gathered instead of re-collecting.
Running memory and delta modes
Keep a running file per domain (and per brand, for AI visibility) in your workspace: audit findings, rank snapshots, gap lists, AI-citation snapshots — dated, structured, append-only. On every run, load the prior snapshot first and pick a mode:
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 81 lines · 173 tokens per session scan A dd6492eb3f84
seo-intel is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 173 tokens to every session and 1,963 once invoked, about $0.0009 per session on Opus 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 2026-08-30.
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