agentleFS
LazyIsEfficientLazyIsEfficient/agentic-os/.claude/skills/seo-ops/SKILL.md

seo-ops

Skill17 starsChanged 24 days agoMITView on GitHub

AI-powered SEO operations: keyword intelligence, competitor gap analysis, Google Search Console optimization, and trend detection. Use when asked to research keywords, analyze competitor content gaps, audit GSC performance, or detect trending topics. For growth experiments see growth-engine; for content optimization see autoresearch.

5 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 5 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run scripts/gsc_auth.py once before any GSC tool — it saves the OAuth token locally.

    line 39
  2. Distinctive:

    Check the playbook in growth-engine before creating new content to apply proven patterns.

    line 41

“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

AI SEO Ops

AI-powered SEO operations: keyword intelligence, competitor gap analysis, GSC optimization, and trend detection.

Core Tools

ToolPurpose
scripts/content_attack_brief.pyFull keyword intelligence pipeline: BOFU keywords, competitor gaps, decaying pages
scripts/gsc_client.pyGoogle Search Console API client (CLI + library)
scripts/gsc_auth.pyOne-time OAuth setup for GSC access
scripts/trend_scout.pyMulti-source trend detection across Google Trends, HN, Reddit, X

Core Rules

  1. Run scripts/gsc_auth.py once before any GSC tool — it saves the OAuth token locally.
  2. Keywords are prioritized by Impact × Confidence (max 100) — focus on high-score BOFU targets first.
  3. Check the playbook in growth-engine before creating new content to apply proven patterns.
  4. Weekly cadence: full brief + daily striking-distance check + 2×/week trend scout.

References

  • references/tool-reference.md — full CLI and library usage for all four tools
  • references/configuration-and-scoring.md — environment variables, scoring model, funnel classification, workflow, dependencies

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.