
Agentkit Seo
- 86 installs
- 2.4k repo stars
- Updated May 12, 2026
- rohitg00/awesome-claude-code-toolkit
Helps with marketing & seo tasks during AI-assisted development.
About
agentkit-seo is a Claude Code skill for marketing & seo. It helps solo builders move faster with AI-assisted coding.
- agentkit-seo
- Marketing & SEO
- AI-coding skill
Agentkit Seo by the numbers
- 86 all-time installs (skills.sh)
- +6 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #1,166 of 1,879 Marketing & SEO skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 86 |
|---|---|
| repo stars | ★ 2.4k |
| Last updated | May 12, 2026 |
| Repository | rohitg00/awesome-claude-code-toolkit ↗ |
What it does
Helps with marketing & seo tasks during AI-assisted development.
Files
AgentKit SEO
Overview
Use this skill as the orchestrator for the whole repository. Its main job is to select the right module skill, avoid loading irrelevant platform rules, and sequence cross-platform work in a sane order.
Routing workflow
1. Identify the target surface from the request. 2. Load only the matching module skill unless the user explicitly asks for a cross-platform pass. 3. If the request spans multiple surfaces, start with agentkit-seo-agent-context-optimization so the factual source of truth is stable before editing platform outputs. 4. If the request involves technical issues with the skill system, consult the main repository documentation.
For broad requests with no clear surface:
- Active applications or job-description tailoring: route to
agentkit-seo-cv-ats. - Recruiter discovery or profile search: route to
agentkit-seo-linkedin. - Proof-of-work, repositories, or developer credibility: route to
agentkit-seo-githuboragentkit-seo-web-portfolio, based on the supplied asset. - Audience building, posting strategy, or public conversation loops: route to
agentkit-seo-x-twitter. - Conflicting, scattered, or cross-platform facts: route to
agentkit-seo-agent-context-optimizationfirst.
Token discipline
- Route to one module by default.
- Load the agent context file before platform references only when facts, consistency, or cross-surface rewriting matter.
- Prefer public URL inspection, local search, or a compact pasted section over asking the user to dump every asset into the prompt.
- Summarize inspected inputs and ask for the smallest missing input set.
- Do not expand into algorithm explanation unless the user asks why.
Intake workflow
- If the user already has an agent context file, ask for or use its explicit path before rewriting platform assets.
- If the task spans multiple surfaces, or the user's facts are scattered, recommend creating or repairing the agent context file first.
- Do not block a narrow one-off edit on a full context file when the supplied material is already enough.
- For public URLs, fetch or inspect public material when tools allow it and cite which source was used.
- For private or login-gated surfaces, ask the user for pasted section text, screenshots, exports, or a local text file instead of guessing.
- If critical facts are missing, ask only for the minimum extra inputs needed to proceed.
Module map
- LinkedIn work:
agentkit-seo-linkedin - GitHub work:
agentkit-seo-github - CV or ATS work:
agentkit-seo-cv-ats - Web portfolio work:
agentkit-seo-web-portfolio - X or Twitter work:
agentkit-seo-x-twitter - Personal source-of-truth context work:
agentkit-seo-agent-context-optimization
More Information
- Main Repository: https://github.com/agentkit-seo/agentkit-seo
- Documentation: https://agentkit-seo.github.io/
- Modules: Includes specialized logic for GitHub, LinkedIn, CV/ATS, Portfolios, and X/Twitter.