dev-gtm-claude-skills
Ensures developer documentation ranks and gets surfaced in AI searches, reducing discoverability gaps and increasing adoption signals through structured, AI-…
Open-source Claude skills for GEO, AI discoverability, and developer GTM workflows. Built for developer-focused companies that want their documentation to be found, parsed, and cited by AI systems.
- Ask Claude to index and optimize developer documentation for AI search discoverability
- Generate structured metadata for code repositories to improve citation in AI responses
- Automate developer-friendly content parsing for GTM workflow documentation systems
Ensures developer documentation ranks and gets surfaced in AI searches, reducing discoverability gaps and increasing adoption signals through structured, AI-friendly content optimization.
Developer-focused product teams and technical content leaders optimizing documentation visibility in LLM-powered search and discovery.
https://github.com/Infrasity-Labs/dev-gtm-claude-skills
By Infrasity-Labs
How to Get It
claude plugins install Infrasity-Labs/dev-gtm-claude-skills
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Index and optimize developer documentation for AI search discoverability
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 93 GitHub stars; 3 contributors; last commit 27d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 93 GitHub stars; 3 contributors; last commit 27d ago; license MIT.