Repeatable AI workflows,
packaged as skills.

JQ AI Skills turns those workflows into installable skill folders. Each skill is organized so Codex, Claude Code, or a similar agentic coding environment can understand when to use it, what steps to follow, what guardrails matter, and what output the user should expect.

The library covers practical studio workflows: safe GitHub publishing, public case study writing, human-reviewed outreach pipeline design, Etsy listing optimization, public-source research briefing, skill review, authorized web scraping, copy cleanup, code deduplication, animated demo generation, and release announcement writing. The v0.7.5 release is the current public reference point for the packaged library, with a top-level START_HERE.md onboarding path, a short QUICK_REFERENCE.md, visitor paths, a public proof index, a first install proof trail, an update-after-release proof trail, a utility skill proof pack, a demo animation v2 walkthrough sample, a release-to-showcase handoff proof sample, a skill library evaluation checklist, a first-skill scorecard, a first skill walkthrough, a first-skill candidate pack, an expected first-skill review, a root INSTALL.md command reference, an install FAQ, an install verification guide, an update-installed-skills guide, a smoke-test install sample, a root TROUBLESHOOTING.md support page, a root CHANGELOG.md release history, a root RELEASE_CHECKLIST.md publishing routine, a root SECURITY.md responsible-use policy, a public ROADMAP.md, a root SUPPORT.md, GitHub issue templates, a pull request checklist, a refreshed skill quality matrix with current visible proof, update proof coverage, utility proof coverage, demo walkthrough proof, and safest install notes, a skill anatomy guide, a skill review checklist, a skill reviewer sample, a public-safe skill request example, a public examples index, a one-minute install guide, a first-run sample for github-safe-publisher, a complete public catalog, a first-skill selection guide, workflow bundle examples for common jobs, fake-data samples for release announcements, outreach pipelines, and Etsy listing optimization, clearer README onboarding, a stable 2026 social preview, GitHub Actions validation, quick install scripts, sample artifacts, and demo media included.

This is the technical credibility layer behind JQ AI SYSTEMS: it shows how repeatable AI workflows can be packaged, reviewed, documented, installed, and reused instead of living as one-off prompts.