Internal AI-Agent Plugin for a Software Studio

Context

About this project

Engineering teams at the studio were relying on ad hoc prompting to get consistent results from AI coding assistants, every developer re-explaining the same delivery conventions, review standards, and SEO rules from scratch. I designed and built an internal Claude Code plugin that packages this knowledge into reusable, versioned tooling, spanning requirements, code review, QA, release, and a new structured-data / on-page SEO auditing capability.

Challenge

The challenge

• Delivery conventions (requirements, scaffolding, review, QA, release) lived as tribal knowledge, not as something the AI assistant could apply consistently
• Structured-data and SEO guidance was a prose checklist the model had to re-apply by hand each time, with no way to catch a regression
• No deterministic way to score a page's on-page SEO the same way twice, which makes an audit worthless for client-facing reporting
• Existing checks only read generic robots directives, missing Google-specific overrides that cause real 'why did we vanish from Google' cases
• No repeatable review discipline for AI-assisted changes across the team

Approach

What I built

1. 1

   Delivery agents and shared skills

   Built a set of role-scoped agents, project manager, tech lead, code reviewer, QA engineer, release manager, that orchestrate a shared library of skills across the studio's stacks (Django, Magento 2, WordPress, Next.js, React), so the same conventions apply no matter who's driving the assistant.

2. 2

   Deterministic scoring engine

   Replaced a structured-data checklist the model used to self-apply in prose with Python validators that return a real exit status: syntax, required properties, breadcrumb rules, schema eligibility, staging-URL leaks.

3. 3

   Cross-skill enforcement

   Wired a structured-data checkpoint into both the review chain (code review, QA, release, deploy) and the prevention side (planning and page-scaffolding skills), so issues get caught on the way in and on the way out, with the rules defined once.

4. 4

   Full on-page SEO auditor

   Broadened a schema-only audit idea into a five-category on-page scorer, structured data, core tags, indexability, social cards, media/speed, plus a read-only audit agent that runs in parallel across many URLs, backed by a fixture-based automated test suite.

5. 5

   Review-driven correctness passes

   Ran the system through a real human-plus-automated review loop and fixed what it surfaced: a false negative where Google-specific noindex directives were invisible to the indexability check, and a page-capture bug where an embedded iframe could overwrite the real page's headers. Also made the final score assembly reproducible byte-for-byte across runs, not just the sub-scores.

Results

Outcome

• A reusable plugin, dozens of skills plus several delivery agents, now backs day-to-day requirements, review, QA, and release work
• An SEO and structured-data auditor scores pages 0-100 with the same result on every run, end to end, not just at the sub-score level
• Two real correctness bugs caught and fixed before shipping: a Google-specific noindex false negative, and an iframe page-capture defect
• Deduction values, eligibility rules, and schema logic each live in exactly one place, the auditor delegates to the same rules the writer skill uses
• A rounded 100+ automated tests back the scoring engine so a page can't silently start scoring differently between releases