AI Automation: Deploying an Evidence-Gated Specialized Skillset via Superloopy

Automation Stack & Architecture

• Agent Framework: Codex-style coding agents with skill-based specialization (`superloopy-frontend`, `superloopy-research`, `superloopy-clone`).
• Integration Layers: CLI / Plugin interface for local environment execution; browser screenshot capture; Design Token contract (DESIGN.md).
• Target Outcome: High-fidelity frontend implementation that satisfies visual quality requirements through mandatory evidence or wayfinding checkrails rather than model size alone.

Agent Roles & Tools Assignment

Frontend Specialist Role/Skill

• Goal: Implement UI while avoiding generic defaults by adhering to strict design languages.
• Tools assigned: DESIGN.md token contracts; a library of 92 brand/style teardowns for direction selection으로 use instead of default SaaS looks; own custom design-system compliance checks; real-browser screenshot utility at desktop/tablet/mobile widths; visual diff tools against reference targets.

Researching Agent Skill/Role

• Goal: Move beyond one-pass summaries toward verified, cited research expansion waves.
• Tools assigned: Claim ledgers and verification artifacts used as output gates.

Cloning Agent Skill/Role

• Goal: Perform authorized website rebuilds with high parity assurance.
• Tools assigned: DOM/topology extraction tool; computed style extractor; asset grabber; component spec generator; build output validator; visual QA artifact checker via .superloopy/evidence/ directory.

Step-by-Step Workflow Orchestration

1. Initialize agent context by clearly defining the job purpose (persona), operating environment (rules/constraints), and available actions or limited relevant toolset to reduce error rates.
2. For frontend tasks, require a contract phase where an external `DESIGN.md` is provided before any code implementation begins.
3. Execute specialized skill logic (`superloopy-frontend`, etc.) using specific design references instead of generic model defaults.
4. Capture evidence through automated side-channels such as real browser screenshots at multiple widths ($desktop$, $tablet$, $mobile$) or generating claim ledgers for research tokens.
5. Run compliance checks against undeclared colors/spacing if performing UI work.
6. Validate success only when certain artifacts exist under path .superloopy/evidence/ (e.g., screenshot matrix, visual diff results).

Error Handling & Loop Prevention

• Preventing Slop: Use mandatory Design Token contracts prior to execution rather than relying on large model size alone.
• Quality Gatekeeping: Implement

! DYOR (Do Your Own Research)