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platform-frontend

Framework-agnostic frontend architecture — state management, components, data fetching. Use when building any web frontend, choosing state patterns, or structuring UI code.

Install with Tessl CLI

npx tessl i github:ravnhq/ai-toolkit --skill platform-frontend
What are skills?

67

1.10x

Quality

51%

Does it follow best practices?

Impact

95%

1.10x

Average score across 3 eval scenarios

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/platform/platform-frontend/SKILL.md
SKILL.md
Review
Evals

Principles

  • Start with local state — lift only when shared
  • Organize code by feature, not by type
  • Use named exports for better refactoring and searchability
  • Never use barrel files (index.ts re-exports) — they break tree-shaking and slow builds
  • Measure before memoizing — don't optimize what isn't slow

Rules

See rules index for detailed patterns.

Examples

Positive Trigger

User: "Choose state boundaries and data-fetching patterns for this web app."

Expected behavior: Use platform-frontend guidance, follow its workflow, and return actionable output.

Non-Trigger

User: "Write a Swift actor for thread-safe cache access."

Expected behavior: Do not prioritize platform-frontend; choose a more relevant skill or proceed without it.

Troubleshooting

Skill Does Not Trigger

  • Error: The skill is not selected when expected.
  • Cause: Request wording does not clearly match the description trigger conditions.
  • Solution: Rephrase with explicit domain/task keywords from the description and retry.

Guidance Conflicts With Another Skill

  • Error: Instructions from multiple skills conflict in one task.
  • Cause: Overlapping scope across loaded skills.
  • Solution: State which skill is authoritative for the current step and apply that workflow first.

Output Is Too Generic

  • Error: Result lacks concrete, actionable detail.
  • Cause: Task input omitted context, constraints, or target format.
  • Solution: Add specific constraints (environment, scope, format, success criteria) and rerun.

Workflow

  1. Identify whether the request clearly matches platform-frontend scope and triggers.
  2. Apply the skill rules and referenced guidance to produce a concrete result.
  3. Validate output quality against constraints; if gaps remain, refine once with explicit assumptions.
Repository
ravnhq/ai-toolkit
Last updated
Created

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.