skills/agent-native/SKILL.md
Designs agent-native applications where agents are first-class citizens with full tool parity, atomic primitives, and explicit completion signals. Covers tool design, context injection, agent-to-UI communication, and mobile checkpoint/resume patterns. Use when architecting an agentic system, designing tool surfaces, building agent-aware UI, implementing context.md patterns, or asking "how do I make my app agent-native."
npx skillsauth add mblode/agent-skills agent-nativeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Agent-native applications treat agents as first-class users. Whatever a human can do through the UI, an agent can achieve through tools. Features are outcomes described in prompts, achieved by an agent with atomic tools, operating in a loop until done.
| File | Read When |
|------|-----------|
| references/core-principles.md | Default: understanding Parity, Granularity, Composability, and Emergent Capability |
| references/tool-design.md | Designing tools — atomic primitives, CRUD, domain tools, dynamic discovery |
| references/files-and-context.md | State management — entity directories, context.md, context injection, files vs database |
| references/agent-ui-communication.md | Building agent feedback — completion signals, partial completion, event types |
| references/mobile-specifics.md | iOS/mobile — checkpoint/resume, iCloud storage, background execution |
# Anti-pattern: workflow-shaped tool
analyze_and_organize(folder) # bundles judgment into code
# Agent-native: atomic primitives
list_files(folder) → read_file(path) → move_file(src, dst) → write_file(path, content)
# The agent decides what to move and where — judgment stays in the prompt
core-principles.md.tool-design.md.context.md file, entity directory structure, and system prompt injection. Read files-and-context.md.agent-ui-communication.md.Copy and track during design:
Agent-native design progress:
- [ ] Capability map: every UI action has an agent equivalent
- [ ] Tools are atomic primitives (judgment in prompts, not tools)
- [ ] Every entity has full CRUD tool coverage
- [ ] System prompt injects available resources and capabilities
- [ ] Agents and users share the same data space
- [ ] Agent actions reflect immediately in UI
- [ ] Completion is signaled explicitly (no heuristic detection)
- [ ] External APIs use dynamic capability discovery where possible
- [ ] Approval model matches stakes and reversibility
- [ ] Ultimate test: describe an unbuilt outcome — can the agent figure it out?
Before shipping, verify:
if/else decision logic, split it.analyze_and_organize bundles decision logic into a tool; break into read_file, move_file, write_file.define-architecture — repo structure and module boundaries before going agent-nativeagents-md — audit CLAUDE.md / AGENTS.md for agent instruction qualitydevelopment
Reverse-engineers a UI animation from a screen recording — extracts frames, tracks motion per frame, fits easing and spring curves, annotates choreography, and emits CSS, Motion/Framer Motion, SwiftUI, React Native, or UIKit code. Use when the user shares or uploads a screen recording or video of a UI animation, or asks to "reverse engineer this animation", "recreate this animation", "match this easing", "extract the animation curve", "figure out the spring from this video", "copy this transition from a video", "how does this animation work", or "reproduce this motion".
development
Produces a read-only review report of the current local diff or branch — it lists findings and does NOT edit files. Use when asked to run `/pr-reviewer` before commit, before push, or before handing changes off for PR creation or update; also use for "review my changes", "code review", "code quality review", or when you want findings listed by severity so you can decide what to fix yourself. Also use for "thermo-nuclear review", "deep code quality audit", "structural review", "harsh maintainability review", or "code judo" — these load the structural quality rubric for an unusually strict maintainability pass. Also use for "deslop this", "clean up AI code", "remove slop", or "review for AI patterns" — these load the AI slop detection catalog. For automatic fix-in-place (no manual review step needed), use the private `simplify` skill instead.
development
Autonomous PR monitor — polls every 2 minutes for merge conflicts, CI/CD failures across GitHub Actions, Buildkite, Vercel, and Fly.io, review comments, and merge readiness. Auto-detects PR from current branch, fixes what it can, notifies on state changes. No setup questions. Also runs as one-shot for specific concerns. Use when asked to babysit a PR, watch a PR, monitor CI, keep a PR green, handle merge conflicts, poll PR status, run `/pr-babysitter`, fix CI, diagnose CI failure, why is CI red, CI is broken, loop on CI, fix CI checks, resolve merge conflicts, or fix conflicts.
development
Feature-level UX audit for React/Next.js code. Catches what Lighthouse, axe, ESLint, and Storybook miss — state coverage gaps (missing loading/empty/error), form data loss on validation, broken focus management, optimistic UI without rollback, skeleton-induced layout shift, vague microcopy, and 25+ other modern frontend UX bugs. Diff-aware (audits changed files only) and produces a 3-tier ship-readiness verdict (release-blocker / fix-this-sprint / backlog) grouped by surface, with concrete fixes using modern React 19 APIs (useActionState, useFormStatus, useOptimistic, useTransition, Suspense). Use before merging a frontend PR, before shipping a feature, or when asked "is this checkout/onboarding/dashboard ready?", "review this PR for UX bugs", "audit this component", "what would break in production?", "is this ready to ship?"