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mblode/agent-skills140 installs

agent-native

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."

How do I install this agent skill?

npx skills add https://github.com/mblode/agent-skills --skill agent-native
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    This skill consists of documentation and architectural guidelines for designing agent-native applications. It contains no executable code or active scripts.

  • Socketpass

    No alerts

  • Snykwarn

    Risk: MEDIUM · 1 issue

  • Runlayerwarn

    2/6 files flagged

  • ZeroLeakspass

    Score: 93/100 · 2 sections analyzed

What does this agent skill do?

Agent-Native

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.

Reference Files

FileRead When
references/core-principles.mdDefault: understanding Parity, Granularity, Composability, and Emergent Capability
references/tool-design.mdDesigning tools — atomic primitives, CRUD, domain tools, dynamic discovery
references/files-and-context.mdState management — entity directories, context.md, context injection, files vs database
references/agent-ui-communication.mdBuilding agent feedback — completion signals, partial completion, event types
references/mobile-specifics.mdiOS/mobile — checkpoint/resume, iCloud storage, background execution

Core Principles

  • Parity — Agent can achieve anything users can do through the UI. Build a capability map; close every gap.
  • Granularity — Tools are atomic primitives. Judgment and decision logic live in prompts, not tool implementations. To change behavior, edit prose, not code.
    # 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
    
  • Composability — New features = new prompts. With atomic tools and parity, you describe an outcome and the agent loops until it's achieved.
  • Emergent capability — The agent handles requests you didn't design for. Observe what users ask; formalize patterns that emerge.
  • Improvement over time — State persists via context files. Prompts can be updated for all users without shipping code.

Design Workflow

  1. Capability audit — Map every UI action to an agent equivalent. Close any gap. Read core-principles.md.
  2. Tool surface design — Define atomic primitives for every entity (CRUD). Add domain tools only for vocabulary, guardrails, or efficiency. Read tool-design.md.
  3. Context and state planning — Design the context.md file, entity directory structure, and system prompt injection. Read files-and-context.md.
  4. Agent-UI feedback design — Define completion signals, event types, shared workspace, and approval gates. Read agent-ui-communication.md.
  5. Validate — Run the checklist below. Describe an unbuilt outcome and test whether the agent can figure it out.

Design Checklist

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?

Validation Loop

Before shipping, verify:

  1. Capability coverage — Pick 5 random UI actions; confirm the agent can accomplish each without touching code.
  2. Tool atomicity — Review every tool; if it contains if/else decision logic, split it.
  3. Context completeness — Clear the agent's context and start a session; does it still know what exists?
  4. End-to-end scenario — Give the agent an outcome you never explicitly built. Does it compose tools to get there?

Anti-Patterns

  • Agent as router — Agent routes to your code instead of acting with judgment; you've built a dispatcher, not an agent.
  • Workflow-shaped tools — analyze_and_organize bundles decision logic into a tool; break into read_file, move_file, write_file.
  • Orphan UI actions — User can do something the agent can't; breaks parity.
  • Context starvation — Agent doesn't know what exists; inject resources and capabilities into every system prompt.
  • Gates without reason — Domain tool is the only path when primitives should also work; default to open.
  • Heuristic completion detection — Detecting done via consecutive idle iterations; require an explicit completion tool call.
  • Static API mapping — 50 tools for 50 endpoints; use discover + access instead.

Related Skills

  • define-architecture — repo structure and module boundaries before going agent-native
  • agents-md — audit CLAUDE.md / AGENTS.md for agent instruction quality

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/mblode/agent-skills/agent-native">View agent-native on skillZs</a>