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erne-init

AI Verified
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
AI
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
94 / 100 · audit passed
Author / version / license
@JubaKitiashvili · MIT
Token usage
Lean
Setup complexity
Plug-and-play
External API key
Not required
Operating systems
macOS · Linux · Windows
Runtime requirements
No special requirements
Permissions
  • Read-only
  • Write / modify
Network behavior
Local-only
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview erne-init.preview 
---
name: erne-init
description: Initialize ERNE — the AI agent harness for React Native & Expo projects. Sets up 13 specialized…
category: ai
runtime: no special runtime
---

# erne-init output preview

## PART A: Task fit
- Use case: Initialize ERNE — the AI agent harness for React Native & Expo projects. Sets up 13 specialized agents, hook-based code quality enforcement, MCP server integrations, and a visual dashboard. Triggers on "set up erne", "initialize erne", "install erne", "configure agents", "add erne to project", or any first-time ERNE setup request. Always use this skill when the user wants to set up ERNE, even if they don't say "init" explicitly..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Step 1: Ask preferences / Step 2: Run the init command / Step 3: Launch the dashboard” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Initialize ERNE — the AI agent harness for React Native & Expo projects. Sets up 13 specialized agents, hook-based code quality enforcement, MCP server integrations, and a visual dashboard. Triggers on "set up erne", "initialize erne", "install erne", "configure agents", "add erne to project", or any first-time ERNE setup request. Always use this skill when the user wants to set up ERNE, even if they don't say "init" explicitly.”.
- **02** When the source has headings, the agent prioritizes “Step 1: Ask preferences / Step 2: Run the init command / Step 3: Launch the dashboard” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.

## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Initialize ERNE — the AI agent harness for React Native & Expo projects. Sets up 13 specialized agents, hook-based code quality…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Step 1: Ask preferences”, “Step 2: Run the init command”, “Step 3: Launch the dashboard”, “Step 4: Tell the user what happened”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name erne-init directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Step 1: Ask preferences / Step 2: Run the init command / Step 3: Launch the dashboard” before expanding.

Boundaries And Review

  • Dependencies: It usually needs no extra API key, so start with a small validation task.
  • Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

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