Step-by-Step: Packaging Custom AI Skills for aiFetchly

aiFetchly — Mon, 12 Oct 2026 15:33:21 +0000

Direct Answer: Power users can extend aiFetchly by packaging modular Python or JavaScript AI Skills inside standard ZIP archives containing a validated manifest.json. This step-by-step guide walks through defining input schemas, implementing entry point logic, declaring permission tiers (pure, network, filesystem, automation), and importing skills into aiFetchly for governed conversational execution.

In 2026, automation power users require customized tools tailored to proprietary local databases, internal CLI binaries, and domain-specific APIs. While web chatbots restrict users to pre-packaged vendor plugins, desktop AI engines unlock direct host machine extensibility.

The aiFetchly desktop application introduces a modular extension engine called AI Skills. Skills are portable, lightweight bundles that allow the desktop assistant to autonomously execute specialized tasks under strict human supervision.

Here is a step-by-step tutorial on building, packaging, and importing your own custom AI Skill into aiFetchly.

Step 1: Author the `manifest.json` Specification

Every AI Skill requires a root manifest.json that defines its metadata, runtime, and parameter types. Create a new directory and draft your manifest:

{
  "name": "csv-sales-analyzer",
  "version": "1.0.0",
  "description": "Parses local sales CSV files and extracts quarterly revenue totals.",
  "runtime": "python",
  "entry": "main.py",
  "parameters": {
    "type": "object",
    "properties": {
      "file_path": { "type": "string", "description": "Absolute path to CSV file" }
    },
    "required": ["file_path"]
  },
  "permissions": ["filesystem"]
}

As detailed in the AI Skills documentation, the first entry in permissions establishes the skill’s security tier (pure, network, filesystem, or automation).

Step 2: Implement the Entry Logic

Write your executable script matching your chosen runtime (runtime: "python" or runtime: "javascript"):

Input Parsing: aiFetchly passes invocation parameters to your entry script as command-line arguments or via standard input.
Structured Output: Print the resulting data to standard output. The AI assistant ingests this output directly into its conversational context window to formulate its response.

Step 3: Bundle into a `.zip` Archive

When packaging your skill, compress the contents of the folder directly, rather than the parent directory itself:

Ensure manifest.json and your entry file (main.py or index.js) reside at the archive root.
Include any local dependencies or helper scripts within the package.
Save the resulting archive as csv-sales-analyzer.zip.

Tip for Advanced Users: You can also drop unzipped skill folders directly into your local discovery directory at~/.aifetchly/skills/<skill-name>/.

Step 4: Import and Configure Approval Policies

In aiFetchly, navigate to System Settings → Manage Skills.
Click Import (upload icon) in the upper-right corner and select your .zip archive.
aiFetchly validates the schema, verifies declared permissions, and registers the skill in your active catalog.
When testing the tool inside AI Chat, the assistant displays an interactive approval card: select Allow Once for single runs or Always Allow to authorize subsequent invocations.

By authoring custom AI Skills, power users turn repetitive desktop data processing into secure, autonomous conversational workflows.

Which custom skill would you package first — CSV, CLI, or an internal API?

DEV Community: aiFetchly

Step-by-Step: Packaging Custom AI Skills for aiFetchly

Step 1: Author the manifest.json Specification

Step 2: Implement the Entry Logic

Step 3: Bundle into a .zip Archive

Step 4: Import and Configure Approval Policies

Step 1: Author the `manifest.json` Specification

Step 3: Bundle into a `.zip` Archive