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skill-params.md

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Aegis Skill Platform Parameters

Aegis automatically injects these environment variables to every skill process. Skills should not ask users to configure these — they are provided by the platform.

Platform Parameters (auto-injected)

Env Var Type Description
AEGIS_GATEWAY_URL string LLM gateway endpoint (e.g. http://localhost:5407). Proxies to whatever LLM provider the user has configured (OpenAI, Anthropic, local). Skills should use this for all LLM calls — it handles auth, routing, and model selection.
AEGIS_VLM_URL string Local VLM (Vision Language Model) server endpoint (e.g. http://localhost:5405). Available when the user has a local VLM running.
AEGIS_SKILL_ID string The skill's unique identifier (e.g. home-security-benchmark).
AEGIS_SKILL_PARAMS JSON string User-configured parameters from config.yaml (see below).
AEGIS_PORTS JSON string All Aegis service ports as JSON. Use the URL vars above instead of parsing this directly.

User Parameters (from config.yaml)

Skills can define user-configurable parameters in a config.yaml file alongside SKILL.md. Aegis parses this at install time and renders a config panel in the UI. User values are passed as JSON via AEGIS_SKILL_PARAMS.

config.yaml Format

params:
  - key: mode
    label: Test Mode
    type: select
    options: [option1, option2, option3]
    default: option1
    description: "Human-readable description shown in the config panel"

  - key: verbose
    label: Verbose Output
    type: boolean
    default: false
    description: "Enable detailed logging"

  - key: threshold
    label: Confidence Threshold
    type: number
    default: 0.7
    description: "Minimum confidence score (0.0–1.0)"

  - key: apiEndpoint
    label: Custom API Endpoint
    type: string
    default: ""
    description: "Optional override for external API"

Supported types: string, boolean, select, number

Reading config.yaml in Your Skill

// Node.js — parse AEGIS_SKILL_PARAMS
let skillParams = {};
try { skillParams = JSON.parse(process.env.AEGIS_SKILL_PARAMS || '{}'); } catch {}

const mode = skillParams.mode || 'default';
const verbose = skillParams.verbose || false;
# Python — parse AEGIS_SKILL_PARAMS
import os, json

skill_params = json.loads(os.environ.get('AEGIS_SKILL_PARAMS', '{}'))
mode = skill_params.get('mode', 'default')
verbose = skill_params.get('verbose', False)

Precedence

CLI flags > AEGIS_SKILL_PARAMS > Platform env vars > Defaults

When a skill supports both CLI arguments and AEGIS_SKILL_PARAMS, CLI flags should take priority. Platform-injected env vars (like AEGIS_GATEWAY_URL) are always available regardless of config.yaml.

Gateway as Proxy

The gateway (AEGIS_GATEWAY_URL) is an OpenAI-compatible proxy. Skills call it like any OpenAI endpoint — the gateway handles:

  • API key management — user configures keys in Aegis settings
  • Provider routing — OpenAI, Anthropic, local models
  • Model selection — user picks model in Aegis UI

Skills should not need raw API keys. If a skill needs direct provider access in the future, Aegis will expose additional env vars (AEGIS_LLM_API_KEY, AEGIS_LLM_PROVIDER, etc.) — but this is not yet implemented.

Example: Calling the Gateway

const gatewayUrl = process.env.AEGIS_GATEWAY_URL || 'http://localhost:5407';

const response = await fetch(`${gatewayUrl}/v1/chat/completions`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
        messages: [{ role: 'user', content: 'Hello' }],
        stream: false,
    }),
});

No API key header needed — the gateway injects it.