apiVersion: 100monkeys.ai/v1      # required; must be exactly this value
kind: AgentManifest               # required; must be exactly "AgentManifest"

metadata:
  name: python-coder              # required; unique DNS-label name (lowercase, alphanumeric, hyphens)
  version: "1.0.0"               # required; semantic version of this manifest
  description: "Writes Python solutions to programming tasks."  # optional
  labels:                         # optional; key-value pairs for filtering and discovery
    role: worker
    team: platform
  annotations:                    # optional; non-identifying metadata
    maintainer: "platform@example.com"

spec:
  # Runtime configuration
  runtime:
    language: python              # required; python | javascript | typescript | rust | go
    version: "3.11"              # required; language version string
    isolation: docker             # optional; inherit | firecracker | docker | process

  # Task definition (instructions for the agent)
  task:
    agentskills:                  # optional; pre-built skill packages
      - "code-review:python"
    instruction: |               # optional; high-level task guidance
      Write a Python solution to the given problem.
      Save the solution to /workspace/solution.py.
      Run it to verify correctness and write a JSON summary to /workspace/result.json.
    prompt_template: |           # optional; Handlebars template for LLM prompts
      {{instruction}}

      User: {{input}}
      {{#if previous_error}}
      Previous attempt failed: {{previous_error}}
      {{/if}}
      Assistant:

  # Security policy (deny-by-default; declare only what is needed)
  security:
    network:
      mode: allow                 # allow | deny | none
      allowlist:
        - pypi.org
        - api.github.com
    filesystem:
      read:
        - /workspace
        - /agent
      write:
        - /workspace
    resources:
      cpu: 1000                  # millicores (1000 = 1 CPU core)
      memory: "1Gi"             # human-readable: "512Mi", "1Gi", "2Gi"
      disk: "5Gi"               # human-readable disk quota
      timeout: "300s"           # human-readable: "300s", "5m", "1h"

  # Volume mounts (ephemeral and persistent storage)
  volumes:
    - name: workspace
      storage_class: ephemeral   # ephemeral | persistent
      mount_point: /workspace    # absolute path inside container
      access_mode: read-write    # read-write | read-only
      ttl_hours: 1              # required for ephemeral; hours until auto-deletion
      size_limit_mb: 5000       # maximum volume size in MiB

    - name: shared-data
      storage_class: persistent
      mount_point: /data
      access_mode: read-only
      source:
        volume_id: "vol-a1b2c3d4-..."  # pin to existing volume

  # Execution strategy and validation
  execution:
    mode: iterative              # one-shot | iterative
    max_iterations: 10           # range: 1–20; default: 10
    memory: false                # enable Cortex memory system
    validation:
      system:
        must_succeed: true       # require exit code 0
        allow_stderr: false      # fail on stderr output
        timeout_seconds: 90      # per-iteration execution timeout
      output:
        format: json             # json | yaml | text | csv | pdf
        schema:                  # optional JSON Schema for structural validation
          type: object
          required: ["solution_path", "output"]
          properties:
            solution_path:
              type: string
            output:
              type: string
      semantic:
        enabled: true            # auto-enabled when task.instruction is set
        model: default           # model alias from aegis-config.yaml
        threshold: 0.80          # confidence threshold (0.0–1.0)
        timeout_seconds: 30
        fallback_on_unavailable: skip  # skip | fail

  # MCP tools the agent may invoke
  tools:
    - name: filesystem
      server: "mcp:filesystem"
      config:
        allowed_paths:
          - /workspace
        access_mode: read-write
    - name: web-search
      server: "mcp:web-search"
      config:
        allowed_domains:
          - pypi.org
          - docs.python.org
        max_results_per_query: 10

  # Environment variables (static, secret references, config references)
  env:
    PYTHONUNBUFFERED: "1"
    LOG_LEVEL: "debug"
    OPENAI_API_KEY: "secret:openai-key"   # injected from secure vault

tools:
  - "mcp:filesystem"
  - "mcp:web-search"
  - "mcp:gmail"

security.resources.timeout                                    (e.g., 600s)
  └─ execution.validation.system.timeout_seconds    (e.g., 90s)  — agent code per iteration
  └─ execution.validation.script.timeout_seconds    (e.g., 30s)  — validation script
  └─ execution.validation.semantic.timeout_seconds  (e.g., 30s)  — LLM judge call

apiVersion: 100monkeys.ai/v1
kind: AgentManifest

metadata:
  name: pr-reviewer
  version: "1.0.0"
  description: "Reviews pull request diffs and returns structured feedback."

spec:
  runtime:
    language: python
    version: "3.11"

  task:
    instruction: |
      Review the provided code diff and return structured feedback covering:
      - Security vulnerabilities or concerns
      - Performance issues or opportunities
      - Code quality and maintainability
      Provide specific line references where relevant.

  security:
    network:
      mode: allow
      allowlist:
        - api.github.com
    resources:
      cpu: 1000
      memory: "1Gi"
      timeout: "300s"

  execution:
    mode: iterative
    max_iterations: 5

apiVersion: 100monkeys.ai/v1
kind: AgentManifest

metadata:
  name: data-extractor
  version: "1.0.0"

spec:
  runtime:
    language: python
    version: "3.11"

  task:
    instruction: |
      Extract structured data from the provided document and output valid JSON
      matching the required schema.

  security:
    filesystem:
      read:
        - /workspace
      write:
        - /workspace/output
    resources:
      cpu: 500
      memory: "512Mi"
      timeout: "120s"

  volumes:
    - name: workspace
      storage_class: ephemeral
      mount_point: /workspace
      access_mode: read-write
      ttl_hours: 1
      size_limit_mb: 500

  execution:
    mode: iterative
    max_iterations: 8
    validation:
      system:
        must_succeed: true
        allow_stderr: false
        timeout_seconds: 60
      output:
        format: json
        schema:
          type: object
          required: ["entities", "relationships", "confidence"]
          properties:
            entities:
              type: array
            relationships:
              type: array
            confidence:
              type: number
              minimum: 0
              maximum: 1
      semantic:
        threshold: 0.85
        fallback_on_unavailable: skip

  tools:
    - name: filesystem
      server: "mcp:filesystem"
      config:
        allowed_paths: ["/workspace"]
        access_mode: read-write

{
  "score": 0.85,
  "confidence": 0.92,
  "reasoning": "The code correctly implements the requirements with minor style issues.",
  "suggestions": ["Add type hints", "Improve error handling"],
  "verdict": "pass"
}

apiVersion: 100monkeys.ai/v1
kind: AgentManifest

metadata:
  name: code-quality-judge
  version: "1.0.0"
  labels:
    role: judge
    domain: code-review

spec:
  runtime:
    language: python
    version: "3.11"

  task:
    instruction: |
      You are a code quality judge. Evaluate the provided code output on:
      1. Correctness: Does it solve the stated problem?
      2. Code quality: Is it idiomatic and well-structured?
      3. Error handling: Does it handle edge cases?

      Always respond with valid JSON:
      {
        "score": <0.0-1.0>,
        "confidence": <0.0-1.0>,
        "reasoning": "<explanation>",
        "suggestions": ["<improvement>"],
        "verdict": "pass|fail|warning"
      }

  security:
    network:
      mode: none
    resources:
      cpu: 500
      memory: "512Mi"
      timeout: "60s"

  execution:
    mode: one-shot
    validation:
      system:
        must_succeed: true
      output:
        format: json
        schema:
          type: object
          required: ["score", "confidence", "reasoning"]
          properties:
            score:
              type: number
              minimum: 0
              maximum: 1
            confidence:
              type: number
              minimum: 0
              maximum: 1
            reasoning:
              type: string