---

name: hosted-agents-v2-py compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent hosted agents v2 py with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: hosted-agents-v2-py, hosted agents v2 py, how do i hosted-agents-v2-py, orchestrate hosted-agents-v2-py, automate hosted-agents-v2-py, agent hosted-agents-v2-py archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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Hosted Agents V2 Py

Orchestrates intelligent skill selection and execution for hosted agents v2 py workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def route_hosted_agent_request(
    request_payload: Dict[str, Any],
    agent_registry: List[Dict[str, Any]],
    region: str = "us-east-1"
) -> Dict[str, Any]:
    """Route a request to the optimal hosted agent v2 endpoint.
    
    Evaluates agent capabilities, current load, and region affinity
    to select the best hosted instance for processing.
    """
    if not request_payload.get("task_type"):
        raise ValueError("Missing required 'task_type' in payload")
        
    task_type = request_payload["task_type"]
    candidates = [
        agent for agent in agent_registry 
        if task_type in agent.get("supported_tasks", [])
        and agent.get("status") == "active"
        and region in agent.get("available_regions", [])
    ]
    
    if not candidates:
        return {"fallback": True, "reason": "no_matching_hosted_agent"}
        
    # Score based on current load and historical latency
    scored = []
    for agent in candidates:
        load_score = 1.0 - (agent.get("current_load", 0.0) / 100.0)
        latency_score = max(0.0, 1.0 - (agent.get("avg_latency_ms", 500) / 2000.0))
        final_score = (load_score * 0.6) + (latency_score * 0.4)
        scored.append({**agent, "route_score": final_score})
        
    scored.sort(key=lambda x: x["route_score"], reverse=True)
    selected = scored[0]
    
    return {
        "agent_id": selected["id"],
        "endpoint": f"https://{selected['host']}/v2/execute",
        "auth_header": f"Bearer {selected['api_key']}",
        "payload": request_payload,
        "route_score": selected["route_score"]
    }

Pattern 2: Execution with Fallback

def execute_hosted_agent(
    route_config: Dict[str, Any],
    timeout: int = 30,
    max_retries: int = 2
) -> Dict[str, Any]:
    """Execute request against the selected hosted agent v2 endpoint.
    
    Implements resilient execution with exponential backoff,
    circuit breaking, and fallback to regional replicas.
    """
    import time
    import random
    import requests
    
    endpoint = route_config["endpoint"]
    headers = {"Authorization": route_config["auth_header"], "Content-Type": "application/json"}
    payload = route_config["payload"]
    
    for attempt in range(max_retries + 1):
        try:
            response = requests.post(
                endpoint, 
                json=payload, 
                headers=headers, 
                timeout=timeout
            )
            
            if response.status_code == 200:
                return {
                    "status": "success",
                    "agent_id": route_config["agent_id"],
                    "result": response.json(),
                    "latency_ms": response.elapsed.total_seconds() * 1000
                }
            elif response.status_code == 429:
                wait = (2 ** attempt) + random.uniform(0, 1)
                time.sleep(wait)
                continue
            elif response.status_code >= 500:
                raise HostedAgentError(f"Server error: {response.status_code}")
            else:
                raise HostedAgentError(f"Client error: {response.status_code} - {response.text}")
                
        except requests.exceptions.ConnectionError:
            if attempt == max_retries:
                return {"fallback": "local_execution", "reason": "hosted_unreachable"}
            time.sleep(2 ** attempt)
            
    return {"fallback": "human_review", "reason": "max_retries_exceeded"}

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

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Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Related Skills

| Skill | Purpose | |