---

name: gemini-api-integration compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent gemini api integration 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: gemini-api-integration, gemini api integration, how do i gemini-api-integration, orchestrate gemini-api-integration, automate gemini-api-integration, agent gemini-api-integration 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"

---

Gemini Api Integration

Orchestrates intelligent skill selection and execution for gemini api integration 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 build_gemini_request(
    task_description: str,
    system_prompt: str,
    model: str = "gemini-1.5-pro",
    max_tokens: int = 2048,
    temperature: float = 0.7
) -> Dict:
    """Construct a validated Gemini API request payload.
    
    Applies Law 2 (Parse at boundary) by validating inputs against Gemini's constraints.
    Returns a new dict structure representing the API call.
    """
    if not task_description or not task_description.strip():
        raise ValueError("Task description cannot be empty")
        
    supported_models = ["gemini-pro", "gemini-1.5-pro", "gemini-1.5-flash"]
    if model not in supported_models:
        raise ValueError(f"Unsupported model: {model}. Use one of {supported_models}")
        
    clean_prompt = task_description.strip()
    if len(clean_prompt) > 30000:
        clean_prompt = clean_prompt[:30000]
        
    request_payload = {
        "contents": [
            {"role": "user", "parts": [{"text": clean_prompt}]}
        ],
        "generationConfig": {
            "maxOutputTokens": max_tokens,
            "temperature": temperature,
            "topK": 40,
            "topP": 0.95
        },
        "safetySettings": [
            {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
            {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}
        ]
    }
    
    if system_prompt:
        request_payload["system_instruction"] = {"parts": [{"text": system_prompt}]}
        
    return request_payload

Pattern 2: Execution with Fallback

def execute_gemini_call(
    request_payload: Dict,
    api_key: str,
    fallback_model: str = "gemini-1.5-flash",
    max_retries: int = 2
) -> Dict:
    """Execute a Gemini API call with domain-specific fallback chain.
    
    Implements Law 4 (Fail Fast/Loud) for API errors.
    Fallback: 1. Retry with exponential backoff -> 2. Switch to faster/cheaper model -> 3. Return cached/error
    """
    import time
    import requests
    
    model_name = request_payload["generationConfig"].get("model", "gemini-1.5-pro")
    url = f"https://generativelanguage.googleapis.com/v1beta/models/{model_name}:generateContent"
    headers = {"Content-Type": "application/json"}
    
    for attempt in range(max_retries + 1):
        try:
            response = requests.post(
                url,
                headers=headers,
                params={"key": api_key},
                json=request_payload,
                timeout=30
            )
            
            if response.status_code == 200:
                return {
                    "success": True,
                    "model_used": model_name,
                    "candidates": response.json().get("candidates", []),
                    "usage_metadata": response.json().get("usageMetadata", {}),
                    "attempts": attempt + 1
                }
                
            if response.status_code == 401:
                raise ValueError("Invalid Gemini API key")
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
                time.sleep(retry_after)
                continue
                
        except requests.exceptions.RequestException as e:
            if attempt == max_retries:
                return _apply_gemini_fallback(request_payload, api_key, fallback_model)
            time.sleep(2 ** attempt)
            
    return {"success": False, "error": "All retries exhausted", "fallback_triggered": True}

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 | |