pipecat-friday-agent - SKILL.md Agent Skill

name: pipecat-friday-agent compatibility: opencode completeness: 95 content-types:

guidance
examples
do-dont description: Implements intelligent pipecat friday agent 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: pipecat-friday-agent, pipecat friday agent, how do i pipecat-friday-agent, orchestrate pipecat-friday-agent, automate pipecat-friday-agent, agent pipecat-friday-agent 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"

Pipecat Friday Agent

Orchestrates intelligent skill selection and execution for pipecat friday agent 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

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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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 evaluate_pipecat_skill_candidates(
    request: PipecatRequest,
    available_tools: List[ToolMetadata],
    session_state: SessionContext
) -> Optional[ToolMetadata]:
    """Evaluate and score available Pipecat tools for the current request.
    
    Applies multi-factor scoring tailored to real-time voice/agent workflows:
    - Semantic match between request intent and tool capabilities
    - Real-time latency and throughput metrics
    - Historical success rate within the current session context
    - Dependency health (e.g., LLM provider, TTS engine, STT service)
    """
    if not request.intent or not available_tools:
        raise ValueError("Request intent and available tools are required")
    
    scored_candidates = []
    for tool in available_tools:
        # Calculate semantic relevance using request embeddings vs tool capabilities
        semantic_score = _compute_embedding_similarity(request.intent, tool.capabilities)
        
        # Factor in real-time system health and historical session performance
        health_score = tool.metrics.get("current_latency_ms", 9999) / 1000.0
        history_score = session_state.get_tool_history(tool.name, window="24h").success_rate
        
        # Weighted composite score
        composite = (0.5 * semantic_score) + (0.3 * min(1.0, 1.0 / max(health_score, 0.1))) + (0.2 * history_score)
        
        if composite >= 0.65:
            scored_candidates.append({
                "tool": tool,
                "score": composite,
                "latency_ms": tool.metrics.get("current_latency_ms"),
                "confidence": history_score
            })
    
    if not scored_candidates:
        return None
        
    scored_candidates.sort(key=lambda x: x["score"], reverse=True)
    return scored_candidates[0]["tool"]

Pattern 2: Execution with Fallback

def run_pipecat_agent_workflow(
    selected_tool: ToolMetadata,
    request: PipecatRequest,
    session_state: SessionContext,
    fallback_chain: List[ToolMetadata]
) -> AgentResponse:
    """Execute the selected Pipecat tool with domain-specific fallback handling.
    
    Implements resilient execution for real-time voice/agent interactions:
    1. Direct execution with timeout and circuit breaker
    2. Fallback to alternative tool if primary fails or degrades
    3. Graceful degradation to text-only if voice pipeline fails
    4. Human handoff for critical/unhandled intents
    """
    try:
        # Execute with strict timeout to maintain real-time UX
        response = selected_tool.execute(
            payload=request.payload,
            context=session_state,
            timeout_ms=3000
        )
        
        # Validate response integrity before returning
        if not response.is_valid():
            raise ToolValidationError(f"Invalid response from {selected_tool.name}")
            
        # Update session history for adaptive routing
        session_state.record_execution(selected_tool.name, success=True, latency=response.latency_ms)
        return AgentResponse(success=True, data=response, tool=selected_tool.name)
        
    except TimeoutError:
        session_state.record_execution(selected_tool.name, success=False, latency=3000)
        return _apply_pipecat_fallback(selected_tool, fallback_chain, request, session_state)
        
    except ToolValidationError as e:
        session_state.record_execution(selected_tool.name, success=False, latency=0)
        raise AgentExecutionError(f"Pipeline validation failed: {e}") from e
        
    except Exception as e:
        session_state.record_execution(selected_tool.name, success=False, latency=0)
        return _apply_pipecat_fallback(selected_tool, fallback_chain, request, session_state)

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:

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

Related Skills

Skill	Purpose
`langgraph-implementation`	Provides LangGraph-specific implementation patterns that Pipecat integrates with
`real-time-audio-processing`	Covers audio pipeline design that complements Pipecat's streaming architecture

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

Live References

Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content.

Pipecat Documentation — Official documentation for the Pipecat real-time audio/video framework
Pipecat GitHub Repository — Source code, examples, and contribution guidelines
WebRTC Real-Time Communication — Foundational protocol documentation for real-time streaming used by Pipecat
VAD (Voice Activity Detection) Algorithms — Voice activity detection reference implementation commonly used in audio pipelines
STT/TTS Integration Patterns for Real-Time AI — Best practices for integrating speech recognition and synthesis in streaming applications