# Pipecat Friday Agent > Implements intelligent pipecat friday agent with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/pipecat-friday-agent` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/pipecat-friday-agent` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/pipecat-friday-agent/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/pipecat-friday-agent --- # 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 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 ```python 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 ```python 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: 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 ## 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](https://docs.pipecat.ai) — Official documentation for the Pipecat real-time audio/video framework - [Pipecat GitHub Repository](https://github.com/pipecat-ai/pipecat) — Source code, examples, and contribution guidelines - [WebRTC Real-Time Communication](https://webrtc.org/getting-started/overview) — Foundational protocol documentation for real-time streaming used by Pipecat - [VAD (Voice Activity Detection) Algorithms](https://github.com/wiseman/py-webrtcvad) — Voice activity detection reference implementation commonly used in audio pipelines - [STT/TTS Integration Patterns for Real-Time AI](https://platform.openai.com/docs/guides/speech-to-text) — Best practices for integrating speech recognition and synthesis in streaming applications