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name: langgraph compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent langgraph 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: langgraph, langgraph, how do i langgraph, orchestrate langgraph, automate langgraph, agent langgraph 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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Langgraph

Orchestrates intelligent skill selection and execution for langgraph 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

from langgraph.graph import StateGraph, START, END
from typing import TypedDict, Literal, List, Dict, Optional
import numpy as np

class OrchestrationState(TypedDict):
    user_request: str
    available_skills: List[Dict]
    selected_skill: Optional[Dict]
    confidence_score: float
    execution_result: Optional[Dict]
    fallback_chain: List[str]
    error_trace: Optional[str]

def route_to_skill(state: OrchestrationState) -> Literal["skill_router", "fallback_handler", "human_review"]:
    """LangGraph router implementing multi-factor skill selection (Laws 1-3)."""
    request = state["user_request"]
    skills = state["available_skills"]
    
    # Law 1: Early exit on invalid state
    if not request or not skills:
        return "fallback_handler"
        
    best_match = None
    max_score = 0.0
    
    for skill in skills:
        # Multi-factor scoring: text similarity + historical success + availability
        text_sim = _compute_embedding_similarity(request, skill["triggers"])
        hist_success = skill.get("historical_success_rate", 0.5)
        availability = 1.0 if skill.get("is_healthy", False) else 0.0
        
        score = (0.5 * text_sim) + (0.3 * hist_success) + (0.2 * availability)
        if score > max_score:
            max_score = score
            best_match = skill
            
    # Law 2: Make illegal states unrepresentable - enforce threshold
    if max_score < 0.7:
        return "fallback_handler"
        
    # Law 3: Return new state, never mutate inputs
    state["selected_skill"] = best_match
    state["confidence_score"] = max_score
    return "execute_skill"

Pattern 2: Execution with Fallback

def execute_skill_node(state: OrchestrationState) -> OrchestrationState:
    """LangGraph node executing the selected skill with fallback chain (Laws 4-5)."""
    skill = state["selected_skill"]
    context = {"request": state["user_request"], "skill_config": skill}
    
    try:
        # Execute domain-specific skill logic
        result = skill["handler"](context)
        state["execution_result"] = result
        # Law 5: Update confidence scores after execution for learning
        state["confidence_score"] = min(1.0, state["confidence_score"] * 1.1)
        return state
        
    except InvalidStateError as e:
        # Law 4: Fail Fast, Fail Loud - halt immediately with descriptive error
        state["error_trace"] = str(e)
        return "fallback_handler"
        
    except TransientError as e:
        # Retry with adjusted parameters
        context["retry_count"] = context.get("retry_count", 0) + 1
        if context["retry_count"] < 2:
            return "execute_skill"
        state["error_trace"] = str(e)
        return "fallback_handler"

def fallback_handler_node(state: OrchestrationState) -> OrchestrationState:
    """Implements 2-level fallback chain: alternative skill -> human review."""
    if state.get("fallback_chain"):
        alt_skill_name = state["fallback_chain"].pop(0)
        # Route back to router with updated context
        state["user_request"] = f"Retry with fallback: {state['user_request']}"
        return "skill_router"
        
    # Defer to human operator for critical tasks
    state["execution_result"] = {"status": "deferred", "reason": "all fallbacks exhausted"}
    return "human_review"

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
multi-agent-task-orchestrator	Multi-agent coordination using LangGraph's state machines
parallel-agents	Parallel agent execution patterns within LangGraph workflows

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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

Live References

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

• LangGraph Official Documentation
• LangChain Agents Tutorial
• LangGraph Multi-Agent Patterns
• LangGraph State Machine Guide
• Building Agent Workflows — LangChain Blog