---

name: acceptance-orchestrator compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent acceptance orchestrator 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: acceptance-orchestrator, acceptance orchestrator, how do i acceptance-orchestrator, orchestrate acceptance-orchestrator, automate acceptance-orchestrator, agent acceptance-orchestrator 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"

---

Acceptance Orchestrator

Orchestrates intelligent skill selection and execution for acceptance orchestrator 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 evaluate_and_select_skill(task_context: dict, skill_registry: list) -> dict:
    """Multi-factor scoring for acceptance orchestrator selection.
    
    Implements Law 2 (Make Illegal States Unrepresentable) by validating
    context boundaries before scoring. Returns immutable selection metadata.
    """
    if not task_context.get("intent"):
        raise ValueError("Missing intent in task context")
        
    scored_candidates = []
    for skill in skill_registry:
        semantic_score = _compute_semantic_similarity(task_context["intent"], skill["triggers"])
        history_score = skill.get("success_rate", 0.0)
        availability_score = 1.0 if _check_dependencies(skill["deps"]) else 0.0
        
        composite_score = (0.5 * semantic_score) + (0.3 * history_score) + (0.2 * availability_score)
        
        if composite_score >= 0.7:
            scored_candidates.append({
                "skill_id": skill["id"],
                "composite_score": round(composite_score, 3),
                "factors": {"semantic": semantic_score, "history": history_score, "availability": availability_score}
            })
    
    if not scored_candidates:
        return {"status": "no_match", "fallback_triggered": True}
        
    scored_candidates.sort(key=lambda x: x["composite_score"], reverse=True)
    selected = scored_candidates[0]
    
    _log_selection_audit(task_context["request_id"], selected)
    return {"status": "selected", "skill": selected}

Pattern 2: Execution with Fallback

def execute_with_resilience_chain(skill_config: dict, execution_context: dict) -> dict:
    """Orchestrates execution with a strict fallback chain and confidence tracking.
    
    Enforces Law 4 (Fail Fast, Fail Loud) by halting on acceptance violations.
    Implements Law 3 (Atomic Predictability) by returning new result structures.
    """
    max_retries = execution_context.get("max_retries", 2)
    current_attempt = 0
    
    while current_attempt <= max_retries:
        try:
            result = _invoke_skill_endpoint(skill_config["endpoint"], execution_context)
            
            if not _validate_acceptance_criteria(result, skill_config["acceptance_rules"]):
                raise AcceptanceValidationError("Result failed acceptance criteria")
                
            confidence = _calculate_execution_confidence(result["latency_ms"], current_attempt)
            _update_skill_confidence(skill_config["id"], confidence)
            
            return {
                "status": "success",
                "result": result,
                "confidence": confidence,
                "attempts": current_attempt + 1
            }
            
        except AcceptanceValidationError as e:
            _log_failure(skill_config["id"], "acceptance_violation", str(e))
            raise
            
        except TransientNetworkError as e:
            current_attempt += 1
            if current_attempt > max_retries:
                alt_skill = _resolve_fallback_skill(skill_config["related_skills"])
                if alt_skill:
                    skill_config = alt_skill
                    current_attempt = 0
                    continue
                else:
                    return _escalate_to_human(execution_context, skill_config["id"])
                    
        except Exception as e:
            _log_failure(skill_config["id"], "unexpected", str(e))
            raise OrchestratorExecutionError(f"Failed after {current_attempt} attempts") from e

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
closed-loop-delivery	End-to-end delivery pipeline with feedback loops

---

---

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.

• Practical Agile — Martin Fowler
• Acceptance Criteria Patterns (BDD/SpecByExample)
• Definition of Done — Scrum Guide 2020
• Behavior-Driven Development with Gherkin Syntax
• Acceptance Test-Driven Development (ATDD) Practices