| 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"} |
| name | acceptance-orchestrator |
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
┌───────────────────────────────────────────────────────────────────────────────┐
│ 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_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
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 |
|---|
closed-loop-delivery | End-to-end delivery pipeline with feedback loops |
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.
