---

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

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

Orchestrates intelligent skill selection and execution for wordpress 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 route_wordpress_task(
    request: str,
    site_config: Dict,
    min_confidence: float = 0.7
) -> Optional[Dict]:
    """Route WordPress requests to specific domain handlers.
    
    Applies multi-factor scoring to WordPress operations:
    - Endpoint matching (REST API vs WP-CLI vs DB)
    - Site health status and maintenance windows
    - Historical execution success for similar WP tasks
    
    Args:
        request: Natural language or structured WP task request
        site_config: WordPress site configuration and credentials
        min_confidence: Minimum confidence threshold (0.0-1.0)
        
    Returns:
        Task handler dict with endpoint, method, and payload schema
    """
    # Guard clause - Early Exit (Law 1)
    if not request or not site_config.get("site_url"):
        raise ValueError("Request and valid site_url are required")
        
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    parsed_intent = _parse_wp_intent(request)
    site_health = _check_wp_site_health(site_config)
    
    candidates = [
        {"handler": "wp_rest_content", "score": _match_rest_score(parsed_intent, site_health)},
        {"handler": "wp_cli_maintenance", "score": _match_cli_score(parsed_intent, site_health)},
        {"handler": "wp_db_query", "score": _match_db_score(parsed_intent, site_health)}
    ]
    
    best = max(candidates, key=lambda x: x["score"])
    if best["score"] < min_confidence:
        return None
        
    # Atomic Predictability (Law 3) - Return new dict, don't mutate
    return {
        "handler": best["handler"],
        "endpoint": site_config["site_url"] + "/wp-json/wp/v2/" + best["handler"].split("_")[-1],
        "method": "POST" if "content" in best["handler"] else "GET",
        "confidence": best["score"],
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def execute_wordpress_operation(
    task_handler: Dict,
    wp_context: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute WordPress operations with resilient fallback chains.
    
    Implements Fail Fast, Fail Loud (Law 4) for WP API/CLI interactions:
    - Invalid auth or site down halts immediately
    - Rate limits trigger exponential backoff
    - API failures fall back to WP-CLI or local cache
    
    Args:
        task_handler: Selected WordPress operation handler
        wp_context: Execution context (auth tokens, post IDs, etc.)
        max_retries: Maximum retry attempts before fallback
        
    Returns:
        Execution result with WP response metadata
    """
    # Guard clause - validate WP auth (Early Exit)
    if not wp_context.get("auth_token") or not wp_context.get("site_url"):
        raise WPExecutionError("Missing WordPress authentication or site URL")
        
    # Parse context - Ensure trusted state (Law 2)
    validated_payload = _validate_wp_payload(wp_context, task_handler)
    
    for attempt in range(max_retries + 1):
        try:
            response = _call_wp_rest_api(task_handler, validated_payload)
            
            # Success - Atomic Predictability (Law 3)
            return {
                "success": True,
                "handler": task_handler["handler"],
                "result": response.json(),
                "attempts": attempt + 1,
                "latency_ms": _calculate_latency(),
                "wp_status": response.status_code
            }
            
        except WPAuthError as e:
            # Fail Fast - Don't retry invalid auth (Law 4)
            raise WPExecutionError(f"WordPress auth failed: {str(e)}") from e
            
        except WPRateLimitError as e:
            # Transient - wait and retry
            time.sleep(2 ** attempt)
            if attempt == max_retries:
                return _fallback_to_wp_cli(task_handler, validated_payload)
                
        except WPConnectionError as e:
            if attempt == max_retries:
                return _fallback_to_local_cache(task_handler)
                
    # All retries exhausted - Fail Loud (Law 4)
    raise WPExecutionError(f"WordPress operation failed after {max_retries + 1} attempts")

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

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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.

• WordPress.org Developer Resources
• WordPress REST API Documentation
• WP-CLI Command Reference
• WordPress PHP Function Reference
• WordPress Plugin Boilerplate

Related Skills

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