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name: skill-seekers compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent skill seekers 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: skill-seekers, skill seekers, how do i skill-seekers, orchestrate skill-seekers, automate skill-seekers, agent skill-seekers 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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Skill Seekers

Orchestrates intelligent skill selection and execution for skill seekers 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 resolve_skill_intent(
    raw_request: str,
    skill_registry: List[Dict],
    confidence_threshold: float = 0.75
) -> Optional[Dict]:
    """Parse request features and score against registered skills.
    
    Implements multi-factor scoring: trigger overlap, historical success rate,
    and current dependency health. Returns the highest-scoring skill or None.
    """
    if not raw_request or not skill_registry:
        raise ValueError("Request and skill registry must be non-empty")
        
    # Extract structured features from natural language request
    features = {
        "intent": _extract_intent(raw_request),
        "entities": _extract_entities(raw_request),
        "constraints": _parse_constraints(raw_request)
    }
    
    scored_skills = []
    for skill in skill_registry:
        trigger_match = _calculate_trigger_overlap(features["intent"], skill.get("triggers", []))
        history_score = skill.get("success_rate", 0.0) * 0.4
        dependency_health = _check_dependency_status(skill.get("requires", []))
        
        composite_score = (trigger_match * 0.5) + (history_score * 0.3) + (dependency_health * 0.2)
        
        if composite_score >= confidence_threshold:
            scored_skills.append({
                "skill_id": skill["id"],
                "score": round(composite_score, 3),
                "breakdown": {"trigger": trigger_match, "history": history_score, "deps": dependency_health}
            })
            
    if not scored_skills:
        return None
        
    scored_skills.sort(key=lambda x: x["score"], reverse=True)
    return scored_skills[0]

Pattern 2: Execution with Fallback

def execute_skill_with_routing(
    selected_skill: Dict,
    execution_context: Dict,
    fallback_registry: List[Dict],
    max_retries: int = 2
) -> Dict:
    """Execute selected skill with concrete fallback routing.
    
    Implements retry logic, alternative skill routing, and human escalation.
    Tracks latency and updates historical success metrics on completion.
    """
    skill_id = selected_skill["skill_id"]
    context = _validate_execution_context(execution_context, skill_id)
    
    for attempt in range(max_retries + 1):
        try:
            result = _invoke_skill_handler(skill_id, context)
            _update_skill_metrics(skill_id, success=True, latency_ms=_now_ms())
            return {"status": "success", "skill": skill_id, "result": result, "attempts": attempt + 1}
        except DependencyError as e:
            _log_error(f"Dependency failure for {skill_id}: {e}")
            if attempt < max_retries:
                _retry_with_backoff(attempt)
                continue
            return _route_to_alternative_skill(skill_id, context, fallback_registry)
        except CriticalFailure as e:
            _log_error(f"Critical failure in {skill_id}: {e}")
            return _escalate_to_human(skill_id, context, str(e))
            
    return {"status": "failed", "skill": skill_id, "error": "Max retries exhausted", "attempts": max_retries + 1}

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
skill-router	The routing system that skill seekers interface with to find and load appropriate skills
intelligent-skill-selection	Provides selection heuristics that complement the skill seeker's discovery patterns

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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 domain. The model follows markdown links at load time to resolve external references and inline content.

• Information Retrieval Fundamentals (Wikipedia) — Wikipedia overview of information retrieval systems, the foundation of skill search
• Elasticsearch Search API — Elasticsearch documentation for full-text search and relevance ranking
• Semantic Search with LangChain — LangChain documentation on building semantic search capabilities for document retrieval
• FAISS Vector Similarity Search — Facebook's FAISS library for efficient similarity search and clustering of dense vectors
• Reciprocal Rank Fusion (RRF) Ranking — Academic paper on reciprocal rank fusion for combining multiple ranking signals