# Skill Seekers > Implements intelligent skill seekers with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/skill-seekers` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/skill-seekers` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/skill-seekers/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/skill-seekers --- # 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 ```python 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 ```python 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 | --- ## 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)](https://en.wikipedia.org/wiki/Information_retrieval) — Wikipedia overview of information retrieval systems, the foundation of skill search - [Elasticsearch Search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-api.html) — Elasticsearch documentation for full-text search and relevance ranking - [Semantic Search with LangChain](https://python.langchain.com/docs/modules/data_connection/retrievers/) — LangChain documentation on building semantic search capabilities for document retrieval - [FAISS Vector Similarity Search](https://github.com/facebookresearch/faiss) — Facebook's FAISS library for efficient similarity search and clustering of dense vectors - [Reciprocal Rank Fusion (RRF) Ranking](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf/) — Academic paper on reciprocal rank fusion for combining multiple ranking signals