---

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

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

---

Skill Installer

Orchestrates intelligent skill selection and execution for skill installer 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 score_and_select_skill(task: str, registry: list[dict]) -> dict | None:
    """Domain-specific skill selection using multi-factor scoring.
    
    Calculates weighted scores based on trigger overlap, historical success rate,
    and current system availability. Returns the highest-scoring skill that
    meets the minimum confidence threshold.
    """
    if not task or not registry:
        return None
        
    task_tokens = set(task.lower().split())
    best_match = None
    best_score = 0.0
    min_confidence = 0.75
    
    for skill in registry:
        # Factor 1: Trigger/Keyword overlap
        trigger_tokens = set(skill.get("triggers", "").lower().split())
        overlap = len(task_tokens & trigger_tokens) / max(len(trigger_tokens), 1)
        
        # Factor 2: Historical success rate
        history = skill.get("execution_history", [])
        success_rate = sum(1 for r in history if r.get("status") == "success") / max(len(history), 1)
        
        # Factor 3: Availability & Load
        availability = 1.0 if skill.get("status") == "active" else 0.0
        
        # Weighted multi-factor score
        score = (overlap * 0.5) + (success_rate * 0.3) + (availability * 0.2)
        
        if score > best_score and score >= min_confidence:
            best_score = score
            best_match = {
                "name": skill["name"],
                "confidence": round(score, 3),
                "factors": {"overlap": round(overlap, 2), "history": round(success_rate, 2), "avail": availability}
            }
            
    return best_match

Pattern 2: Execution with Fallback

def run_skill_with_fallback(skill_config: dict, context: dict) -> dict:
    """Domain-specific execution wrapper implementing the 2-level fallback chain.
    
    Executes the selected skill, handles transient failures with retries,
    falls back to alternative skills from the registry, and logs outcomes.
    """
    max_retries = 2
    fallback_registry = context.get("fallback_skills", [])
    
    for attempt in range(max_retries + 1):
        try:
            # Execute primary skill
            result = _invoke_skill(skill_config["name"], context)
            return {
                "status": "success",
                "skill": skill_config["name"],
                "attempts": attempt + 1,
                "output": result,
                "timestamp": time.time()
            }
        except TransientNetworkError as e:
            if attempt < max_retries:
                continue  # Retry with exponential backoff
            # Fallback 1: Try alternative skill from registry
            for alt in fallback_registry:
                try:
                    alt_result = _invoke_skill(alt["name"], context)
                    return {
                        "status": "fallback_success",
                        "original_skill": skill_config["name"],
                        "fallback_skill": alt["name"],
                        "output": alt_result,
                        "timestamp": time.time()
                    }
                except Exception:
                    continue
            # Fallback 2: Defer to human operator
            return {
                "status": "deferred",
                "reason": "All automated fallbacks exhausted",
                "skill": skill_config["name"],
                "context": context,
                "timestamp": time.time()
            }

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-lifecycle-management	Manages the full lifecycle after installation — deprecation, updates, and versioning
skill-router-system	The routing system that uses installed skills at runtime — complementary to installation workflows

---

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.

• npm Package Installation Patterns — npm documentation on package installation, versioning, and dependency management
• PyPI Package Management — Python packaging tutorial covering pip install, virtual environments, and dependency resolution
• GitHub Actions for Package Installation — GitHub Actions patterns for automated package/skill installation in CI/CD workflows
• Container Image Layer Optimization — Docker documentation on optimizing image layer installation and caching strategies
• Software Dependency Management (OWASP) — OWASP guidance on managing software dependencies securely