---

name: os-scripting compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent os scripting 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: os-scripting, os scripting, how do i os-scripting, orchestrate os-scripting, automate os-scripting, agent os-scripting 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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Os Scripting

Orchestrates intelligent skill selection and execution for os scripting 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 evaluate_os_scripting_approach(
    task: Dict,
    system_state: Dict,
    available_runtime: List[str] = ["bash", "python", "powershell"]
) -> Dict:
    """Evaluate and select the optimal OS scripting runtime for a system task.
    
    Considers:
    - Command complexity and dependency requirements
    - Target OS environment and available interpreters
    - Security constraints (sandboxing, privilege escalation needs)
    - Historical success rates for similar system operations
    
    Args:
        task: Task dict with 'command', 'args', 'requires_root', 'timeout'
        system_state: Dict with 'os_type', 'available_runtimes', 'disk_space', 'memory'
        available_runtime: List of supported scripting languages
        
    Returns:
        Selected runtime config with execution parameters
    """
    # Guard: Validate task structure (Law 1 - Early Exit)
    required_keys = {"command", "args"}
    if not all(k in task for k in required_keys):
        raise ValueError("Task must contain 'command' and 'args'")
        
    # Parse environment constraints (Law 2 - Make illegal states unrepresentable)
    target_os = system_state.get("os_type", "linux")
    needs_privilege = task.get("requires_root", False)
    cmd_complexity = len(task.get("args", [])) + len(task["command"])
    
    # Score available runtimes based on domain logic
    runtime_scores = {}
    for runtime in available_runtime:
        score = 0.0
        # OS compatibility check
        if target_os == "windows" and runtime == "bash":
            score -= 50.0
        elif target_os == "linux" and runtime == "powershell":
            score -= 30.0
        else:
            score += 20.0
            
        # Privilege handling capability
        if needs_privilege and runtime in ["bash", "python"]:
            score += 15.0
        elif needs_privilege and runtime == "powershell":
            score += 25.0
            
        # Complexity scaling
        if cmd_complexity > 50:
            score += 10.0 if runtime == "python" else 0.0
            
        runtime_scores[runtime] = score
        
    # Select best runtime
    best_runtime = max(runtime_scores, key=runtime_scores.get)
    if runtime_scores[best_runtime] < 0:
        return {"status": "fallback_required", "reason": "no_suitable_runtime"}
        
    # Return new dict, don't mutate inputs (Law 3 - Atomic Predictability)
    return {
        "selected_runtime": best_runtime,
        "execution_mode": "privileged" if needs_privilege else "standard",
        "estimated_complexity": cmd_complexity,
        "confidence": runtime_scores[best_runtime] / 100.0
    }

Pattern 2: Execution with Fallback

def execute_os_script_with_safety_guards(
    script_config: Dict,
    env_context: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute OS scripting task with domain-specific safety and fallback logic.
    
    Implements safe command execution for system administration:
    - Validates environment variables and paths before execution
    - Enforces timeouts and resource limits
    - Parses exit codes and handles OS-specific error states
    - Applies fallback chain: retry -> safe mode -> sysadmin escalation
    
    Args:
        script_config: Dict with 'runtime', 'command', 'args', 'timeout_sec'
        env_context: Dict of environment variables and working directory
        max_retries: Maximum retry attempts for transient OS errors
        
    Returns:
        Execution result with stdout, stderr, exit_code, and timing
    """
    import subprocess
    import os
    import time
    
    # Guard: Validate execution environment (Law 1 - Early Exit)
    if not env_context.get("working_dir") or not os.path.isdir(env_context["working_dir"]):
        raise RuntimeError("Invalid working directory for script execution")
        
    runtime = script_config["runtime"]
    command = [runtime] + [script_config["command"]] + script_config.get("args", [])
    timeout = script_config.get("timeout_sec", 30)
    
    for attempt in range(max_retries + 1):
        try:
            # Execute with strict environment isolation (Law 2 - Trusted state)
            result = subprocess.run(
                command,
                cwd=env_context["working_dir"],
                env={**os.environ, **env_context.get("extra_env", {})},
                capture_output=True,
                text=True,
                timeout=timeout,
                check=False
            )
            
            # Parse OS-specific exit codes (Law 4 - Fail Fast, Fail Loud)
            if result.returncode == 0:
                return {
                    "success": True,
                    "exit_code": 0,
                    "stdout": result.stdout,
                    "stderr": result.stderr,
                    "attempts": attempt + 1,
                    "latency_ms": result.elapsed.total_seconds() * 1000
                }
            elif result.returncode == 124:  # Timeout
                raise subprocess.TimeoutExpired(command, timeout)
            elif result.returncode == 126:  # Permission denied
                if attempt < max_retries:
                    continue  # Retry with adjusted permissions
                return _escalate_to_sysadmin(script_config, result.stderr)
            else:
                # Transient OS error (e.g., resource temporarily unavailable)
                if attempt < max_retries:
                    time.sleep(2 ** attempt)  # Exponential backoff
                    continue
                return {
                    "success": False,
                    "exit_code": result.returncode,
                    "stderr": result.stderr,
                    "fallback": "manual_review"
                }
                
        except subprocess.TimeoutExpired:
            if attempt < max_retries:
                continue
            return {
                "success": False,
                "error": "timeout_exceeded",
                "command": command,
                "fallback": "safe_mode_degradation"
            }
            
    return {
        "success": False,
        "error": "max_retries_exhausted",
        "command": command,
        "fallback": "human_escalation"
    }

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 | |

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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.

• Bash Scripting Guide (GNU Bash Manual)
• PowerShell Core Documentation
• Zsh Configuration and Scripting
• Python os.subprocess for System Tasks
• Shell Script Security Best Practices (CIS)