# Os Scripting > Implements intelligent os scripting with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/os-scripting` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/os-scripting` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/os-scripting/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/os-scripting --- # 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 ```python 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 ```python 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 | | --- --- ## 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)]()