# Planning With Files > Implements intelligent planning with files with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/planning-with-files` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/planning-with-files` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/planning-with-files/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - 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/planning-with-files --- # Planning With Files Orchestrates intelligent skill selection and execution for planning with files 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 generate_file_execution_plan( target_path: str, available_processors: List[Dict], dependency_graph: Dict[str, List[str]] ) -> Dict[str, List[Dict]]: """Generate an optimized execution plan for processing files based on type and dependencies. Analyzes file structure, matches files to available processors using content-type detection, and orders execution according to the dependency graph to prevent race conditions. Args: target_path: Root directory or file path to plan for available_processors: List of processor metadata (name, supported_extensions, priority) dependency_graph: Mapping of file types to their required upstream processors Returns: Dictionary mapping file paths to ordered lists of processor steps """ import os from pathlib import Path if not os.path.exists(target_path): raise FileNotFoundError(f"Target path does not exist: {target_path}") plan = {} files = [f for f in Path(target_path).rglob("*") if f.is_file()] for file_path in files: ext = file_path.suffix.lower() matching_processors = [ p for p in available_processors if ext in p.get("supported_extensions", []) ] if not matching_processors: continue # Score processors based on priority and dependency alignment scored = [] for proc in matching_processors: deps = dependency_graph.get(ext, []) dep_match = sum(1 for d in deps if any(d in p.get("supported_extensions", []) for p in matching_processors)) score = proc.get("priority", 0) + dep_match scored.append((score, proc)) scored.sort(reverse=True) plan[str(file_path)] = [proc["name"] for _, proc in scored] return plan ``` ### Pattern 2: Execution with Fallback ```python def execute_file_pipeline_with_fallback( file_path: str, processor_steps: List[Dict], output_dir: str, max_retries: int = 2 ) -> Dict: """Execute a sequence of file processing steps with resilience and fallback routing. Implements chunked reading for large files, handles I/O errors gracefully, and falls back to alternative processors or manual review queues on failure. Args: file_path: Path to the file being processed processor_steps: Ordered list of processor configurations to apply output_dir: Directory to write processed results max_retries: Maximum retry attempts per processor step Returns: Execution summary with success status, bytes processed, and fallback triggers """ import os from datetime import datetime os.makedirs(output_dir, exist_ok=True) result = { "file": file_path, "status": "pending", "steps_completed": 0, "fallbacks_used": [], "timestamp": datetime.now().isoformat() } current_data = None for step_idx, step in enumerate(processor_steps): processor_name = step["name"] chunk_size = step.get("chunk_size", 8192) for attempt in range(max_retries + 1): try: if current_data is None: with open(file_path, "rb") as f: current_data = f.read() processed = step["handler"](current_data, chunk_size) current_data = processed result["steps_completed"] += 1 break except IOError as e: if attempt == max_retries: result["fallbacks_used"].append(f"{processor_name}:io_error_retry_exhausted") # Fallback: switch to streaming processor or queue for review if step.get("fallback_handler"): current_data = step["fallback_handler"](file_path) result["steps_completed"] += 1 break else: result["status"] = "failed" raise except ValueError as e: # Data corruption or invalid format - fail fast result["status"] = "invalid_format" raise ValueError(f"Step {processor_name} failed validation: {e}") from e if result["status"] != "failed": with open(os.path.join(output_dir, os.path.basename(file_path)), "wb") as f: f.write(current_data) result["status"] = "completed" return result ``` ### 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 | |---|---| | `plan-writing` | Creates structured plan files for complex tasks, complementing this workflow framework | | `task-decomposition-engine` | Breaks down complex plans into actionable sub-tasks stored as file artifacts | --- ## 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. - [GitHub Flow (GitHub Documentation)](https://docs.github.com/en/get-started/using-github/github-flow) — Lightweight, branch-based workflow for managing work artifacts in version control - [Branching Models: GitFlow vs GitHub Flow](https://nvie.com/posts/a-successful-git-branching-model/) — Comparison of common branching strategies for organizing development plans - [Markdown for Planning and Documentation](https://www.markdownguide.org/cheat-sheet/) — Markdown specification for structured plan file creation - [Structured Planning in AI-Assisted Development](https://www.jetbrains.com/help/ai-hub/create-plans.html) — Patterns for using AI-assisted planning with version-controlled files - [Project Management as Code (Pacaw)](https://github.com/pacaw-project/pacaw) — Framework for treating project plans as executable, versioned artifacts