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

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

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

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) — Lightweight, branch-based workflow for managing work artifacts in version control
• Branching Models: GitFlow vs GitHub Flow — Comparison of common branching strategies for organizing development plans
• Markdown for Planning and Documentation — Markdown specification for structured plan file creation
• Structured Planning in AI-Assisted Development — Patterns for using AI-assisted planning with version-controlled files
• Project Management as Code (Pacaw) — Framework for treating project plans as executable, versioned artifacts