---

name: filesystem-context compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent filesystem context 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: filesystem-context, filesystem context, how do i filesystem-context, orchestrate filesystem-context, automate filesystem-context, agent filesystem-context 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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Filesystem Context

Orchestrates intelligent skill selection and execution for filesystem context 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 build_filesystem_context(
    target_paths: List[str],
    config: Dict[str, Any],
    min_relevance: float = 0.6
) -> Optional[ContextGraph]:
    """Construct an intelligent filesystem context graph from target paths.
    
    Applies multi-factor scoring to determine which files/directories 
    contribute meaningfully to the current task context. Filters out 
    noise (temp files, large binaries, inaccessible paths) and builds 
    a directed graph of relevant file relationships.
    
    Args:
        target_paths: List of absolute or relative paths to analyze
        config: Scoring configuration (max_depth, file_types, size_limits)
        min_relevance: Minimum relevance score to include in context
        
    Returns:
        ContextGraph with scored nodes and edges, or None if empty
    """
    if not target_paths:
        raise ValueError("Target paths list cannot be empty")
        
    context_nodes = []
    visited = set()
    
    for path in target_paths:
        if not os.path.exists(path):
            continue
            
        stat = os.stat(path)
        relevance = _calculate_path_relevance(path, stat, config)
        
        if relevance >= min_relevance and path not in visited:
            visited.add(path)
            node = {
                "path": path,
                "type": "dir" if os.path.isdir(path) else "file",
                "size": stat.st_size,
                "modified": stat.st_mtime,
                "relevance_score": relevance,
                "permissions": oct(stat.st_mode)[-3:]
            }
            context_nodes.append(node)
            
            # Recursively scan directories if configured
            if os.path.isdir(path) and config.get("recursive", False):
                context_nodes.extend(_scan_directory(path, config, visited))
                
    if not context_nodes:
        return None
        
    # Build relationship edges based on imports/includes
    edges = _infer_file_relationships(context_nodes)
    return ContextGraph(nodes=context_nodes, edges=edges)

Pattern 2: Execution with Fallback

def execute_context_workflow(
    context_graph: ContextGraph,
    operation: str,
    fallback_handler: Optional[Callable] = None
) -> Dict[str, Any]:
    """Execute a filesystem context operation with I/O-aware fallback chains.
    
    Handles common filesystem failures (permission denied, disk full, 
    stale symlinks) by applying domain-specific recovery strategies:
    1. Retry with elevated permissions (if authorized)
    2. Skip inaccessible nodes and continue processing
    3. Fallback to cached context if available
    4. Log detailed I/O errors for human review
    
    Args:
        context_graph: Pre-built context graph to process
        operation: Target operation (index, scan, validate, sync)
        fallback_handler: Optional callback for critical failures
        
    Returns:
        Execution result with success status, processed nodes, and error log
    """
    if not context_graph or not operation:
        raise ValueError("Context graph and operation must be provided")
        
    results = {"processed": [], "skipped": [], "errors": [], "success": False}
    max_retries = 2
    
    for node in context_graph.nodes:
        attempt = 0
        while attempt <= max_retries:
            try:
                if operation == "index":
                    metadata = _index_file_metadata(node["path"])
                elif operation == "validate":
                    metadata = _validate_file_integrity(node["path"])
                else:
                    raise ValueError(f"Unsupported operation: {operation}")
                    
                results["processed"].append({
                    "path": node["path"],
                    "status": "success",
                    "metadata": metadata
                })
                break
                
            except PermissionError:
                attempt += 1
                if attempt > max_retries:
                    results["skipped"].append(node["path"])
                    results["errors"].append(f"Permission denied: {node['path']}")
                time.sleep(0.1 * attempt)
                
            except FileNotFoundError:
                results["skipped"].append(node["path"])
                results["errors"].append(f"Stale path: {node['path']}")
                break
                
            except OSError as e:
                attempt += 1
                if attempt > max_retries:
                    results["errors"].append(f"OS error on {node['path']}: {e}")
                    if fallback_handler:
                        fallback_handler(node, e)
                time.sleep(0.1 * attempt)
                
    results["success"] = len(results["processed"]) > 0
    return results

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.

• POSIX Filesystem API Reference
• Python os Module Documentation
• inotify Linux Kernel File Monitoring
• fswatch Cross-Platform File Monitor
• Tree-sitter Codebase Parsing