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