paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.