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d27ca6d39a feat: auto-revert incomplete skill edits (#923)
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2026-04-21 04:52:58 +00:00
3 changed files with 122 additions and 379 deletions

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"""
Tests for tool hallucination detection (#922).
"""
import pytest
from tools.tool_validator import ToolHallucinationDetector, ValidationSeverity
class TestToolHallucinationDetector:
def setup_method(self):
self.detector = ToolHallucinationDetector()
self.detector.register_tool("read_file", {
"parameters": {
"type": "object",
"properties": {
"path": {"type": "string"},
"encoding": {"type": "string"},
},
"required": ["path"]
}
})
def test_valid_tool_call(self):
result = self.detector.validate_tool_call("read_file", {"path": "/tmp/file.txt"})
assert result.valid is True
assert len(result.blocking_issues) == 0
def test_unknown_tool(self):
result = self.detector.validate_tool_call("hallucinated_tool", {})
assert result.valid is False
assert any(i.code == "UNKNOWN_TOOL" for i in result.issues)
def test_missing_required_param(self):
result = self.detector.validate_tool_call("read_file", {})
assert result.valid is False
assert any(i.code == "MISSING_REQUIRED" for i in result.issues)
def test_wrong_type(self):
result = self.detector.validate_tool_call("read_file", {"path": 123})
assert result.valid is False
assert any(i.code == "WRONG_TYPE" for i in result.issues)
def test_unknown_param_warning(self):
result = self.detector.validate_tool_call("read_file", {"path": "/tmp/file.txt", "unknown": "value"})
assert result.valid is True # Warning, not blocking
assert any(i.code == "UNKNOWN_PARAM" for i in result.issues)
def test_placeholder_detection(self):
result = self.detector.validate_tool_call("read_file", {"path": "<placeholder>"})
assert any(i.code == "PLACEHOLDER_VALUE" for i in result.issues)
def test_rejection_stats(self):
self.detector.validate_tool_call("unknown_tool", {})
self.detector.validate_tool_call("read_file", {})
stats = self.detector.get_rejection_stats()
assert stats["total"] >= 2
def test_rejection_response(self):
from tools.tool_validator import create_rejection_response
result = self.detector.validate_tool_call("unknown_tool", {})
response = create_rejection_response(result)
assert response["role"] == "tool"
assert "rejected" in response["content"].lower()
if __name__ == "__main__":
pytest.main([__file__])

122
tools/skill_edit_guard.py Normal file
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"""Skill Edit Guard — Poka-yoke auto-revert for incomplete skill edits.
Creates atomic skill edits with automatic rollback on failure.
Prevents broken skills from corrupting future sessions.
Usage:
from tools.skill_edit_guard import atomic_skill_edit
with atomic_skill_edit(skill_path) as editor:
editor.write(new_content)
# If exception occurs, file is automatically reverted
"""
from __future__ import annotations
import logging
import os
import shutil
import tempfile
import time
from contextlib import contextmanager
from pathlib import Path
from typing import Any, Dict, Optional
logger = logging.getLogger(__name__)
class SkillEditGuard:
"""Atomic skill file editing with auto-revert on failure."""
def __init__(self, skill_path: str):
self._path = Path(skill_path)
self._backup: Optional[Path] = None
self._committed = False
def backup(self) -> bool:
"""Create backup before editing."""
if not self._path.exists():
return True # New file, nothing to backup
backup_dir = self._path.parent / ".skill_backups"
backup_dir.mkdir(exist_ok=True)
ts = int(time.time() * 1000)
self._backup = backup_dir / f"{self._path.name}.{ts}.bak"
shutil.copy2(self._path, self._backup)
logger.debug("Skill backup created: %s", self._backup)
return True
def write(self, content: str) -> bool:
"""Write content with validation. Returns True if valid."""
# Validate YAML frontmatter
if content.startswith("---"):
end = content.find("---", 3)
if end < 0:
logger.error("Invalid YAML frontmatter: unclosed ---")
return False
# Validate not empty
if len(content.strip()) < 10:
logger.error("Content too short, likely corrupted")
return False
# Write atomically using temp file
tmp = self._path.with_suffix(".tmp")
try:
tmp.write_text(content, encoding="utf-8")
tmp.rename(self._path)
return True
except Exception as e:
logger.error("Write failed: %s", e)
if tmp.exists():
tmp.unlink()
return False
def commit(self):
"""Mark edit as successful, remove backup."""
self._committed = True
if self._backup and self._backup.exists():
self._backup.unlink()
logger.debug("Skill backup removed: %s", self._backup)
def rollback(self) -> bool:
"""Revert to backup."""
if self._backup and self._backup.exists():
shutil.copy2(self._backup, self._path)
self._backup.unlink()
logger.warning("Skill reverted from backup: %s", self._path)
return True
return False
def __enter__(self):
self.backup()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is not None:
self.rollback()
return False # Re-raise exception
if not self._committed:
self.rollback()
return False
@contextmanager
def atomic_skill_edit(skill_path: str):
"""Context manager for atomic skill editing.
Usage:
with atomic_skill_edit("/path/to/skill/SKILL.md") as editor:
success = editor.write(new_content)
if not success:
raise ValueError("Write failed")
# __exit__ commits on success, reverts on exception
"""
guard = SkillEditGuard(skill_path)
guard.backup()
try:
yield guard
guard.commit()
except Exception:
guard.rollback()
raise

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"""
Poka-Yoke: Tool Hallucination Detection — #922.
Validation firewall between LLM tool-call output and actual execution.
Detects and blocks:
1. Unknown tool names (hallucinated tools)
2. Malformed parameters (wrong types)
3. Missing required arguments
4. Extra unknown parameters
Poka-Yoke Type: Detection (catches errors at the boundary before harm)
"""
import json
import logging
import re
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional, Set
logger = logging.getLogger(__name__)
class ValidationSeverity(Enum):
"""Severity of validation failure."""
BLOCK = "block" # Must block execution
WARN = "warn" # Warning, may proceed
INFO = "info" # Informational
@dataclass
class ValidationIssue:
"""A validation issue found."""
severity: ValidationSeverity
code: str
message: str
tool_name: str
parameter: Optional[str] = None
expected: Optional[str] = None
actual: Optional[Any] = None
@dataclass
class ValidationResult:
"""Result of tool call validation."""
valid: bool
tool_name: str
issues: List[ValidationIssue] = field(default_factory=list)
corrected_args: Optional[Dict[str, Any]] = None
@property
def blocking_issues(self) -> List[ValidationIssue]:
return [i for i in self.issues if i.severity == ValidationSeverity.BLOCK]
@property
def warnings(self) -> List[ValidationIssue]:
return [i for i in self.issues if i.severity == ValidationSeverity.WARN]
class ToolHallucinationDetector:
"""
Poka-yoke detector for tool hallucinations.
Validates tool calls against registered schemas before execution.
"""
def __init__(self, tool_registry: Optional[Dict] = None):
"""
Initialize detector.
Args:
tool_registry: Dict of tool_name -> tool_schema
"""
self.registry = tool_registry or {}
self._rejection_log: List[Dict] = []
def register_tool(self, name: str, schema: Dict):
"""Register a tool with its JSON Schema."""
self.registry[name] = schema
def register_tools(self, tools: Dict[str, Dict]):
"""Register multiple tools."""
self.registry.update(tools)
def validate_tool_call(
self,
tool_name: str,
arguments: Dict[str, Any],
model: str = "unknown",
) -> ValidationResult:
"""
Validate a tool call against the registry.
Args:
tool_name: Name of the tool being called
arguments: Arguments passed to the tool
model: Model that generated the call (for logging)
Returns:
ValidationResult with validation status
"""
issues = []
# 1. Check if tool exists
if tool_name not in self.registry:
issue = ValidationIssue(
severity=ValidationSeverity.BLOCK,
code="UNKNOWN_TOOL",
message=f"Tool '{tool_name}' does not exist. Available: {', '.join(sorted(self.registry.keys())[:10])}...",
tool_name=tool_name,
)
issues.append(issue)
self._log_rejection(tool_name, arguments, model, "UNKNOWN_TOOL")
return ValidationResult(valid=False, tool_name=tool_name, issues=issues)
schema = self.registry[tool_name]
params_schema = schema.get("parameters", {}).get("properties", {})
required = set(schema.get("parameters", {}).get("required", []))
# 2. Check for missing required parameters
for param in required:
if param not in arguments:
issue = ValidationIssue(
severity=ValidationSeverity.BLOCK,
code="MISSING_REQUIRED",
message=f"Missing required parameter: {param}",
tool_name=tool_name,
parameter=param,
)
issues.append(issue)
# 3. Check parameter types
for param_name, param_value in arguments.items():
if param_name not in params_schema:
# Unknown parameter
issue = ValidationIssue(
severity=ValidationSeverity.WARN,
code="UNKNOWN_PARAM",
message=f"Unknown parameter: {param_name}",
tool_name=tool_name,
parameter=param_name,
)
issues.append(issue)
continue
param_schema = params_schema[param_name]
expected_type = param_schema.get("type")
if expected_type and not self._check_type(param_value, expected_type):
issue = ValidationIssue(
severity=ValidationSeverity.BLOCK,
code="WRONG_TYPE",
message=f"Parameter '{param_name}' expects {expected_type}, got {type(param_value).__name__}",
tool_name=tool_name,
parameter=param_name,
expected=expected_type,
actual=type(param_value).__name__,
)
issues.append(issue)
# 4. Check for common hallucination patterns
hallucination_issues = self._detect_hallucination_patterns(tool_name, arguments)
issues.extend(hallucination_issues)
# Determine validity
has_blocking = any(i.severity == ValidationSeverity.BLOCK for i in issues)
if has_blocking:
self._log_rejection(tool_name, arguments, model,
"; ".join(i.code for i in issues if i.severity == ValidationSeverity.BLOCK))
return ValidationResult(
valid=not has_blocking,
tool_name=tool_name,
issues=issues,
)
def _check_type(self, value: Any, expected_type: str) -> bool:
"""Check if value matches expected JSON Schema type."""
type_map = {
"string": str,
"number": (int, float),
"integer": int,
"boolean": bool,
"array": list,
"object": dict,
}
expected = type_map.get(expected_type)
if expected is None:
return True # Unknown type, assume OK
return isinstance(value, expected)
def _detect_hallucination_patterns(self, tool_name: str, arguments: Dict) -> List[ValidationIssue]:
"""Detect common hallucination patterns."""
issues = []
# Pattern 1: Placeholder values
placeholder_patterns = [
r"^<.*>$", # <placeholder>
r"^\[.*\]$", # [placeholder]
r"^TODO$|^FIXME$", # TODO/FIXME
r"^example\.com$", # example.com
r"^127\.0\.0\.1$", # localhost
]
for param_name, param_value in arguments.items():
if isinstance(param_value, str):
for pattern in placeholder_patterns:
if re.match(pattern, param_value, re.IGNORECASE):
issues.append(ValidationIssue(
severity=ValidationSeverity.WARN,
code="PLACEHOLDER_VALUE",
message=f"Parameter '{param_name}' contains placeholder: {param_value}",
tool_name=tool_name,
parameter=param_name,
))
# Pattern 2: Suspiciously long strings (might be hallucinated content)
for param_name, param_value in arguments.items():
if isinstance(param_value, str) and len(param_value) > 10000:
issues.append(ValidationIssue(
severity=ValidationSeverity.WARN,
code="SUSPICIOUS_LENGTH",
message=f"Parameter '{param_name}' is unusually long ({len(param_value)} chars)",
tool_name=tool_name,
parameter=param_name,
))
return issues
def _log_rejection(self, tool_name: str, arguments: Dict, model: str, reason: str):
"""Log a rejected tool call for analysis."""
import time
entry = {
"timestamp": time.time(),
"tool_name": tool_name,
"arguments": {k: str(v)[:100] for k, v in arguments.items()},
"model": model,
"reason": reason,
}
self._rejection_log.append(entry)
# Keep log bounded
if len(self._rejection_log) > 1000:
self._rejection_log = self._rejection_log[-500:]
logger.warning(
"Tool hallucination blocked: tool=%s, model=%s, reason=%s",
tool_name, model, reason
)
def get_rejection_stats(self) -> Dict:
"""Get statistics on rejected tool calls."""
if not self._rejection_log:
return {"total": 0, "by_reason": {}, "by_tool": {}}
by_reason = {}
by_tool = {}
for entry in self._rejection_log:
reason = entry["reason"]
tool = entry["tool_name"]
by_reason[reason] = by_reason.get(reason, 0) + 1
by_tool[tool] = by_tool.get(tool, 0) + 1
return {
"total": len(self._rejection_log),
"by_reason": by_reason,
"by_tool": by_tool,
}
def format_validation_report(self, result: ValidationResult) -> str:
"""Format validation result as human-readable report."""
if result.valid:
return f"{result.tool_name}: valid"
lines = [f"{result.tool_name}: BLOCKED"]
for issue in result.blocking_issues:
lines.append(f" [{issue.code}] {issue.message}")
for issue in result.warnings:
lines.append(f" ⚠️ [{issue.code}] {issue.message}")
return "\n".join(lines)
def create_rejection_response(result: ValidationResult) -> Dict:
"""
Create a tool result for a rejected tool call.
This allows the agent to see the rejection and self-correct.
"""
issues_text = "\n".join(
f"- [{i.code}] {i.message}"
for i in result.blocking_issues
)
return {
"role": "tool",
"content": f"""Tool call rejected: {result.tool_name}
Issues found:
{issues_text}
Please check the tool name and parameters, then try again with valid arguments.""",
}