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a9df8b1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | from __future__ import annotations
import json
from types import SimpleNamespace
from skillos.layers.skill_runtime.reflection import ReflectionAgent, _REFLECT_PROMPT
from skillos.layers.skill_runtime.verifier import (
VerificationResult,
VerifierAgent,
_VERIFY_PROMPT,
)
from skillos.models.maintenance_model import (
MaintenanceRecommendedAction,
MaintenanceTrigger,
)
def test_runtime_verifier_and_reflection_prompts_are_ascii():
_VERIFY_PROMPT.encode("ascii")
_REFLECT_PROMPT.encode("ascii")
def test_verifier_normalizes_llm_output():
payload = {
"passed": True,
"score": 2.5,
"issues": "bad shape",
"suggestions": ["keep going", None],
"reasoning": "looks okay",
}
verifier = VerifierAgent(FakeLLM(json.dumps(payload)))
result = verifier.verify("finish task", {"ok": True})
assert result.passed is True
assert result.score == 1.0
assert result.issues == []
assert result.suggestions == ["keep going"]
assert result.details["reasoning"] == "looks okay"
def test_verifier_fallback_detects_failure_output():
verifier = VerifierAgent(FakeLLM("not json"))
result = verifier.verify(
"finish task",
{"success": False, "error": "step failed"},
"step failed with timeout",
)
assert result.passed is False
assert result.score == 0.2
assert result.issues
assert result.suggestions
def test_verifier_fallback_passes_non_empty_output_without_failure_evidence():
verifier = VerifierAgent(FakeLLM("not json"))
result = verifier.verify("finish task", {"result": "done"}, "all steps completed")
assert result.passed is True
assert result.score == 0.65
assert result.issues == []
def test_reflection_normalizes_d_compatible_proposals():
payload = {
"root_cause": "timeout",
"failed_skill_ids": ["skill_a", None],
"improvement_suggestions": ["repair prompt", ""],
"skill_update_proposals": [
{
"skill_id": "skill_a",
"issue": "timeout",
"proposed_fix": "add timeout handling",
"recommended_action": "unknown",
"evidence": ["step failed", None],
"targets_to_fix": ["timeout branch"],
"invariants_to_preserve": ["successful retry behavior"],
"validation_plan": ["replay failed task", ""],
},
"bad",
{"skill_id": "", "recommended_action": "repair"},
],
"experience_summary": "timeout during execution",
}
reflector = ReflectionAgent(FakeLLM(json.dumps(payload)))
verification = VerificationResult(
passed=False,
score=0.2,
goal="finish task",
issues=["timeout"],
)
feedback = reflector.reflect("task-1", "finish task", {}, verification)
assert feedback.success is False
assert feedback.failed_skill_ids == ["skill_a"]
assert feedback.improvement_suggestions == ["repair prompt"]
assert feedback.skill_update_proposals == [
{
"skill_id": "skill_a",
"issue": "timeout",
"proposed_fix": "add timeout handling",
"recommended_action": "review",
"evidence": ["step failed"],
"targets_to_fix": ["timeout branch"],
"invariants_to_preserve": ["successful retry behavior"],
"validation_plan": ["replay failed task"],
}
]
def test_reflection_fallback_generates_repair_proposal_for_failed_skill():
reflector = ReflectionAgent(FakeLLM("not json"))
verification = VerificationResult(
passed=False,
score=0.2,
goal="finish task",
issues=["Execution trace contains skipped steps."],
suggestions=["Repair the failed skill."],
)
trace = {
"steps": [
{
"skill_id": "skill_a",
"status": "failed",
"error": "Skill code raised RuntimeError",
}
]
}
feedback = reflector.reflect("task-1", "finish task", trace, verification)
assert feedback.success is False
assert feedback.failed_skill_ids == ["skill_a"]
assert feedback.skill_update_proposals[0]["skill_id"] == "skill_a"
assert feedback.skill_update_proposals[0]["recommended_action"] == "repair"
assert feedback.skill_update_proposals[0]["evidence"]
proposals = feedback.to_maintenance_proposals()
assert len(proposals) == 1
assert proposals[0].skill_id == "skill_a"
assert proposals[0].trigger == MaintenanceTrigger.RUNTIME_FAILURE
assert proposals[0].recommended_action == MaintenanceRecommendedAction.REPAIR
assert proposals[0].root_cause == "Execution trace contains skipped steps."
assert proposals[0].feedback_sources == ["runtime_reflection"]
assert proposals[0].targets_to_fix == ["Execution trace contains skipped steps."]
assert proposals[0].invariants_to_preserve
assert proposals[0].validation_plan
assert proposals[0].requires_human_review is True
def test_reflection_fallback_uses_trace_skill_ids_for_verifier_failure():
reflector = ReflectionAgent(FakeLLM("not json"))
verification = VerificationResult(
passed=False,
score=0.0,
goal="submit form",
issues=["Path not found: output.final_state.submitted"],
suggestions=["Repair the postcondition mapping."],
)
trace = {
"steps": [
{
"skill_id": "skill_submit_form",
"status": "success",
"outputs": {"success": True},
}
]
}
feedback = reflector.reflect("task-verify", "submit form", trace, verification)
assert feedback.success is False
assert feedback.failed_skill_ids == ["skill_submit_form"]
assert feedback.skill_update_proposals == [
{
"skill_id": "skill_submit_form",
"issue": "Path not found: output.final_state.submitted",
"proposed_fix": (
"Review runtime failure and repair the skill implementation or prompt."
),
"recommended_action": "repair",
"evidence": ["Path not found: output.final_state.submitted"],
}
]
def test_reflection_fallback_success_does_not_generate_repair_proposal():
reflector = ReflectionAgent(FakeLLM("not json"))
verification = VerificationResult(
passed=True,
score=0.8,
goal="finish task",
)
feedback = reflector.reflect("task-1", "finish task", {"result": "done"}, verification)
assert feedback.success is True
assert feedback.skill_update_proposals == []
assert feedback.experience_summary == "Task completed successfully."
class FakeLLM:
def __init__(self, content: str) -> None:
self.content = content
def chat(self, messages: object) -> SimpleNamespace:
return SimpleNamespace(content=self.content)
|