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https://github.com/magnus919/agent-skills.git
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usernameandfactory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
476d7e11b0
Add ruff linter/formatter with pre-commit hooks, pytest-cov with 60% coverage threshold, CODEOWNERS, Dependabot for pip/GHA updates, and .env.example. Auto-fix existing ruff violations across eval_runner/ and scripts/. 10 agent-readiness criteria resolved: lint_config, formatter, pre_commit_hooks, naming_consistency, dead_code_detection, test_coverage_thresholds, test_performance_tracking, codeowners, dependency_update_automation, env_template. Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
"""Deterministic fake adapter for unit and CI testing without model credentials."""
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from __future__ import annotations
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import time
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from .models import AdapterInput, AdapterOutput, ExitStatus, ToolEvent
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class FakeAdapter:
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"""Returns canned responses derived from the case prompt.
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Behavior is fully deterministic: the response echoes the case ID, tool
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events are synthesized from the assertion count, and timing is fixed.
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Useful for validating the runner pipeline, manifest serialization, and
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grader bindings without any external dependencies.
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"""
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@property
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def name(self) -> str:
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return "fake"
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@property
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def version(self) -> str:
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return "0.1.0"
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def execute(self, input: AdapterInput) -> AdapterOutput:
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start = time.monotonic()
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case = input.case
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response = f"[fake] Processed case '{case.id}': {case.prompt[:80]}"
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activation_evidence = f"skill loaded from {input.skill_path.name}/SKILL.md"
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tool_events = [
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ToolEvent(
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name="read_file",
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arguments={"path": f"{input.skill_path.name}/SKILL.md"},
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result_summary="skill content loaded",
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timestamp="2025-01-01T00:00:00Z",
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),
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]
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for i, assertion in enumerate(case.assertions):
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tool_events.append(
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ToolEvent(
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name="assert_check",
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arguments={"index": i, "assertion": assertion[:60]},
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result_summary="pass",
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timestamp="2025-01-01T00:00:00Z",
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)
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)
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elapsed_ms = (time.monotonic() - start) * 1000
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return AdapterOutput(
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exit_status=ExitStatus.COMPLETED,
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response=response,
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activation_evidence=activation_evidence,
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artifacts=[],
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environment_state={"work_dir": str(input.work_dir)},
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tool_events=tool_events,
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duration_ms=elapsed_ms,
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token_usage={"input_tokens": 100, "output_tokens": 50},
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raw_trace_path=None,
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error=None,
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)
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