c9f69d9d6d
范围（不含 design-story-foundation、docs/、humanization/README.md 等进行中改动）:
1. 新增 harness/ 控制平面
- skill_harness.py 静态审计：32 个运行时 Skill 的 frontmatter/manifest/文档污染，当前 0 问题
- run_selected.py 选择性执行器：manifest 与磁盘一一对账、依赖阻断、
空跑与 skip-only 失败关闭、AST 测试形状门
- manifests/skills.json：32 个 Skill 的合同责任方与协作领域登记
- manifests/test-inventory.json：81 个测试资产登记
- specs/skill-testing.md 与 README.md：测试分层、证据边界与 harness 职责
2. 实现测试从 .claude/skills/*/scripts/ 迁至 tests/skills/<skill>/
- 71 个测试文件迁移并修复项目根与临时目录运行导入
- 数据库触发器测试宽泛异常收窄为 psycopg.errors.RaiseException
- 抽取离线大测试拆出真实 PG smoke（默认阻断，不计入离线通过）
- 抽取 presence 去重边界拆出独立测试：493 + 78 = 571 项检查不变
3. 运行时文档清理
- 13 个 SKILL.md 移除自测/离线验证段落、测试命令与测试文件事实源表述，
只保留运行时合同；业务运行合同、额度、授权与离线模式均保留
4. SoT 同步
- AGENTS.md：新增 Skill 领域索引（7 个合同责任方分组，覆盖 32 个运行时 Skill）
- 领域 07：测试入口改由 harness/manifests/ 登记，SKILL.md 不承载测试命令
- humanization 覆盖矩阵：活动测试路径同步迁移
验证证据: harness 自测 15 项 + runner 自测 13 项通过；静态审计 32 Skill / 0 问题；
73 个非数据库测试通过；8 个集成条目中 6 个 PostgreSQL 项被依赖门明确阻断；
py_compile 与 git diff --check 通过。未连接 PostgreSQL、网络、真实模型或额度。
已知边界: 真正 skill_behavior_eval 仍为 0，尚未验证任何 Skill 自然语言行为；
evaluate-frozen-replay 的 raw 存储边界冲突留待单独治理。
180 lines
9.4 KiB
Python
180 lines
9.4 KiB
Python
#!/usr/bin/env python3
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"""clean_detect 失败关闭路径的纯离线回归测试。
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红线：不连接数据库，不调用网络或真实 LLM。
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chat_governed 与 extract_json 全部 mock/patch，只验证 clean_detect 在三种情形下的行为合同：
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1. 治理链耗尽 → (None,None,None) → 写空占位+skipped，绝不返回假成功
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2. JSON 解析失败 → 失败关闭，该窗不清洗、不拿坏数据当成功
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3. 正常路径 → 正确解析 deletions（逐字原文+理由），不改写正文、不做删除
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"""
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import json
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import pathlib
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import sys
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import tempfile
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import unittest
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from unittest.mock import patch
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from click.testing import CliRunner
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PROJECT_ROOT = pathlib.Path(__file__).resolve().parents[3]
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SCRIPT_DIR = PROJECT_ROOT / ".claude" / "skills" / "clean-book-text" / "scripts"
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sys.path.insert(0, str(SCRIPT_DIR))
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import clean_detect # noqa: E402
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class CleanDetectOfflineTest(unittest.TestCase):
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"""覆盖 clean_detect 三条失败关闭/正常路径。"""
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def setUp(self):
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# 每个测试用独立 tmp 目录，patch OUT 使产物落在 tmp 而非真实 /tmp/muse-clean
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self._tmp = tempfile.TemporaryDirectory()
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self.tmp_path = pathlib.Path(self._tmp.name)
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self.work_id = 99999 # 用不存在的 work_id 避免碰撞真实产物
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self.work_dir = self.tmp_path / str(self.work_id)
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self.work_dir.mkdir(parents=True)
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# 写 manifest 和窗文本文件（模拟 clean_prep 的产出）
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self.window_file = self.work_dir / "window-001.txt"
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self.window_file.write_text(
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"【第1章 | 开始】\n这是正文。\n一秒记住♂粒÷小÷说→网\n", encoding="utf-8")
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manifest = {
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"title": "测试书",
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"windows": [
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{"win": 1, "from": 1, "to": 1, "file": str(self.window_file)},
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],
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}
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(self.work_dir / "manifest.json").write_text(
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json.dumps(manifest, ensure_ascii=False), encoding="utf-8")
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self.runner = CliRunner()
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self.patcher = patch.object(clean_detect, "OUT", self.tmp_path)
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self.patcher.start()
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def tearDown(self):
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self.patcher.stop()
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self._tmp.cleanup()
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def _invoke(self):
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return self.runner.invoke(clean_detect.main, ["--work-id", str(self.work_id)])
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# ── 情形1：治理链耗尽 ──────────────────────────────────────────────
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def test_governance_chain_exhausted_writes_empty_placeholder_and_skips(self):
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"""chat_governed 返回 (None,None,None) 时：写空占位+skipped，
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不产生有效 deletions，绝不返回假成功。"""
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with patch.object(clean_detect, "chat_governed", return_value=(None, None, None)):
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result = self._invoke()
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self.assertEqual(0, result.exit_code, f"命令应正常退出但输出：{result.output}")
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# 产物文件存在
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out_file = self.work_dir / "deletions-001.json"
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self.assertTrue(out_file.exists(), "应写出空占位产物")
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data = json.loads(out_file.read_text(encoding="utf-8"))
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# 核心断言：deletions 为空、有 skipped 标记
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self.assertEqual([], data["deletions"], "治理链耗尽时 deletions 必须为空")
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self.assertIn("skipped", data, "必须记 skipped 标记")
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self.assertIn("治理链", data["skipped"], "skipped 原因应说明治理链耗尽")
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# 不能出现 model 字段（那是成功路径才写的，避免被下游误当成功）
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self.assertNotIn("model", data, "失败路径不应写 model 字段")
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# 输出中应有跳过与不清洗提示
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self.assertIn("跳过", result.output)
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self.assertIn("不清洗", result.output)
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# ── 情形2：JSON 解析失败 ────────────────────────────────────────────
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def test_json_parse_failure_fails_closed(self):
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"""chat_governed 成功但 extract_json 抛 ValueError 时：
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失败关闭，不拿坏数据当成功。"""
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fake_content = "这不是JSON，是模型的胡言乱语"
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fake_usage = {"prompt_tokens": 100, "completion_tokens": 50}
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with patch.object(clean_detect, "chat_governed",
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return_value=(fake_content, fake_usage, "MiniMax-M3")), \
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patch.object(clean_detect, "extract_json",
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side_effect=ValueError("无法解析")):
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result = self._invoke()
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self.assertEqual(0, result.exit_code, f"命令应正常退出但输出：{result.output}")
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out_file = self.work_dir / "deletions-001.json"
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self.assertTrue(out_file.exists(), "应写出失败关闭产物")
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data = json.loads(out_file.read_text(encoding="utf-8"))
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self.assertEqual([], data["deletions"], "解析失败时 deletions 必须为空")
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self.assertIn("skipped", data, "必须记 skipped 标记")
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self.assertIn("JSON", data["skipped"], "skipped 原因应说明 JSON 解析失败")
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self.assertNotIn("model", data, "失败路径不应写 model 字段")
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self.assertIn("解析失败", result.output)
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self.assertIn("不清洗", result.output)
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# ── 情形3：正常路径 ─────────────────────────────────────────────────
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def test_valid_deletions_parsed_correctly_without_rewrite_or_delete(self):
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"""chat_governed 返回合法 deletions JSON 时：正确解析产出（逐字原文+理由），
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clean_detect 本身不改写正文、不做删除（删除是 clean_apply 的事）。"""
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deletions = [
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{"chapter_order": 1, "exact": "一秒记住♂粒÷小÷说→网", "reason": "书站广告变体"},
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]
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fake_content = json.dumps({"deletions": deletions}, ensure_ascii=False)
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fake_usage = {"prompt_tokens": 5000, "completion_tokens": 200}
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with patch.object(clean_detect, "chat_governed",
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return_value=(fake_content, fake_usage, "MiniMax-M3")):
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result = self._invoke()
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self.assertEqual(0, result.exit_code, f"命令应正常退出但输出：{result.output}")
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out_file = self.work_dir / "deletions-001.json"
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self.assertTrue(out_file.exists())
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data = json.loads(out_file.read_text(encoding="utf-8"))
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# 核心断言：deletions 正确解析，逐字原文与理由原样保留
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self.assertEqual(1, len(data["deletions"]))
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self.assertEqual("一秒记住♂粒÷小÷说→网", data["deletions"][0]["exact"])
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self.assertEqual("书站广告变体", data["deletions"][0]["reason"])
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self.assertEqual(1, data["deletions"][0]["chapter_order"])
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# 成功路径写 model 字段
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self.assertEqual("MiniMax-M3", data["model"])
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# 不应有 skipped 标记
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self.assertNotIn("skipped", data, "成功路径不应有 skipped 标记")
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# clean_detect 不改写正文：源文件内容不变（删除是 clean_apply 的职责）
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original_text = self.window_file.read_text(encoding="utf-8")
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self.assertIn("一秒记住♂粒÷小÷说→网", original_text,
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"源 txt 不应被改动（clean_detect 只探测不删除）")
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self.assertIn("这是正文。", original_text, "正文不应被改动")
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# 输出中应有检出提示
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self.assertIn("检出 1 段", result.output)
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# ── 情形3补充：空 deletions（该窗无垃圾）是成功路径而非 skipped ──────
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def test_empty_deletions_is_success_not_skip(self):
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"""模型返回空 deletions 表示该窗干净——是成功路径（写 model），不是 skipped。"""
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fake_content = '{"deletions": []}'
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fake_usage = {"prompt_tokens": 3000, "completion_tokens": 30}
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with patch.object(clean_detect, "chat_governed",
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return_value=(fake_content, fake_usage, "MiniMax-M3")):
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result = self._invoke()
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self.assertEqual(0, result.exit_code)
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out_file = self.work_dir / "deletions-001.json"
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data = json.loads(out_file.read_text(encoding="utf-8"))
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self.assertEqual([], data["deletions"])
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self.assertEqual("MiniMax-M3", data["model"], "空 deletions 仍是成功路径，应写 model")
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self.assertNotIn("skipped", data, "空 deletions 不是失败，不应有 skipped")
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self.assertIn("检出 0 段", result.output)
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# ── 断点续跑：已有产物时不调用 LLM ──────────────────────────────────
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def test_existing_output_skipped_without_force(self):
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"""已有产物文件时不调用 chat_governed（断点续跑幂等），--force 才重跑。"""
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out_file = self.work_dir / "deletions-001.json"
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out_file.write_text('{"deletions": [], "model": "old"}', encoding="utf-8")
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with patch.object(clean_detect, "chat_governed") as mock_gov:
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result = self._invoke()
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mock_gov.assert_not_called()
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self.assertEqual(0, result.exit_code)
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self.assertIn("已有产物", result.output)
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# 文件内容不变
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data = json.loads(out_file.read_text(encoding="utf-8"))
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self.assertEqual("old", data["model"])
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if __name__ == "__main__":
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unittest.main(verbosity=2)
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