delivery-gate

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Delivery Gate — Mechanical Quality Gate for Claude Code

Delivery Gate — Claude Code的机械质量检查门

A Stop hook that checks three things before Claude can finish a session, using only deterministic checks — file modification timestamps, disk usage, and regex patterns on the transcript text. No AI inference.

This is distinct from reasoning gates (like

self-audit

): delivery-gate checks machine-verifiable facts; self-audit checks output quality across four reasoning dimensions. Together they form defense in depth:

delivery-gate: "Was the learning library touched today? Is disk space safe?"
self-audit: "Is the file content correct, complete, and honest?"

This is the same pattern as CI pipeline gates — automated, deterministic checks that verify machine-readable facts rather than trusting self-reported status.

这是一种Stop钩子，在Claude结束会话前会检查三项内容，仅使用确定性检查——文件修改时间戳、磁盘使用情况以及对话文本的正则匹配模式，无需AI推理。

它与推理检查门（如

self-audit

）不同：delivery-gate检查机器可验证的事实；self-audit则从四个推理维度检查输出质量。二者共同构建深度防御体系：

delivery-gate：“今日是否更新了学习库？磁盘空间是否安全？”
self-audit：“文件内容是否正确、完整且真实？”

这与CI流水线检查门的模式一致——通过自动化、确定性检查来验证机器可读的事实，而非依赖自我报告的状态。

What It Checks

检查内容

Check	Mechanism	On Hit
Rationalization patterns	Regex on transcript tail	Warning only (never blocks)
Stale learning libraries	mtime on 5 configurable paths	Warning if some stale; Block if >=3 stale OR growth-log stale + complex task
Disk space < 50GB	`shutil.disk_usage`	Warning
Disk space < 15GB	`shutil.disk_usage`	Block (exit 2)

Rationalization detection warns about patterns like "skip tests for now" and "pre-existing bug" — surface signals that thinking may have been cut short. It never blocks on its own, because regex heuristics can false-positive. The blocking conditions are: disk critical,

>=3 learning libs stale

, OR

growth-log

specifically stale (all require complex task >=3 edits).

检查项	实现机制	触发结果
合理化模式	匹配对话末尾的正则表达式	仅警告（从不阻止）
过期学习库	检查5个可配置路径的修改时间	部分过期时警告；阻止条件：≥3个库过期或 growth-log过期且任务复杂
磁盘空间 < 50GB	`shutil.disk_usage`	警告
磁盘空间 < 15GB	`shutil.disk_usage`	阻止（退出码2）

合理化检测会针对诸如“暂时跳过测试”“已有bug”之类的模式发出警告——这些是思维可能被打断的表层信号。由于正则启发式可能出现误报，因此它不会单独触发阻止。阻止条件为：磁盘空间临界、

≥3个学习库过期

，或

growth-log

明确过期（均要求复杂任务≥3次编辑）。

Why

设计初衷

Claude Code's built-in checks cover code quality (build → type → lint → test). But there's a different failure mode: the agent produces working code while the session hygiene was neglected — learning not captured, rationalized shortcuts, disk running out silently.

Over many sessions of "ship and forget," the human hasn't grown. This hook enforces the habit: complex task → must touch learning libraries.

Claude Code的内置检查覆盖代码质量（构建→类型检查→代码规范→测试）。但存在另一种失败模式：Agent生成了可运行的代码，却忽略了会话规范——未记录学习内容、使用合理化借口走捷径、磁盘空间悄然耗尽。

长期“交付后即遗忘”会导致人类用户无法成长。该钩子强制养成习惯：复杂任务→必须更新学习库。

Install

安装

bash

cp quality-gate.py ~/.claude/scripts/

Add to

~/.claude/settings.json

json

{
  "hooks": {
    "Stop": [{
      "hooks": [{
        "type": "command",
        "command": "python3 ~/.claude/scripts/quality-gate.py",
        "timeout": 5000
      }]
    }]
  }
}

bash

cp quality-gate.py ~/.claude/scripts/

添加至

~/.claude/settings.json

：

json

{
  "hooks": {
    "Stop": [{
      "hooks": [{
        "type": "command",
        "command": "python3 ~/.claude/scripts/quality-gate.py",
        "timeout": 5000
      }]
    }]
  }
}

Learning Libraries

学习库

Create these files in your project's memory directory. The hook checks if at least one was updated today:

memory/
├── growth-log/          # Daily learning entries (directory)
├── decisions/log.md     # Decision log
├── output-index.md      # Index of session outputs
├── ratings-tracker.md   # Skill ratings over time
└── tooling_capabilities.md  # Known tools inventory

Customize the

LIBS

dict to match your own file structure.

在项目的内存目录中创建以下文件。钩子会检查是否至少有一个文件在今日被更新：

memory/
├── growth-log/          # 每日学习记录（目录）
├── decisions/log.md     # 决策日志
├── output-index.md      # 会话输出索引
├── ratings-tracker.md   # 技能评分跟踪
└── tooling_capabilities.md  # 已知工具清单

可自定义

LIBS

字典以匹配你的文件结构。

Configuration

配置

Edit

quality-gate.py

Variable	Default	Purpose
`RATIONALIZE`	4 patterns	Regex patterns for rationalization detection
`LIBS`	5 libraries	Files/dirs to check for today's updates
`COMPLEX_THRESHOLD`	3	Edit/Write calls to classify as complex
`DISK_WARN_GB`	50	Warn below this
`DISK_CRIT_GB`	15	Block below this

编辑

quality-gate.py

：

变量	默认值	用途
`RATIONALIZE`	4种模式	用于检测合理化行为的正则表达式模式
`LIBS`	5个学习库	需检查今日更新的文件/目录
`COMPLEX_THRESHOLD`	3	判定任务为复杂所需的编辑/写入次数
`DISK_WARN_GB`	50	低于该值时发出警告
`DISK_CRIT_GB`	15	低于该值时阻止会话结束

Examples

示例

Simple session — allowed:

edit_count=1 (< 3, not complex) → exit 0

Complex task, learning captured — allowed:

edit_count=5 (complex) → checks LIBS → growth-log updated today → exit 0

Complex task, no learning — BLOCKED:

edit_count=4 (complex) → checks LIBS → all 5 stale → exit 2
stderr: "Blocked: complex task completed but no learning captured today."

Low disk space — BLOCKED:

disk_free=12GB < 15GB critical → exit 2
stderr: "Blocked: disk space at 12GB (threshold: 15GB)."

简单会话——允许结束：

edit_count=1 (< 3，非复杂任务) → 退出码0

复杂任务且已记录学习内容——允许结束：

edit_count=5（复杂任务）→ 检查学习库 → growth-log今日已更新 → 退出码0

复杂任务但未记录学习内容——被阻止：

edit_count=4（复杂任务）→ 检查学习库 → 全部5个库均过期 → 退出码2
stderr: "Blocked: complex task completed but no learning captured today."

磁盘空间不足——被阻止：

disk_free=12GB < 15GB临界值 → 退出码2
stderr: "Blocked: disk space at 12GB (threshold: 15GB)."

Limitations

局限性

The hook enforces the habit of touching learning libraries, not the quality of what was recorded. If

output-index.md

is updated but

growth-log

is skipped, the hook passes (1 of 5 libraries touched). This is by design: mechanical gates check machine-verifiable facts. For content quality verification, pair with

self-audit

该钩子仅强制养成更新学习库的习惯，不检查记录内容的质量。例如若仅更新

output-index.md

而跳过

growth-log

，钩子仍会通过（5个库中有1个已更新）。这是设计使然：机械检查门仅验证机器可识别的事实。如需验证内容质量，请搭配

self-audit

使用。

Compatibility

兼容性

Python 3.8+ (uses
```
from __future__ import annotations
```
)
Cross-platform: Windows, macOS, Linux
Zero dependencies beyond stdlib

Python 3.8+（使用
```
from __future__ import annotations
```
）
跨平台：Windows、macOS、Linux
仅依赖标准库，无额外依赖

Quality

质量说明

This code went through 4 rounds of automated code review (CodeRabbit + Greptile) with 9 real bugs found and fixed.

此代码经过4轮自动化代码审查（CodeRabbit + Greptile），共发现并修复9个真实bug。