依据WCAG 2.2设计和检查Web、iOS及Android界面,核查读屏标签、键盘操作、配色对比度和操作目标尺寸,使界面支持辅助技术。
- 为纯图标按钮添加说明
- 检查键盘操作与模态框焦点
- 检查对比度与操作目标尺寸
在Claude Code结束工作时检查学习记录的更新时间、磁盘可用空间和会话末尾的特定表述,按配置发出警告或阻止完成,并提示可能省略步骤的迹象。
Stop hook that blocks Claude from finishing until quality checks pass. Detects rationalization patterns (surface text heuristics), stale learning logs (filesystem mtime), and low disk space. Complements self-audit by mechanically enforcing learning capture habits. Use when Claude should be mechanically blocked from declaring work finished before quality checks and learning capture actually pass.
在Claude Code准备结束工作时,检查学习记录的更新时间、磁盘可用空间及会话末尾的特定表述。注册为结束时运行的Stop hook后,会根据配置条件发出警告或阻止结束。判定使用文件时间和正则表达式,不进行AI推理。
适合在编辑多个文件后,保持记录经验与决策的习惯。也可检查磁盘空间不足,以及“暂时不运行测试”等省略步骤的迹象。可与检查内容质量的self-audit配合使用。
需要Python 3.8或更高版本,支持Windows、macOS和Linux,仅依赖标准库。需要放置quality-gate.py,并在~/.claude/settings.json中注册。它检查记录是否更新,不判断内容是否正确。表述检测可能误报,因此只发出警告;它不会执行构建或测试。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。
总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月19日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
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:
This is the same pattern as CI pipeline gates — automated, deterministic checks that verify machine-readable facts rather than trusting self-reported status.
| 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).
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.
From the ECC repository root:
mkdir -p ~/.claude/scripts
cp skills/delivery-gate/hooks/quality-gate.py ~/.claude/scripts/
Add to ~/.claude/settings.json:
{
"hooks": {
"Stop": [{
"hooks": [{
"type": "command",
"command": "python3 ~/.claude/scripts/quality-gate.py",
"timeout": 5000
}]
}]
}
}
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.
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 |
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)."
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.
from __future__ import annotations)This code went through 4 rounds of automated code review (CodeRabbit + Greptile) with 9 real bugs found and fixed.
self-audit — Reasoning quality gate (completeness/consistency/groundedness/honesty)verification-loop — Code quality checks (build/type/lint/test)gateguard — PreToolUse safety gate还没有评价。欢迎分享你的使用感受。
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还没有评论。来写第一条吧。
依据WCAG 2.2设计和检查Web、iOS及Android界面,核查读屏标签、键盘操作、配色对比度和操作目标尺寸,使界面支持辅助技术。
从指令、记忆、工具执行和界面显示等十二个层面排查人工智能代理故障,依据代码和日志梳理原因,按严重程度列出问题并提出修复方案。
用实际开发任务比较多个编程智能体,记录成功率、可获取的API费用、耗时和重复运行的稳定性,为工具选型及更新后的性能评估提供数据。
设计和调整AI智能体使用的工具、输入与返回格式,明确出错后的恢复、重试和停止条件,管理上下文信息量,并以完成率、重试次数和成功任务成本评估改进。
根据错误信息和执行环境,分析AI代理反复失败或重复操作的原因。通过小范围、可撤销的恢复操作检验假设,并将结果与依据整理成可供交接的报告。
在AI代理反复失败或重复操作时,记录错误与环境前提、排查原因,采取影响有限的恢复操作,并汇总结果和证据,为有依据的重试与交接提供支持。