본문으로 건너뛰기
cccskills
무료GitHub에 공개

delivery-gate

Check learning-log timestamps, free disk space, and shortcut-related phrases before Claude Code ends a session. A Stop hook, which runs when the agent tries to finish, warns or blocks completion according to configured rules without using AI judgment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

설치 방법 보기

이럴 때 유용합니다

  • Checking learning records after complex editing tasks
  • Blocking completion when disk space is critically low
  • Flagging phrases that suggest skipped checks
  • Combining mechanical checks with self-audit

포함된 파일(2)

  • SKILL.md5.1 KB
  • hooks/quality-gate.py7.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Delivery Gate — Mechanical Quality Gate for Claude Code

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.

What It Checks

CheckMechanismOn Hit
Rationalization patternsRegex on transcript tailWarning only (never blocks)
Stale learning librariesmtime on 5 configurable pathsWarning if some stale; Block if >=3 stale OR growth-log stale + complex task
Disk space < 50GBshutil.disk_usageWarning
Disk space < 15GBshutil.disk_usageBlock (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).

Why

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.

Install

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
      }]
    }]
  }
}

Learning Libraries

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.

Configuration

Edit quality-gate.py:

VariableDefaultPurpose
RATIONALIZE4 patternsRegex patterns for rationalization detection
LIBS5 librariesFiles/dirs to check for today's updates
COMPLEX_THRESHOLD3Edit/Write calls to classify as complex
DISK_WARN_GB50Warn below this
DISK_CRIT_GB15Block below this

Examples

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)."

Limitations

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.

Compatibility

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

Quality

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

See Also

  • self-audit — Reasoning quality gate (completeness/consistency/groundedness/honesty)
  • verification-loop — Code quality checks (build/type/lint/test)
  • gateguard — PreToolUse safety gate

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Adding descriptive labels to icon-only buttons
  • Reviewing keyboard navigation and modal focus
  • Checking contrast and control sizes
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Investigating failures that appear only inside an agent application
  • Tracing old conversation content leaking into new sessions
  • Checking skipped or falsely claimed tool execution
affaan-m/ECC28.3만2026년 10월 16일 업데이트

agent-eval

무료요약 있음AI 평가 80점

실제 개발 과제로 여러 코딩 에이전트를 비교합니다. 성공률, 확인 가능한 API 비용, 소요 시간과 반복 실행의 안정성을 측정해 도입 결정과 업데이트 후 평가에 활용합니다.

  • 실제 개발 과제로 에이전트를 비교합니다
  • 새 도구나 모델을 도입하기 전에 평가합니다
  • 에이전트 업데이트 후 성능을 확인합니다
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Define an AI agent’s tool set and input formats
  • Make tool results and next steps easier to interpret
  • Specify safe retries and stopping conditions
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Diagnosing repeated failures in an agent run
  • Investigating loops through the same tool calls
  • Reviewing output degradation as context grows
affaan-m/ECC28.3만2026년 10월 5일 업데이트

Diagnose failed AI agent runs by capturing errors, checking likely causes, and choosing small, reversible recovery actions. The workflow produces a structured report of the failure, recovery steps, and evidence, helping explain repeated retries or stalled progress.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Diagnosing repeated tool calls with no progress
  • Investigating quality loss as context grows
  • Checking file paths and environment mismatches
affaan-m/ECC28.3만2026년 10월 16일 업데이트

affaan-m의 스킬 모두 보기

이 스킬의 문제 신고