delivery-gate
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.
- License 许可
- MIT
- 最近更新
- 2026-11-11
- Stars
- ★ 3
- Forks
- 0
01 前言 Front Matter
- name
- delivery-gate
- metadata
- { "origin": "ECC", "version": "1.1.1" }
- description
- 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.
02 资源文件 1
- 脚本hooks/quality-gate.py7.9 KB
03 SKILL.md 正文
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
| 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 on a complex task if >=3 stale OR growth-log stale |
| Disk space < 50GB | shutil.disk_usage |
Reminder |
| Disk space < 30GB | shutil.disk_usage |
Warning |
| Disk space < 15GB | shutil.disk_usage |
Block (exit 2), on every Stop |
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 (any session), or, on a complex task (>=3 Edit/Write calls), >=3 learning libs stale OR growth-log specifically stale. If the project memory directory does not exist, the learning-library checks are skipped: the hook only warns that it cannot verify learning capture, and a complex task is not blocked (the disk checks still apply).
The hook blocks a given Stop at most once. When Claude Code re-runs it with stop_hook_active: true (Claude already continued once because of this hook), it exits 0, so a condition the agent cannot fix (such as low disk space) cannot trap the session in an endless loop.
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": 5
}]
}]
}
}
Learning Libraries
Create these files in your project's memory directory (~/.claude/projects/<encoded-project-path>/memory/, where every non-alphanumeric character of the project path becomes -). The hook checks which of them were 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:
| 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_REMIND_GB |
50 | Remind below this |
DISK_WARN_GB |
30 | Warn below this |
DISK_CRIT_GB |
15 | Block 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 + 2 others updated today (2 stale) → exit 0
Complex task, no learning — BLOCKED:
edit_count=4 (complex) → checks LIBS → all 5 stale → exit 2
stderr: "Blocked: complex task but >=3 learning libs stale."
Low disk space — BLOCKED:
disk_free=12GB < 15GB critical → exit 2
stderr: "Blocked: disk space at 12GB (<15GB). Free space before continuing."
Limitations
The hook enforces the habit of touching learning libraries, not the quality of what was recorded. On a complex task it passes only when growth-log was updated today and at most 2 libraries are stale; updating output-index.md alone still blocks. Without a project memory directory there is nothing to check, so it only warns. 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
04 同仓库更多 Skills
higgsfield-websites
Build, edit, and deploy full-stack websites, apps and games via the Higgsfield CLI (`higgsfield website …`). Each is a React 19 + TanStack Start SSR app in one Cloudflare Worker (D1/R2/KV/DO/Containers). THREE product types, picked via `--type` on create: `website` (standalone, no Higgsfield integration — references/website-flow.md), `app` (Sign in with Higgsfield + fnf SDK, Quanta — references/app-flow.md), `game` (realtime multiplayer rooms — references/game-flow.md). Routes to the right flow; each carries its own rules and deploy/publish gates. Use when: "build me a website", "make a landing page", "create a web app", "build a SaaS dashboard / portfolio", "make me a game", "deploy this site", "publish". Also owns GAME ART: "make a spritesheet", "tileable texture", "animate a 3D character", game music/SFX — see the game-* references. NOT for: single image/video/audio generation (higgsfield-generate), product photos (higgsfield-product-photoshoot), marketplace cards (higgsfield-marketplace-cards).
writing-skills
Use when creating new skills, editing existing skills, or verifying skills work before deployment
writing-plans
Use when you have a spec or requirements for a multi-step task, before touching code
verification-before-completion
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
using-superpowers
Use when starting any conversation - establishes how to find and use skills, requiring skill invocation before ANY response including clarifying questions
using-git-worktrees
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback