SkillAtlasSkill 详情

python-quality-gate

Essays and writing behind this toolkit live at vexjoy.com.

审核状态:已审核Quality 80Security 50

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项目 README

来源文件:README.md

抓取于 2026年8月31日

VexJoy Agent

VexJoy Agent

Essays and writing behind this toolkit live at vexjoy.com.

AI agents skip steps.

"Looks correct" replaces running tests. "Trivial change" replaces verification. The agent confidently ships broken code because nothing structurally prevented it from skipping the work.

Harnesses have a second problem: given only a skill list, they do not route eagerly enough, or correctly enough. Good skills sit unused. So this toolkit connects the skills, agents, and workflows we want directly into the harness, automatically. You don't have to understand what is here. Say what you want in plain English and you get all the value we have put into it: the right specialist with the right methodology, behind gates that demand exit codes, not assertions.

44 domain agents, 122 workflow skills, 78 hooks, 136 scripts. Agents carry knowledge, skills enforce methodology, hooks block incomplete work, scripts handle determinism.

Works across Claude Code (/do), Codex ($do), Factory (/do), Reasonix (/do).

What It Looks Like

$ claude

> /do debug this Go test

  Routing: go-engineer + systematic-debugging
  Phase 1/4: Reproduce: running test, capturing failure...
  Phase 2/4: Hypothesize: 3 candidates from stack trace...
  Phase 3/4: Verify: isolated root cause in connection pool timeout
  Phase 4/4: Fix: patch applied, test passing, PR opened

  ✓ Delivered: PR #847, fix connection pool timeout in health check

The router reads intent, picks a Go agent paired with a debugging skill, and runs the full lifecycle. You typed one sentence. The system did the rest.

The Pipeline

  ROUTE        PLAN         EXECUTE      VERIFY       DELIVER      RECORD
 ┌──────┐    ┌──────┐    ┌──────┐    ┌──────┐    ┌──────┐    ┌──────┐
 │ /do  │───▶│ Task │───▶│Agent │───▶│Tests │───▶│  PR  │───▶│Route │
 │Router│    │ Plan │    │+Skill│    │Gates │    │Branch│    │Result│
 └──────┘    └──────┘    └──────┘    └──────┘    └──────┘    └──────┘

Anti-Rationalization

This is the single thing that separates it from "agent with a system prompt."

Agent SaysWhat Happens
"Code looks correct, skip tests"Exit gate requires test output. Blocked.
"Trivial change, no verification"Hook blocks completion without evidence.
"Similar to before"Skill demands case-specific proof.
"User is in a hurry"Protocol overrides time pressure.
"I'm confident"Gate demands exit code, not assertion.

Hooks fire automatically. Gates block completion. Skills encode counter-arguments at every skip-worthy step. The agent verifies or it doesn't finish.

For what I do, the difference is enormous. If you're doing simple single-file edits, maybe less so.

Knowledge Work Is First-Class

The same routing serves knowledge work. The content engine researches, drafts in a calibrated voice, validates against 397 AI patterns, and repurposes finished pieces for each platform. /html turns any request into a single self-contained HTML file: report, slide deck, prototype, data viz, diagram. Non-engineers who try the toolkit consistently name the HTML artifacts as the thing they love. No code, no setup beyond the installer.

It Proves Its Own Changes

Changes to the toolkit itself ship with evidence. New skills get blind A/B tests against a no-skill baseline before merge. Routing and writing-standard decisions carry measured verdicts; PHILOSOPHY.md cites the numbers. Experiments that lost go into the negative-results registry, what-didnt-work.md; the registry now covers routing reversals, unvalidated A/B citations, and disabled lint rules alongside the original program refutations.

The automated nightly evolution loop (/evolve, writes to evolution-reports/) ran regularly through mid-May 2026. It is currently dormant; recent evidence has come from manual PRs instead.

Installation

git clone https://github.com/notque/vexjoy-agent.git ~/vexjoy-agent
cd ~/vexjoy-agent
./install.sh

Links into ~/.claude/ and mirrors into ~/.codex/, ~/.factory/, ~/.reasonix/ — each mirror only when that runtime is detected (its command on PATH or its home dir already exists). The installer asks symlink (live updates via git pull) or copy (stable snapshot).

Want only part of the toolkit? Run ./install.sh --configure to pick which skills, agents, and hooks install, or copy .local.example/profile.yaml to .local/profile.yaml and edit. No profile file = full install, unchanged behavior. Credit: @thomasvan. Details: .local.example/README.md.

CLIEntry Point
Claude Code/do
Codex$do
Factory/do
Reasonix/do

Full setup: docs/start-here.md

Codex CLI Parity

Mirrors agents, skills, and supported hooks into ~/.codex/. The original six-hook allowlist was correct for Codex v0.114, when tool hooks only intercepted Bash. Current support requires Codex v0.144.1+ and classifies the 74 Claude hook registrations as 26 native, 35 adapter-backed, and 13 unsupported (61 supported). These are registration counts, not unique hook files. The installer also preserves explicit per-subagent model routing for GPT-5.6 Sol by setting the MultiAgent V2 compatibility keys documented in openai/codex#31814.

Codex now exposes apply_patch to tool hooks. VexJoy's adapter converts each patch operation into the Write/Edit payload expected by existing guards, but it cannot intercept writes performed through unified_exec, unmatched MCP tools, WebSearch, or other unsupported tool paths. PreCompact and Stop adapters also receive less telemetry than Claude Code: Codex does not provide Claude's conversation_history or session_data. This is expanded compatibility, not full Claude parity.

After install or any hook-definition change, run /hooks in Codex and review the new definitions before trusting them. Codex hash-trusts hook commands and skips changed, unreviewed definitions.

Gemini CLI / Antigravity CLI Support (removed)

Gemini CLI support removed (deprecated upstream, transitioned to Antigravity CLI); Antigravity support pending CLI maturity. Per Google's transition announcement, Gemini CLI stops serving requests on 2026-06-18 for Google AI Pro / Ultra and free Gemini Code Assist for individuals. Gemini API integrations (image-gen backends, sprite pipeline, GEMINI_API_KEY) are unaffected and stay in the toolkit.

If a prior install mirrored into ~/.gemini/, remove the stale mirrors with:

rm -rf ~/.gemini/skills ~/.gemini/agents ~/.gemini/hooks ~/.gemini/scripts ~/.gemini/antigravity/plugins/vexjoy-agent
Factory CLI Support

Mirrors agents (as "droids"), skills, and all hooks into ~/.factory/. Hook config merges into ~/.factory/settings.json with paths rewritten.

Reasonix Support

Mirrors skills, scripts, and the allowlisted hooks (scripts/reasonix-hooks-allowlist.txt) into ~/.reasonix/ (no agent or custom-command surface, so neither is installed; the /do router rides in as a skill). Reasonix fires only 4 events (PreToolUse, PostToolUse, UserPromptSubmit, Stop), so only hooks for those events are allowlisted. Hook config is written to the hooks key of ~/.reasonix/settings.json in Reasonix's native flat shape (one entry per hook, match regex over the tool name); the generator builds absolute python3 commands, so no path rewrite is applied. MCP/model/permissions in ~/.reasonix/config.json are user-owned and left untouched.

Token-saving mode

The toolkit supplies its own routing, domain knowledge, methodology, and enforcement. The default system prompt duplicates most of that.

claude --system-prompt "."

Strips built-in tool-use instructions. The toolkit's agents, skills, hooks, and CLAUDE.md provide equivalent coverage.

Four Layers

LayerCountDoes
Agents44Domain knowledge: idiom tables, failure mode catalogs, error-to-fix mappings
Skills122Phased methodology with gates. Can't skip steps. Each phase has exit criteria requiring evidence.
Hooks78Fire on lifecycle events. Block incomplete work. Zero LLM cost.
Scripts136Determinism: test runners, linters, validators. No LLM judgment.

Full skill catalog: docs/skills.md.

┌─────────────────────────────────────────────────┐
│  SKILL.md                                       │
│  ┌─ Frontmatter ─────────────────────────────┐  │
│  │ triggers, pairs_with, success-criteria     │  │
│  └────────────────────────────────────────────┘  │
│  Reference Loading Table (conditional imports)   │
│  Phased Instructions (numbered, with gates)      │
│  Verification (evidence requirements)            │
└─────────────────────────────────────────────────┘

Built with the Toolkit

A game built entirely by Claude Code using these agents, skills, and pipelines:

Choose Your Path

I just want to use it Install, learn /do, done.

I do knowledge work Writing, research, data analysis, moderation, HTML artifacts. No code.

I'm a developer Architecture, extension points, adding agents and skills.

I'm an AI power user Routing tables, pipelines, hooks, telemetry DB.

I'm an AI agent Machine-dense inventory. Tables, paths, schemas.

I'm on LinkedIn 🚀 Thought leadership. Agree? 👇

Philosophy

  • Zero-expertise operation. Say what you want. The system classifies, dispatches, enforces, delivers.
  • LLMs orchestrate, programs execute. Deterministic work belongs to scripts. LLM judgment handles design decisions, diagnosis, review.
  • Density. Every word carries instruction, rule, or decision. Cut everything else.
  • Breadth over depth. Right context ensures correctness. Unfocused context adds cost.
  • Structural enforcement. Exit codes enforce what instructions can't. Quality gates are automated, not advisory.
  • Everything pipelines. Complex work decomposes into phases. Phases have gates. Gates prevent cascading failures.

Full design philosophy: PHILOSOPHY.md

Maintenance

One report-only script surfaces upkeep work; it prints a digest and never edits, deletes, or blocks.

  • python3 scripts/stale-skill-scan.py --top 20 ranks stale skills and agents as pruning candidates. Run it quarterly; see docs/deprecation-template.md.

Scheduled work follows the same boundary as everything else: judgment uses agents; repeatable plumbing uses scripts.

NeedUse
Run a deterministic command on a schedulescripts/agent-scheduler.py with runner: "command"
Run an agent judgment on a schedule, webhook, or file changescripts/agent-scheduler.py with the default runner: "claude"
Install or remove a user crontab entry safelyscripts/crontab-manager.py
Audit shell cron reliabilitycron-automation
Keep one interactive objective moving until criteria verifyobjective-loop

Contributing

See CONTRIBUTING.md.

License

MIT. See LICENSE.

其他

中风险

  • 来源需自行核对维护者身份。
  • 包含脚本或命令调用,安装前请复核。
  • 未检测到明显外部权限要求。
  • 存在潜在风险命令,请谨慎安装。
  • 扫描发现:3 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/notque/vexjoy-agent.git
  3. 将 "skills/code-quality/python-quality-gate" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/notque/vexjoy-agent.git
  3. 将 "skills/code-quality/python-quality-gate" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/notque/vexjoy-agent.git
  3. 将 "skills/code-quality/python-quality-gate" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/notque/vexjoy-agent.git
  3. 将 "skills/code-quality/python-quality-gate" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/notque/vexjoy-agent.git
  3. 将 "skills/code-quality/python-quality-gate" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: python-quality-gate
description: "Python quality checks: ruff, pytest, mypy, bandit in deterministic order."
user-invocable: false
allowed-tools:
  - Read
  - Write
  - Bash
  - Grep
  - Glob
  - Edit
  - Task
  - Skill
agent: python-general-engineer
routing:
  force_route: true
  not_for: "general Python coding (use python-general-engineer), data analysis scripts, or tutorials — fires when the user wants ruff/mypy/pytest/bandit quality checks run on existing code"
  triggers:
    - "Python quality"
    - "ruff check"
    - "bandit scan"
    - "mypy check"
    - "python lint"
    - "python quality gate"
    - "check python"
    - "pre-commit check"
  category: code-quality
  pairs_with:
    - code-linting
    - test-driven-development

Python Quality Gate Skill

Run four quality tools in deterministic order -- ruff, pytest, mypy, bandit -- and produce a structured pass/fail report with severity-categorized issues and auto-fix commands.

Reference Loading Table

SignalLoad These FilesWhy
writing the quality gate reportreport-template.mdLoads detailed guidance from report-template.md.
invoking ruff/mypy/pytest; severity classification and pass/fail thresholdstool-commands.mdLoads detailed guidance from tool-commands.md.

Instructions

Phase 1: Detection and Setup

Step 1: Read CLAUDE.md and detect project configuration.

Read and follow the repository's CLAUDE.md before any execution. Then detect project configuration:

ls -la pyproject.toml setup.py setup.cfg mypy.ini .python-version 2>/dev/null

Identify Python version target, ruff config, pytest config, mypy config from pyproject.toml. Only validate code -- never add tools, features, or flexibility not requested.

Step 2: Detect source and test directories.

ls -d src/ app/ lib/ 2>/dev/null || echo "Source: current directory"
ls -d tests/ test/ 2>/dev/null || echo "Tests: not found"

Step 3: Verify tool availability.

ruff --version
pytest --version
mypy --version || echo "mypy not installed (optional)"
bandit --version || echo "bandit not installed (optional)"

If ruff or pytest are missing, STOP. These are required:

ERROR: Required tool not found: {tool_name}
Install with: pip install ruff pytest pytest-cov

Do not install missing tools automatically unless the user explicitly requests it. Do not modify pyproject.toml or configuration files unless explicitly asked.

Gate: ruff and pytest available. Project structure identified. Proceed only when gate passes.

Phase 2: Execute Quality Checks

Run all checks in fixed order, capturing full output for each. Show complete command output with exact file paths and line numbers -- never summarize or paraphrase tool output, because summarization hides the details engineers need to locate and fix issues.

Step 1: Ruff linting.

ruff check . --output-format=grouped

Step 2: Ruff formatting check.

ruff format --check .

Step 3: Type checking with mypy (if installed).

mypy . --ignore-missing-imports --show-error-codes

Skip and note in report if mypy is not installed. Even if tests pass, still run mypy when available -- tests check behavior while types check contracts, and passing one does not make the other redundant.

Step 4: Run test suite.

pytest -v --tb=short --cov=src --cov-report=term-missing

If no tests directory exists, skip and note in report. Never skip tests to make the gate pass -- tests verify functionality, and skipping them hides broken code. Only skip optional tools (mypy, bandit) if genuinely unavailable, not to manufacture a passing status.

Step 5: Security scanning with bandit (if installed).

bandit -r src/ -ll --format=screen

Skip and note in report if bandit is not installed. Linting passing does not mean code is correct -- linting finds style issues, not logic or security bugs. Run every available tool.

Gate: All available tools have been run. Full output captured. Proceed to analysis.

Phase 3: Categorize and Analyze

Step 1: Categorize issues by severity.

See references/tool-commands.md for complete severity classification tables.

Summary of severity levels:

  • Critical: F errors (pyflakes), E9xx (syntax), undefined names, test failures, high-severity security
  • High: E501, E711/E712, F841, N8xx, arg-type/assignment mypy errors
  • Medium: W warnings, C4xx, no-untyped-def mypy errors
  • Low: SIM suggestions, UP upgrade suggestions

Always prioritize critical issues over style fixes -- critical issues (F errors, test failures) break functionality while style issues do not. Fix critical first, high second; use auto-fix for bulk style cleanup only after critical issues are resolved.

Step 2: Count auto-fixable issues.

ruff check . --statistics

Issues marked with [*] are auto-fixable. Show suggested auto-fix commands for these issues so users know what can be fixed automatically.

Step 3: Determine overall status.

FAIL if:

  • Any ruff F errors or test failures
  • Any high-severity bandit issues
  • Mypy errors exceed 10 (configurable)
  • Test coverage below 80% (if coverage enabled)

PASS otherwise. Exit with non-zero status if any critical check fails.

Gate: All issues categorized. Pass/fail determined. Proceed to report.

Phase 4: Generate Report

Format a structured markdown report. See references/report-template.md for the full template.

The report MUST include:

  1. Overall PASS/FAIL status
  2. Summary table (each tool's status and issue count)
  3. Total issues and auto-fixable count
  4. Detailed results per tool (issues grouped by severity, then grouped by type and file for readability)
  5. Critical issues requiring attention with file:line references
  6. Auto-fix commands section
  7. Quality metrics: error counts and coverage percentages

Report facts -- show raw command output rather than describing it. No self-congratulation ("great job", "looking good"). Generate the full report even when only style issues are found, because style issues can hide real problems in noise and a full severity-prioritized report surfaces them.

Print the complete report to stdout. Never summarize or truncate. If --output {file} flag was provided, also write report to file. Remove any intermediate temporary files at completion -- keep the final report only if the user requested file output.

Gate: Report generated and displayed. Task complete.

Auto-Fix Mode (only when explicitly requested)

Auto-fix modifies files in place -- never run it without explicit user confirmation. Running ruff --fix blindly can change code semantics (import removal, reformatting), so always run check-only first, review issues, confirm auto-fix intent, then verify changes.

When user explicitly requests auto-fix:

ruff check . --fix
ruff format .

After auto-fix, show the diff so changes can be reviewed, then re-run the quality gate to verify:

git diff

Examples

Example 1: Pre-Merge Quality Check

User says: "Run quality checks before I merge this PR" Actions:

  1. Detect project config and available tools (Phase 1)
  2. Run ruff check, ruff format, mypy, pytest, bandit in order (Phase 2)
  3. Categorize 12 issues: 0 critical, 3 high, 9 medium (Phase 3)
  4. Generate report showing PASSED with 3 high-priority suggestions (Phase 4) Result: Structured report with actionable fix commands

Example 2: Quality Gate Failure

User says: "Check code quality on the payments module" Actions:

  1. Detect config, find src/payments/ directory (Phase 1)
  2. Run all tools -- pytest shows 2 failures, ruff finds F401 errors (Phase 2)
  3. Categorize: 2 critical (test failures), 1 critical (undefined name), 5 medium (Phase 3)
  4. Generate FAILED report with critical issues listed first (Phase 4) Result: FAILED status with prioritized fix list, auto-fix commands for 5 medium issues

Error Handling

Error: "ruff: command not found"

Cause: Ruff is not installed in the current environment Solution: Install with pip install ruff. Do not proceed without ruff -- exit with status 2.

Error: "Tests failed with exit code 1"

Cause: pytest found test failures Solution: This is expected behavior, not a tool error. Parse output, include failure details in report, mark overall status as FAILED, continue with remaining checks.

Error: "No Python files found"

Cause: Running from wrong directory or not a Python project Solution: Verify location with ls pyproject.toml src/ tests/. Run from project root.

Error: "Mypy cache corruption"

Cause: Stale or corrupted .mypy_cache directory Solution: Clear cache with rm -rf .mypy_cache and retry. If mypy continues to fail, skip type checking and note in report.

References

Reference Files

  • ${CLAUDE_SKILL_DIR}/references/tool-commands.md: Severity classifications, expected output formats, CLI flags
  • ${CLAUDE_SKILL_DIR}/references/report-template.md: Full structured report template
  • ${CLAUDE_SKILL_DIR}/references/pyproject-template.toml: Complete ruff, pytest, mypy, bandit configuration

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