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

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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。
  • 未检测到高风险命令。
  • 扫描发现:1 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/content/reddit-moderate" 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/content/reddit-moderate" 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/content/reddit-moderate" 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/content/reddit-moderate" 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/content/reddit-moderate" 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: reddit-moderate
description: "Reddit moderation via PRAW: fetch modqueue, classify reports, take actions."
user-invocable: false
argument-hint: "[--auto] [--dry-run]"
agent: python-general-engineer
allowed-tools:
  - Bash
  - Read
routing:
  triggers:
    - "moderate Reddit"
    - "modqueue"
    - "Reddit reports"
    - "Reddit moderation"
    - "check reports"
  category: process
  pairs_with:
    - content-calendar

Reddit Moderate

On-demand Reddit community moderation powered by PRAW. Fetches your modqueue, classifies content against subreddit rules and author history using LLM-powered report classification, and executes mod actions you confirm.

Modes

ModeInvocationBehavior
Interactive/reddit-moderateFetch queue, classify, present with analysis, you confirm actions
Auto/loop 10m /reddit-moderate --autoFetch queue, classify, auto-action high-confidence items, flag rest
Dry-run/reddit-moderate --dry-runFetch queue, classify, show recommendations without acting

Reference Loading Table

SignalLoad These FilesWhy
Classifying items, category definitions, confidence thresholdsclassification-prompt.mdRoutes to the matching deep reference
Prompt template, untrusted content handling, prompt injection defenseclassification-prompt.mdRoutes to the matching deep reference
Action mapping by confidence level, config.json formatclassification-prompt.mdRoutes to the matching deep reference
Per-item classification steps, repeat offender check, mass-report detectionclassification-prompt.mdRoutes to the matching deep reference
Script subcommands, flags, usage examplesscript-commands.mdRoutes to the matching deep reference
Exit codes, error troubleshootingscript-commands.mdRoutes to the matching deep reference
Scan commands, setup commands, queue/report commandsscript-commands.mdRoutes to the matching deep reference
Subreddit data directory structure, file purposescontext-loading.mdRoutes to the matching deep reference
Setup flow for new subreddits, bootstrappingcontext-loading.mdRoutes to the matching deep reference
Credentials, prerequisites, dry-run defaultcontext-loading.mdRoutes to the matching deep reference
Context loading sequence, missing file handlingcontext-loading.mdRoutes to the matching deep reference

Instructions

Interactive Mode (default)

Phase 1: FETCH -- Get the modqueue with classification prompts.

python3 skills/content/reddit-moderate/scripts/reddit-mod.py queue --json --limit 25 | python3 skills/content/reddit-moderate/scripts/reddit-mod.py classify

This pipes modqueue items through the classify subcommand, which loads subreddit context from reddit-data/{subreddit}/ and assembles a classification prompt for each item. The output is a JSON array where each result contains item metadata, heuristic flags (mass_report_flag, repeat_offender_count), and a prompt field with the fully rendered classification prompt.

The classify subcommand is a prompt assembler only; it does not call any LLM. Fields classification, confidence, and reasoning are null/empty placeholders for the LLM to fill in Phase 2.

Read the output. For each item, read the prompt field and classify it.

Phase 2: CLASSIFY -- For each item, read the rendered classification prompt and assign a classification. The prompt contains all subreddit context, rules, author history, and report signals. Classify as one of: FALSE_REPORT, VALID_REPORT, MASS_REPORT_ABUSE, SPAM, BAN_RECOMMENDED, NEEDS_HUMAN_REVIEW.

Assign a confidence score (0-100) and one-sentence reasoning for each item.

Load references/classification-prompt.md for category definitions, the full prompt template, per-item classification steps, and confidence thresholds.

Phase 3: PRESENT -- For each modqueue item, present a summary grouped by classification. Include the classification label and confidence:

Item 1: [t3_abc123] "Post title here"
  Author: u/username (score: 5, reports: 2)
  Report reasons: "spam", "off-topic"
  Body: [first 200 chars of content]
  Classification: VALID_REPORT (confidence: 92%)
  Reasoning: Author history shows 5 promotional posts in 7 days with no
             community engagement. Violates subreddit rules against self-promotion.
  Recommendation: REMOVE (reason: Rule 3)

Item 2: [t1_def456] "Comment text here"
  Author: u/other_user (score: 12, reports: 1)
  Report reason: "rude"
  Classification: FALSE_REPORT (confidence: 88%)
  Reasoning: Sarcastic but within community norms. Report appears frivolous.
  Recommendation: APPROVE

Phase 4: CONFIRM -- Ask the user to confirm or override recommendations. Wait for user input. Wait for explicit user confirmation before proceeding.

Phase 5: ACT -- Execute confirmed actions:

python3 skills/content/reddit-moderate/scripts/reddit-mod.py approve --id t1_def456
python3 skills/content/reddit-moderate/scripts/reddit-mod.py remove --id t3_abc123 --reason "Rule 3: Self-promotion"

Report results after each action.

Load references/script-commands.md for all subcommand flags and examples.

Auto Mode (for /loop)

When invoked with --auto argument or when the user says "auto mode":

  1. Fetch queue and build classification prompts:

    python3 skills/content/reddit-moderate/scripts/reddit-mod.py queue --auto --since-minutes 15 --json | python3 skills/content/reddit-moderate/scripts/reddit-mod.py classify
    
  2. For each item, read the rendered prompt field and classify it using the categories and confidence scoring from references/classification-prompt.md.

  3. For items meeting the confidence threshold:

    • FALSE_REPORT / MASS_REPORT_ABUSE => approve
    • SPAM => remove as spam
    • VALID_REPORT => remove with generated reason
    • BAN_RECOMMENDED => always skip (requires human review regardless of confidence)
  4. For items below the confidence threshold => skip (leave for human review).

  5. Output a summary of actions taken, items skipped, and classifications.

Critical auto-mode rules:

  • Always require human review before banning users
  • Always require human review before locking threads
  • When in doubt, SKIP; false negatives are better than false positives
  • Log every auto-action for the user to review later

Proactive Scan Mode

Scan recent posts/comments for rule violations that were not reported:

python3 skills/content/reddit-moderate/scripts/reddit-mod.py scan --json --classify --limit 50 --since-hours 24

With --classify, the scan output includes classification prompts. Read each prompt and classify the item. Items with scan_flags (job_ad_pattern, training_vendor_pattern, possible_non_english) have heuristic signals that supplement the LLM classification.

Same confidence thresholds and safety rules as auto mode apply.

Reference Loading

Load these references when the task matches the signal:

Signal / TaskReference File
Classifying items, category definitions, confidence thresholdsreferences/classification-prompt.md
Prompt template, untrusted content handling, prompt injection defensereferences/classification-prompt.md
Action mapping by confidence level, config.json formatreferences/classification-prompt.md
Per-item classification steps, repeat offender check, mass-report detectionreferences/classification-prompt.md
Script subcommands, flags, usage examplesreferences/script-commands.md
Exit codes, error troubleshootingreferences/script-commands.md
Scan commands, setup commands, queue/report commandsreferences/script-commands.md
Subreddit data directory structure, file purposesreferences/context-loading.md
Setup flow for new subreddits, bootstrappingreferences/context-loading.md
Credentials, prerequisites, dry-run defaultreferences/context-loading.md
Context loading sequence, missing file handlingreferences/context-loading.md

References

This skill uses these shared patterns:

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