SkillAtlasSkill 详情

motion-pipeline

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.

数据与 AI

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: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/game/motion-pipeline" 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/game/motion-pipeline" 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/game/motion-pipeline" 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/game/motion-pipeline" 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/game/motion-pipeline" 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: motion-pipeline
promoted_to: game-pipeline
user-invocable: false
description: "CPU-only motion data processing pipeline for game animation: BVH import, contact detection, root decomposition, motion blending, FABRIK IK. No GPU required."
allowed-tools:
  - Read
  - Bash
  - Write
  - Edit
  - Glob
  - Grep
routing:
  triggers:
    - "mocap"
    - "motion data"
    - "animation pipeline"
    - "BVH import"
    - "contact detection"
    - "IK solve"
    - "motion blend"
    - "bone trajectory"
    - "root extraction"
    - "FABRIK"
    - "skeletal animation data"
  category: game-animation
  pairs_with:
    - game-sprite-pipeline
    - phaser-gamedev
  agents:
    - rive-skeletal-animator
    - pixijs-combat-renderer
    - game-asset-generator

Motion Pipeline Skill

CPU-only motion data processing pipeline for game animation, inspired by Meta's ai4animationpy framework (CC BY-NC 4.0). All operations run on numpy and scipy with no GPU or PyTorch required.

Why standalone implementations?

ai4animationpy's Math/Tensor.py imports torch unconditionally at the top level, which propagates through every module (Animation, Import, IK, Math). This means zero ai4animationpy modules are importable without PyTorch installed. The standalone implementations in scripts/motion-pipeline.py replicate the key algorithms from their source code using only numpy + scipy.

Environment setup

# Create venv (one-time)
python3 -m venv /home/feedgen/vexjoy-agent/motion-pipeline-env/

# Install CPU-only deps
motion-pipeline-env/bin/pip install numpy scipy pygltflib Pillow

# Verify
motion-pipeline-env/bin/python -c "import numpy; import scipy; import pygltflib; print('OK')"

The venv is gitignored. The skill documents setup; it does not commit the venv.

Commands

All commands output JSON to stdout. Errors go to stderr with exit code 1.

import-bvh

Parse a BVH mocap file and print a motion summary.

motion-pipeline-env/bin/python scripts/motion-pipeline.py import-bvh FILE \
  [--scale 0.01]   # scale cm->m for CMU/Mixamo files

Output fields: name, num_frames, num_joints, framerate, total_time_seconds, bones[], root_trajectory (x/y/z range).

extract-contacts

Detect ground contact frames per bone (foot, hand) using height + velocity thresholds. Replicates ContactModule.GetContacts() from ai4animationpy.

motion-pipeline-env/bin/python scripts/motion-pipeline.py extract-contacts FILE \
  --bones LeftFoot RightFoot \
  --height 0.1 \
  --vel 0.5

Output: { "bones": { "<name>": { "contact_frames": [...] } }, "total_frames": N }.

decompose

Split motion into root trajectory (WHERE + HOW) and per-joint local Euler angles (POSE). Implements the RootModule / MotionModule decomposition pattern.

motion-pipeline-env/bin/python scripts/motion-pipeline.py decompose FILE \
  --hip Hips

Output: root_trajectory.positions[], root_trajectory.velocities[], root_trajectory.facing_directions[], per_joint_euler_zyx_degrees{}.

First 5 frames shown in stdout; full data requires piping to a file.

blend

Blend two BVH clips at a fixed alpha using SLERP rotations and LERP positions. Clips must share the same bone hierarchy.

motion-pipeline-env/bin/python scripts/motion-pipeline.py blend FILE_A FILE_B \
  --alpha 0.5

Output: summary of the blended motion.

solve-ik

Run FABRIK inverse kinematics on a bone chain at a single frame.

motion-pipeline-env/bin/python scripts/motion-pipeline.py solve-ik FILE \
  --chain Hips:LeftFoot \
  --target 0.2,0.05,0.3 \
  --frame 10

Output: chain[], target[], initial_positions[], solved_positions[], end_effector_error (metres).

generate-move-ts

Convert a BVH mocap file into a TypeScript MoveFrame function compatible with road-to-aew's wrestlingMoves.ts interface. Outputs keyframe-interpolated TypeScript to stdout (and optionally a file).

motion-pipeline-env/bin/python scripts/generate-move-ts.py BVH MOVE_NAME \
  [--scale 0.01] \
  [--contact-bones LeftToeBase RightToeBase LeftHand RightHand] \
  [--num-keyframes 12] \
  [--hip-bone Hips] \
  [--output path/to/output.ts]
ArgumentDefaultPurpose
BVH—Path to .bvh mocap file
MOVE_NAME—Kebab-case name (e.g. roundhouse-kick) used in TS identifiers
--scale0.01Position scale; 0.01 converts cm→m for CMU/Mixamo files
--contact-bonesLeftToeBase RightToeBase LeftHand RightHandBones used to detect the impact window
--num-keyframes12Keyframe count in the output array (min 2)
--hip-boneHipsRoot bone name for trajectory extraction
--outputstdout onlyWrite TS to this file path in addition to stdout

Implementation note: The script imports motion-pipeline.py as a module via importlib rather than calling it as a subprocess. This bypasses the 5-frame truncation applied by the decompose CLI command, giving access to all frames.

Output structure:

// Generated from roundhouse-kick.bvh on 2026-04-13
// Keyframes: 12, Impact window: 0.45-0.55
const ROUNDHOUSE_KICK_KEYFRAMES = [...] as const;

export function getRoundhouseKick(progress: number): MoveFrame {
  // keyframe lookup + linear interpolation
  // isImpact based on detected contact window
  return { attacker, defender, isImpact };
}

The attacker's offsetX/Y/Z are root trajectory positions normalized to start at origin. Rotations are in radians (converted from the BVH's Euler ZYX degrees). The defender reaction is computed procedurally: pushed backward at impact, eases to mat post-impact.

Impact detection: The script finds the first run of 3+ consecutive contact frames across the specified bones. For strike moves, this captures the moment of hit. For walking/idle clips (feet always down), the window will be frame-0 and isImpact will be nearly never true — this is correct behavior.

Validation: The script prints a summary to stderr including trajectory range, impact window, and a structural syntax check. Exit code 1 if validation fails.

Data architecture pattern

The decomposition from ai4animationpy becomes a design contract for all game animation work:

Animation State
  root_trajectory   -- WHERE (position, velocity, facing direction)
  per_joint_euler   -- HOW (local pose in ZYX Euler degrees)
  contact_frames    -- WHAT (contact states for feet, hands)
  [guidance]        -- WHY (intent; handled at game engine layer)

This separation enables:

  • Different movement speeds without distorting body pose
  • Contact-driven game events (damage triggers, sound, VFX)
  • AI/input guidance independent of motion playback

Source reference: ai4animationpy modules adopted

ai4animationpy moduleThis script equivalentNotes
Import/BVHImporter.BVHload_bvh()Same parsing logic; scipy replaces torch
Animation/MotionMotion dataclassnumpy-only; no torch backend
Animation/ContactModuleextract_contacts()Height + velocity criterion identical
Animation/RootModuledecompose() root sectionFK decomposition via matrix inverse
Animation/MotionModuledecompose() joint sectionLocal Euler extraction via scipy
IK/FABRIKsolve_ik_fabrik()Algorithm identical; no Actor dependency

Integration points

Downstream agentData consumed
rive-skeletal-animatorper_joint_euler_zyx_degrees from decompose
pixijs-combat-renderercontact_frames from extract-contacts
combat-effects-upgradecontact_frames (impact timing)
game-asset-generatorProduces source BVH files for this pipeline

Sample BVH for testing

A walking cycle from ai4animationpy demos is available at:

/tmp/ai4animationpy/Demos/BVHLoading/WalkingStickLeft_BR.bvh

This is a full-body biped walking clip from the Geno character rig.

Reference: ai4animationpy

  • Source: /tmp/ai4animationpy (cloned locally)
  • License: CC BY-NC 4.0 (non-commercial; aligned with hobby game projects)
  • GitHub: https://github.com/facebookresearch/ai4animationpy
  • Key finding: ALL modules require torch at import time via Math/Tensor.py line 5. No conditional import path exists. Standalone implementations are the correct approach.

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