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Awesome Code

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huangwb8
awesome-code

当用户明确要求使用 awesome-code、进行多代理协作或需要复杂任务规划时使用。它负责拆解任务、选择专业角色、设置门禁并审查结果;所有 Agent 的创建、隔离、串行或并行执行都必须交给 `parallel-vibe`。⚠️ 不适用:用户只需单一角色完成简单修改或咨询,或未表达多代理协作意图。

Overview

Publisherhuangwb8
Repositoryskills
Skill nameawesome-code
Stars
50
Forks
8
Bundled files
50
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 50 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by huangwb8 on GitHub. Read the source before you install it.

Installation

Install the Awesome Code AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/huangwb8/skills/tree/main/skills/alpha/awesome-code
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/huangwb8/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/alpha/awesome-code .claude/skills/awesome-code
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Awesome Code in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Awesome Code on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Awesome Code is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Awesome Code - AI 自主规划多代理软件开发协调系统

目标

当用户明确要求"使用 awesome-code / 多代理协作 / 并行协调开发"时使用。通过脚本收集可用 Agent 摘要、配置约束与 dispatch_gate,再由 AI 负责任务拆解、角色选择和策略规划;只要计划使用 Agent,就必须把创建、隔离、串行或并行执行交给 parallel-vibe,本 Skill 不得直接创建或调度 Agent。当配置中的 required route agent 缺失时必须阻塞继续执行。⚠️ 不适用:用户仅需单一角色的简单修改或咨询、用户未表达多代理协作意图、用户只是了解技能概念。

流程

输入

输入为用户的复杂开发任务、项目根目录和可用 Agent 配置;可选输入包括 config.yaml 的 required route、parallel-vibe 执行底座、脚本路径和已有任务工作区。仅在用户明确要求多代理/并行协调时触发;复杂开发任务可在获得该授权后由本 Skill 规划,单一角色的局部任务不走本 Skill。

执行步骤

执行前置:动态发现技能安装路径(硬编码部分)

在调用任何脚本之前,必须先运行 scripts/get_path.py 动态发现真实安装路径,并使用返回的绝对路径执行后续命令(避免硬编码 ~/.claude/skills/ / .claude/skills/)。

bash
python3 ~/.claude/skills/awesome-code/scripts/get_path.py
python3 ~/.codex/skills/awesome-code/scripts/get_path.py
# 或(项目级安装)
python3 .claude/skills/awesome-code/scripts/get_path.py
python3 .codex/skills/awesome-code/scripts/get_path.py

从 JSON 输出中读取:

  • skill_root
  • executable_scripts.*(例如 executable_scripts.agent_coordinator)
核心理念
  • 脚本做确定性操作:路径发现、agents/*/SKILL.md frontmatter 摘要提取、配置加载、Agent 缺失检查
  • AI 做语义判断:理解任务、选择 Agent、决定 single-pass / focused-agent / parallel / sequential 策略
  • 少分派优先:小而明确的任务直接完成;在用户已明确授权使用本 Skill 后,只有专业风险、跨模块依赖或用户明确要求协作时才升级
  • 歧义先拦截:目标、边界或验收标准不清楚的高风险/宽泛任务,由 AI 主动澄清或显式记录保守假设
  • 外科手术式修改:每轮遵守 dispatch_guidance.minimal_change_scope_default
  • 目标驱动验证:执行前先决定怎样证明完成,执行后报告验证结果
  • 强制门禁:配置中的 required route agent 缺失、禁用或不可调度时,必须通过 dispatch_gate 阻塞继续执行
  • 留痕可审计:实际调用 required agent 后,需要补 dispatch_receipts 才能证明门禁已被满足
  • 专业化分工:每个子代理专注一个领域,降低单模型的认知负担
  • 执行底座分离:awesome-code 只负责规划、角色选择、门禁和结果审查;Agent 的创建、工作区隔离、串行/并行调度、回收与汇总统一由 parallel-vibe 负责
  • 渐进式信息披露:只在需要时加载对应子代理的 SKILL.md
代理团队
role领域
tdd-workflowTDD 测试驱动开发
systematic-debugging系统化调试与根因分析
code-reviewer代码审查与质量保证
git-workflowGit 工作流与版本控制
frontend-specialist前端开发与组件设计
backend-specialist后端开发与 API 设计
devops-specialistDevOps 与自动化运维
security-specialist应用安全与合规
documentation-specialist技术文档与 API 文档
context-optimizer上下文管理与优化
brainstorming交互式设计优化
mirror-optimizer镜像源优化
writing-plans实施计划与任务拆解
multi-agent-coordinator多代理协调
核心工作流
  1. 运行 get_path.py,拿到 executable_scripts.agent_coordinator 的绝对路径。
  2. 调用 agent_coordinator.py 收集规划上下文,读取 available_agents、config_constraints、dispatch_guidance 与 dispatch_gate。
  3. 若 dispatch_gate.can_proceed = false:
    • 停止继续执行,不要假装已经进入实现阶段
    • 明确说明 blocking_reason 与 missing_agents
    • 只给出“如何补齐 required route agent / 配置 / 运行条件”的下一步
  4. 读取 execution_contract。若计划使用任何 Agent,必须调用配置中声明的执行底座;若契约缺失或无效,dispatch_gate 必须阻塞多代理路径,不得由本 Skill 补默认底座,也不得直接调用宿主的 spawn 或并发机制。
  5. 若门禁允许继续,AI 自主规划:
    • 阅读任务描述和 available_agents 的 description
    • 判断是否需要澄清;用户要求自主推进时,选择最保守且可验证的假设
    • 自行选择 single-pass、focused-agent、parallel 或 sequential
    • 若选择子代理,只加载选中 Agent 的 awesome-code/agents/{role}/SKILL.md
    • 若判断某个 config_constraints.required_routes 适用,该 route 中的 agents 视为 required
  6. 按规划执行:
    • single-pass:主模型直接完成
    • focused-agent:把一个 Agent 的执行计划交给 parallel-vibe
    • parallel:把相互独立的 Agent 任务交给 parallel-vibe 并行执行
    • sequential:把存在依赖链的 Agent 任务交给 parallel-vibe 按序执行
    • 全程遵守 dispatch_guidance 的最小变更边界
  7. 聚合结果并留痕:
    • 统一口径(术语/目标/约束)
    • 标注 P0/P1/P2 优先级
    • 为实际调用的 required agent 回填 dispatch_receipts
    • 对照自定验收标准与验证计划给出结果
Agent 选择指导
  • Bug、测试失败和异常优先考虑 systematic-debugging;先根因,后修复。
  • test-first、回归测试和覆盖率任务优先考虑 tdd-workflow。
  • 安全、认证、权限、注入和敏感数据任务优先考虑 security-specialist。
  • 前端实现、UI/UX、设计系统、仪表盘和落地页任务优先考虑 frontend-specialist;需要先探索方向时可先用 brainstorming。
  • API、服务端、数据库和业务逻辑任务优先考虑 backend-specialist。
  • 部署、CI/CD、容器和运维任务优先考虑 devops-specialist。
  • 文档、README 和 API 文档任务优先考虑 documentation-specialist。
  • 计划、拆解和跨代理协调分别考虑 writing-plans 与 multi-agent-coordinator。

最小示例:

bash
python3 ~/.claude/skills/awesome-code/scripts/get_path.py
python3 /ABS/PATH/awesome-code/scripts/agent_coordinator.py "fix login bug"
常用脚本

注意:脚本路径以 get_path.py 输出为准。

  • scripts/get_path.py:输出 skill_root 与可执行脚本绝对路径(JSON)
  • scripts/agent_coordinator.py:Agent 摘要收集 + 配置约束 + dispatch_gate
  • scripts/subagent_policy.py:读取 required routes 并校验配置中 required route agents 是否可用
  • scripts/subagent_dispatch_audit.py:生成 dispatch_manifest 并校验 dispatch_receipts
  • scripts/create_test_session.py:创建 A/B 轮会话目录与计划骨架(便于追溯)
  • scripts/test_runner.py:运行测试/覆盖率
  • scripts/code_analyzer.py:静态分析与质量检查
  • scripts/performance_benchmark.py:基准测试与报告
Single Source of Truth
  • 版本号仅在 awesome-code/config.yaml:skill_info.version 维护;SKILL.md 不记录版本历史。
  • 代理启用状态:awesome-code/config.yaml:multi_agent.enabled_agents
  • 多代理执行底座:awesome-code/config.yaml:dependencies.agent_execution(当前声明为 parallel-vibe;运行时不补默认值)
  • 强制分派策略:awesome-code/config.yaml:multi_agent.dispatch_policy.*
  • 质量阈值/开关:awesome-code/config.yaml:tdd、awesome-code/config.yaml:code_review 等
  • 变更记录:awesome-code/CHANGELOG.md
参考资料(仅一层深度;需要时按需加载)
  • TDD:awesome-code/references/tdd-best-practices.md
  • 系统化调试:awesome-code/references/debugging-systematic.md
  • 代码审查清单:awesome-code/references/code-review-checklist.md
  • Git 工作流:awesome-code/references/git-workflow.md
  • 多代理协调模式:awesome-code/references/multi-agent-patterns.md
  • 上下文优化策略:awesome-code/references/context-optimization.md
  • 批判性思维与测试优化:awesome-code/references/CRITICAL_THINKING_GUIDE.md
  • A 轮计划模板:awesome-code/references/A_ROUND_PLAN_TEMPLATE.md
  • 建设性建议:awesome-code/references/CONSTRUCTIVE_SUGGESTION_GUIDELINES.md
  • 问题挖掘技巧:awesome-code/references/ISSUE_DISCOVERY_TECHNIQUES.md
  • 反例库:awesome-code/references/ANTI_PATTERNS_LIBRARY.md
  • 脚本调用策略:awesome-code/references/SCRIPT_PATH_STRATEGY.md

输出

自主规划输出

agent_coordinator.py 不再输出 recommended_agents、confidence 或 execution_plan。这些属于 AI 的语义规划职责。

脚本输出至少包含:

  • planning_mode
  • available_agents
  • agent_count
  • config_constraints.required_routes
  • dispatch_gate.can_proceed
  • dispatch_gate.blocking_reason
  • dispatch_gate.missing_agents
  • dispatch_guidance
  • execution_contract

输出管理

BenszAPI 任务工作区

本技能用于“复杂开发任务”的多代理规划:确定性脚本只负责路径发现、Agent 摘要收集、配置约束读取和 required route 可用性门禁;任务理解、Agent 选择与执行策略由 AI 自主完成,实际 Agent 执行必须通过 parallel-vibe。

校验

校验 get_path.py 与 agent_coordinator.py 的输出是否包含 available_agents、agent_count、required routes、dispatch_gate 和 execution_contract 字段;确认执行契约完全来自配置,配置缺失或无效时门禁阻塞;确认所选策略与任务风险匹配、实际调用的 required agent 有 dispatch_receipts,并且结果与验证计划可追溯。

失败与恢复

路径发现、配置读取或 required route 检查失败时保留脚本输出并阻塞后续分派,明确缺失 Agent 或阻塞原因;代理执行失败时保留已产生的结果及宿主提供的 workspace 证据,由主 Agent 决定安全重试或回退,不把未执行的代理工作标记为完成。

约束

公共硬约束

本块由 docs/templates/skill-common-constraints.md 统一维护;每个 SKILL.md 的 ## 约束 必须逐字同步本块,不得在副本中改写公共规则。

  • 任务需要落盘时,使用唯一的 ./.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/ 根目录;共享材料放入 shared/,Skill 专属材料放入该 Skill 的 input/、output/、log/。
  • 正式交付物、源代码和正式计划按项目约定保存,不写入任务工作区;未经授权不覆盖、删除、迁移或远程写入。
  • 项目维护变更检查 BAC 可用性并记录需求、AI 产出、工具结果、文件改动和验证摘要;BAC 只做过程审计,不替代署名、责任或合规判断。
  • 不记录 API Key、访问令牌、密码、Cookie、环境/凭据文件、私有 Prompt、身份信息、本地用户名、主机名或不必要的大体积原始数据。
  • 文件路径必须规范化并限制在授权项目范围内;外部 URL、子进程和网络访问遵循最小权限,防止路径遍历、SSRF 和命令注入。
  • Skill 版本唯一记录在自身 config.yaml:skill_info.version;公开 API、协议、目录或配置变更同步文档与 CHANGELOG.md。
  • bensz-collect-bugs 是一个 Agent Skill;仅将 Bensz Agent Skill 或 Bensz 基础设施本身的设计缺陷交给它。先脱敏写入 ~/.bensz-skills/bugs/,当前任务不中断,只有用户明确要求才公开上报,禁止直接修改用户已安装的 Skill 源码。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Awesome Code AI skill do?

当用户明确要求使用 awesome-code、进行多代理协作或需要复杂任务规划时使用。它负责拆解任务、选择专业角色、设置门禁并审查结果;所有 Agent 的创建、隔离、串行或并行执行都必须交给 `parallel-vibe`。⚠️ 不适用:用户只需单一角色完成简单修改或咨询,或未表达多代理协作意图。

Why use Awesome Code on TypingMind?

Because you install it once and use it with any model. Awesome Code is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Awesome Code in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huangwb8/skills/tree/main/skills/alpha/awesome-code. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Awesome Code?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Awesome Code?

As many as you like. As long as a model supports skills, you can use Awesome Code with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Awesome Code AI skill free?

Yes. It is published on GitHub by huangwb8 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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