Agent安装

Name: Agent安装
Author: vibeeval

其他社区

作者仓库星标 487
作者更新于实时读取
作者仓库 vibecosystem

流狐档案事实层信息：领域、Agent、信任分、运行环境、权限与网络

领域: 通用
兼容 Agent: Claude Code
Cursor
Cline
Codex
Windsurf
Gemini CLI

+20
信任分: 88 / 100 · 社区维护
作者 / 版本 / 许可: @vibeeval · 未声明 license
Token 消耗评级: 低消耗
接入复杂程度: 需简单配置
是否需要外部 API Key: 不需要
兼容的系统: 未声明（默认跨平台）
底层运行要求: Python
文件与系统权限: 只读
允许写入 / 修改
Shell 执行
网络行为: 仅限本地
安装命令数: 26 条

档案由构建时根据 SKILL.md 与安装命令自动衍生，可能与作者实际意图存在差异。

需要注意： 未限定 allowed-tools，默认拥有全部工具权限。

输出预览 skill-upgrader-vibeeval.preview

---
name: skill-upgrader
description: Upgrade any skill to v5 Hybrid format using decision theory + modal logic Meta-skill that upgrad…
category: 通用
runtime: Python
---

# skill-upgrader 输出预览

## PART A: 任务判断
- 适用问题：通用任务拆解、检查和交付。
- 输入要求：目标材料、限制条件、期望输出和验收方式。
- 证据边界：围绕“When to Use / Prerequisites / Workflow”读取原文规则，不把推断写成作者承诺。

## PART B: 执行结果
- **01** 任务判断：确认你的需求是否属于通用任务拆解、检查和交付，并标出输入、限制和预期结果。
- **02** 执行计划：优先按“When to Use / Prerequisites / Workflow”拆成步骤，说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果：给出可复制的命令、文件改动、检查清单或内容草稿，并说明如何继续迭代。
- **04** 风险边界：结合 读取文件、写入/修改文件、执行终端命令、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。

## Running Rules
- 读取文件、写入/修改文件、执行终端命令；主要在本地完成；通常不需要额外 API Key。
- 先小样例验证，再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。

SKILL.md SKILL.md

---
name: skill-upgrader
description: Upgrade any skill to v5 Hybrid format using decision theory + modal logic Meta-skill that upgrad…
category: 通用
source: vibeeval/vibecosystem
---

# skill-upgrader

## 什么时候使用
- 把通用方向的常用动作沉淀成 Agent 可调用的技能 适合处理通用任务拆解、检查、交付和复盘，核心价值是把输入、判断、执行、验证和交付边界固定下来，避免 Agent 泛泛回答。 把任务拆成可执行、可检查、可继续迭代的步骤；通常不需要额外…
- 面向通用任务拆解、检查和交付，优先处理能明确输入、步骤和验收标准的工作。

## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。

## 执行规则
- 围绕「When to Use / Prerequisites / Workflow」组织步骤，不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令；主要在本地完成；通常不需要额外 API Key。
- 先跑小样例，确认结果可检查后再扩大任务范围。

## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown，不编造命令、平台或依赖。

证据边界与执行链路

作者原文负责流程事实；仓库文件负责来源和命令；流狐只补充适用场景、限制和质量判断。

skill "skill-upgrader" {
  输入层     -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
  上下文层   -> When to Use / Prerequisites / Workflow
  规则层     -> SKILL.md 触发条件 / 执行顺序 / 输出格式
  运行层     -> Python | 读取文件、写入/修改文件、执行终端命令 | 主要在本地完成
  安全层     -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
  输出层     -> 可复制结果 + 检查清单 + 下一步迭代
}

解读按原文结构重写，命令、链接、术语均保留；右侧可核对作者原始 SKILL.md

先判断是否适合

核心用途：把通用方向的常用动作沉淀成 Agent 可调用的技能适合处理通用任务拆解、检查、交付和复盘，核心价值是把输入、判断、执行、验证和交付边界固定下来，避免 Agent 泛泛回答。把任务拆成可执行、可检查、可继续迭代的步骤；通常不需要额外 API Key，适…
适用任务：更适合通用任务拆解、检查、交付和复盘，尤其是输入、步骤、验收口径可以反复复用的场景。
不宜硬套：如果任务只有一次性问答、来源材料很少，或需要作者没有说明的命令和外部服务，应先降级为普通对话。

作者设计意图

触发依据：使用时机、上下文收集、输出格式和失败处理共同决定它是否适合当前任务。
来源边界：作者原文负责事实，仓库文件负责命令和文件证据；Fluxly 只补充用途、限制和使用判断。

作者的方法与取舍

执行主线：任务围绕「When to Use / Prerequisites / Workflow」组织，把触发条件、必要上下文、执行规则和交付标准连成一条可复核的链路。
设计重点：重点不在重复教程，而在解释作者为什么规定这些步骤、哪些条件不可省略，以及结果偏离时应回到哪一层检查。
使用判断：先确认任务与原文边界一致，再决定是否启用；来源没有说明的命令、平台和依赖保持未知，不由 Fluxly 补写。

边界和复核

依赖条件：通常不需要额外 API Key，适合先用小任务验证触发是否稳定。
权限提醒：会涉及 read / write / shell-exec 权限，执行前要说明可读写范围、命令边界和回退方式。
质量标准：结果应包含明确产物、必要证据和下一步动作；如果只是泛泛解释，需要补充输入和验收条件后重跑。

Skill Upgrader

Meta-skill that upgrades any SKILL.md to Decision Theory v5 Hybrid format using 4 parallel Ragie-backed agents.

When to Use

"Upgrade this skill to v5"
"Formalize this skill with decision theory"
"Add MDP structure to this skill"
"Apply the skill-upgrader to X"

Prerequisites

Ragie RAG with indexed books:

decision-theory partition: LaValle Planning Algorithms, Sutton & Barto RL
modal-logic partition: Blackburn Modal Logic, Huth & Ryan Logic in CS

Workflow

Step 1: Setup Session

SESSION=$(date +%Y%m%d-%H%M%S)-upgrade-{skill_name}
mkdir -p thoughts/skill-builds/${SESSION}

Step 2: Initialize Blackboard

Create thoughts/skill-builds/{session}/00-blackboard.md:

# Skill Upgrade: {skill_name}
Started: {timestamp}

## Input Skill
{path_to_skill}

## Target Format
Decision Theory v5 Hybrid

## Agent Findings
(Agents append below)

---

Step 3: Launch 4 Agents in Parallel

Use Task tool to spawn all 4 agents simultaneously. Each agent:

Reads the input skill
Queries Ragie for their specific book
Appends findings to the blackboard

Agent 1: LaValle Planner

Book: LaValle's "Planning Algorithms" (decision-theory partition) Focus: States, Actions, Transitions

Task(
  subagent_type="general-purpose",
  prompt="""
INPUT SKILL: {path}
BLACKBOARD: thoughts/skill-builds/{session}/00-blackboard.md

YOUR BOOK: LaValle's "Planning Algorithms" in Ragie partition 'decision-theory'

TASK: Identify MDP structure in the skill.

Query Ragie:
```bash
uv run python scripts/ragie_query.py -q "MDP state space definition" -p decision-theory
uv run python scripts/ragie_query.py -q "action space sequential decisions" -p decision-theory
uv run python scripts/ragie_query.py -q "POMDP partial observability" -p decision-theory

Read the input skill and answer:

What are the STATES? (phases, modes, tracked info)
What are the ACTIONS? (what can agent do in each state)
How do TRANSITIONS work? (deterministic or stochastic)
Is this POMDP or fully observable?

WRITE to blackboard section: ## Agent 1: States, Actions & Transitions

Format as plain English with LaValle chapter citations. """ )


---

## Agent 2: Sutton & Barto Optimizer

**Book:** Sutton & Barto's "Reinforcement Learning" (decision-theory partition)
**Focus:** Policy, Termination, Value
**Depends on:** Agent 1

Task( subagent_type="general-purpose", prompt=""" INPUT SKILL: {path} BLACKBOARD: thoughts/skill-builds/{session}/00-blackboard.md

YOUR BOOK: Sutton & Barto's "Reinforcement Learning" in Ragie partition 'decision-theory'

WAIT: Read Agent 1's findings from blackboard first.

TASK: Design policy and termination conditions.

Query Ragie:

uv run python scripts/ragie_query.py -q "policy deterministic stochastic" -p decision-theory
uv run python scripts/ragie_query.py -q "episodic termination conditions" -p decision-theory
uv run python scripts/ragie_query.py -q "reward function design" -p decision-theory

Using Agent 1's states and actions, answer:

What's the POLICY? (state → action rules)
When does it END? (terminal states, success/failure)
What are REWARDS? (goals +, costs -)
Which states are HIGH/LOW value?

WRITE to blackboard section: ## Agent 2: Policy & Values

Format as plain English with Sutton & Barto section citations. """ )


---

## Agent 3: Blackburn Modal Logician

**Book:** Blackburn's "Modal Logic" (modal-logic partition)
**Focus:** Constraints (temporal, epistemic, deontic)

Task( subagent_type="general-purpose", prompt=""" INPUT SKILL: {path} BLACKBOARD: thoughts/skill-builds/{session}/00-blackboard.md

YOUR BOOK: Blackburn's "Modal Logic" in Ragie partition 'modal-logic'

TASK: Extract constraints from the skill.

Query Ragie:

uv run python scripts/ragie_query.py -q "temporal logic LTL operators" -p modal-logic
uv run python scripts/ragie_query.py -q "epistemic logic knowledge" -p modal-logic
uv run python scripts/ragie_query.py -q "deontic logic obligations" -p modal-logic

Read the input skill and identify:

TEMPORAL: "must do X before Y" → □, ◇, U
EPISTEMIC: "must know X" → K operator
DEONTIC: "must/forbidden/may" → O, F, P
DYNAMIC: "action causes effect" → [action]

WRITE to blackboard section: ## Agent 3: Constraints

For each constraint:

Plain English description
Modal logic notation
Why it matters
Blackburn chapter citation """ )


---

## Agent 4: Huth & Ryan Verifier

**Book:** Huth & Ryan's "Logic in Computer Science" (modal-logic partition)
**Focus:** Validation, Safety, Liveness
**Depends on:** Agents 1-3

Task( subagent_type="general-purpose", prompt=""" INPUT SKILL: {path} BLACKBOARD: thoughts/skill-builds/{session}/00-blackboard.md

YOUR BOOK: Huth & Ryan's "Logic in Computer Science" in Ragie partition 'modal-logic'

WAIT: Read Agents 1-3 findings from blackboard first.

TASK: Verify consistency and completeness.

Query Ragie:

uv run python scripts/ragie_query.py -q "safety properties verification" -p modal-logic
uv run python scripts/ragie_query.py -q "liveness properties eventually" -p modal-logic
uv run python scripts/ragie_query.py -q "model checking CTL" -p modal-logic

Check:

SAFETY: What bad things never happen? □¬(bad)
LIVENESS: What good things eventually happen? ◇(good)
CONSISTENCY: Any contradictions between agents?
COMPLETENESS: Any gaps in coverage?

WRITE to blackboard section: ## Agent 4: Verification

Report with ✓/✗ for each property. Overall verdict: PASS or NEEDS_WORK Huth & Ryan section citations. """ )


---

## Step 4: Synthesize Final Skill

After all agents complete, read the blackboard and create:

**Output:** `thoughts/skill-builds/{session}/SKILL-upgraded.md`

Use v5 Hybrid template:

```yaml
---
name: {original_name}
description: {original_description}
version: 5.1-hybrid
---

# Option: {name}

## Initiation (I)
[From original + Agent 1 state analysis]

## Observation Space (Y)
[From Agent 1 POMDP analysis]

## Action Space (U)
[From Agent 1 actions]

## Policy (pi)
[From Agent 2 state→action rules]

## Termination (beta)
[From Agent 2 episode structure]

## Q-Heuristics
[From Agent 2 value guidance]

## Constraints
[From Agent 3 modal logic]

## Verification
[From Agent 4 safety/liveness]

Example Usage

User: "Upgrade .claude/skills/implement_plan/SKILL.md to v5 Hybrid"

Claude:
1. Creates session directory
2. Initializes blackboard
3. Launches 4 agents in parallel (Task tool)
4. Waits for completion
5. Reads blackboard
6. Synthesizes upgraded skill
7. Reports: "Upgraded skill at thoughts/skill-builds/.../SKILL-upgraded.md"

Ragie Query Reference

# Decision theory partition
uv run python scripts/ragie_query.py -q "your question" -p decision-theory

# Modal logic partition
uv run python scripts/ragie_query.py -q "your question" -p modal-logic

# With reranking for better results
uv run python scripts/ragie_query.py -q "your question" -p decision-theory --rerank

Files Created

After upgrade:

thoughts/skill-builds/{session}/
├── 00-blackboard.md      # Agent collaboration
├── SKILL-upgraded.md     # Final v5 Hybrid skill
└── validation-report.md  # Agent 4 verification

安装命令

一键复制安装选择包管理器,点击右侧复制,粘贴到终端回车即可

npx skills add vibeeval/vibecosystem/skills/skill-upgrader -y

查看通用安装教程

查看 Skill 原文

兼容工具

Claude Code
Cursor
Cline
Codex
Windsurf
Gemini
OpenCode
Roo Code
AAide
+Augment
GitHub Copilot
OpenClaw
KKiro
Junie
WWarp
Goose
Trae
Kilo Code
OpenHands
Amp
Zencoder
CContinue
AIAider
CodeBuddy
CodeGeeX
Replit Agent

安全审计

88 / 100

社区

未声明 license/许可。
未声明 version。
未限定 allowed-tools，默认拥有全部工具权限。

元信息

来源: github
vibeeval/vibecosystem
English

@vibeeval 的其他技能

分类与标签

分类其他

先判断是否适合

作者设计意图

作者的方法与取舍

边界和复核

Skill Upgrader

When to Use

Prerequisites

Workflow

Step 1: Setup Session

Step 2: Initialize Blackboard

Step 3: Launch 4 Agents in Parallel

Agent 1: LaValle Planner

Example Usage

Ragie Query Reference

Files Created

讨论