skill-upgrader by parcadei/continuous-claude-v3
npx skills add https://github.com/parcadei/continuous-claude-v3 --skill skill-upgrader使用 4 个并行 Ragie 支持的代理,将任何 SKILL.md 升级到决策理论 v5 混合格式的元技能。
具有索引书籍的 Ragie RAG:
SESSION=$(date +%Y%m%d-%H%M%S)-upgrade-{skill_name}
mkdir -p thoughts/skill-builds/${SESSION}
创建 thoughts/skill-builds/{session}/00-blackboard.md:
# 技能升级:{skill_name}
开始时间:{timestamp}
## 输入技能
{path_to_skill}
## 目标格式
决策理论 v5 混合格式
## 代理发现
(代理将在此下方追加内容)
---
使用 Task 工具同时生成所有 4 个代理。每个代理:
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书籍: LaValle 的《规划算法》(决策理论分区) 关注点: 状态、动作、转移
Task(
subagent_type="general-purpose",
prompt="""
输入技能:{path}
黑板:thoughts/skill-builds/{session}/00-blackboard.md
你的书籍:Ragie 分区 'decision-theory' 中的 LaValle 的《规划算法》
任务:识别技能中的 MDP 结构。
查询 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
阅读输入技能并回答:
写入黑板章节:## 代理 1:状态、动作与转移
格式为带有 LaValle 章节引用的纯英文。""" )
---
## 代理 2:Sutton & Barto 优化器
**书籍:** Sutton & Barto 的《强化学习》(决策理论分区)
**关注点:** 策略、终止、价值
**依赖:** 代理 1
Task( subagent_type="general-purpose", prompt=""" 输入技能:{path} 黑板:thoughts/skill-builds/{session}/00-blackboard.md
你的书籍:Ragie 分区 'decision-theory' 中的 Sutton & Barto 的《强化学习》
等待:首先从黑板读取代理 1 的发现。
任务:设计策略和终止条件。
查询 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
使用代理 1 的状态和动作,回答:
写入黑板章节:## 代理 2:策略与价值
格式为带有 Sutton & Barto 章节引用的纯英文。""" )
---
## 代理 3:Blackburn 模态逻辑学家
**书籍:** Blackburn 的《模态逻辑》(模态逻辑分区)
**关注点:** 约束(时态、认知、道义)
Task( subagent_type="general-purpose", prompt=""" 输入技能:{path} 黑板:thoughts/skill-builds/{session}/00-blackboard.md
你的书籍:Ragie 分区 'modal-logic' 中的 Blackburn 的《模态逻辑》
任务:从技能中提取约束。
查询 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
阅读输入技能并识别:
写入黑板章节:## 代理 3:约束
对于每个约束:
纯英文描述
模态逻辑表示法
为何重要
Blackburn 章节引用 """ )
书籍: Huth & Ryan 的《计算机科学中的逻辑》(模态逻辑分区) 关注点: 验证、安全性、活性 依赖: 代理 1-3
Task( subagent_type="general-purpose", prompt=""" 输入技能:{path} 黑板:thoughts/skill-builds/{session}/00-blackboard.md
你的书籍:Ragie 分区 'modal-logic' 中的 Huth & Ryan 的《计算机科学中的逻辑》
等待:首先从黑板读取代理 1-3 的发现。
任务:验证一致性和完整性。
查询 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
检查:
写入黑板章节:## 代理 4:验证
报告每个属性时使用 ✓/✗。总体结论:通过 或 需要改进 Huth & Ryan 章节引用。""" )
---
## 步骤 4:合成最终技能
所有代理完成后,阅读黑板并创建:
**输出:** `thoughts/skill-builds/{session}/SKILL-upgraded.md`
使用 v5 混合模板:
```yaml
---
name: {original_name}
description: {original_description}
version: 5.1-hybrid
---
# 选项:{name}
## 初始化
[来自原始技能 + 代理 1 的状态分析]
## 观测空间
[来自代理 1 的 POMDP 分析]
## 动作空间
[来自代理 1 的动作]
## 策略
[来自代理 2 的状态→动作规则]
## 终止条件
[来自代理 2 的回合结构]
## Q-启发式
[来自代理 2 的价值指导]
## 约束
[来自代理 3 的模态逻辑]
## 验证
[来自代理 4 的安全性/活性]
用户:"将 .claude/skills/implement_plan/SKILL.md 升级到 v5 混合格式"
Claude:
1. 创建会话目录
2. 初始化黑板
3. 并行启动 4 个代理(Task 工具)
4. 等待完成
5. 阅读黑板
6. 合成升级后的技能
7. 报告:"升级后的技能位于 thoughts/skill-builds/.../SKILL-upgraded.md"
# 决策理论分区
uv run python scripts/ragie_query.py -q "your question" -p decision-theory
# 模态逻辑分区
uv run python scripts/ragie_query.py -q "your question" -p modal-logic
# 使用重排序以获得更好结果
uv run python scripts/ragie_query.py -q "your question" -p decision-theory --rerank
升级后:
thoughts/skill-builds/{session}/
├── 00-blackboard.md # 代理协作
├── SKILL-upgraded.md # 最终的 v5 混合技能
└── validation-report.md # 代理 4 的验证报告
每周安装数
211
代码仓库
GitHub 星标数
3.6K
首次出现
2026 年 1 月 22 日
安全审计
安装于
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Meta-skill that upgrades any SKILL.md to Decision Theory v5 Hybrid format using 4 parallel Ragie-backed agents.
Ragie RAG with indexed books:
SESSION=$(date +%Y%m%d-%H%M%S)-upgrade-{skill_name}
mkdir -p thoughts/skill-builds/${SESSION}
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)
---
Use Task tool to spawn all 4 agents simultaneously. Each agent:
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:
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:
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:
WRITE to blackboard section: ## Agent 3: Constraints
For each constraint:
Plain English description
Modal logic notation
Why it matters
Blackburn chapter citation """ )
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:
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]
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"
# 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
After upgrade:
thoughts/skill-builds/{session}/
├── 00-blackboard.md # Agent collaboration
├── SKILL-upgraded.md # Final v5 Hybrid skill
└── validation-report.md # Agent 4 verification
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