Evolution
sickn33/agentic-awesome-skills
This skill enables makepad-skills to self-improve continuously during development.
基于双环架构的AGI进化模型，通过意向性分析、人格层映射和元认知检测实现持续自我演进；当用户需要智能对话、人格定制或复杂问题求解时使用
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Install Agi Evolution Model
$ npx skills add LeoYeAI/openclaw-master-skills --skill agi-evolution-model -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills agi-evolution-model --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agi-evolution-model-basic .claude/skills/agi-evolution-model && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "agi-evolution-model" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/agi-evolution-model-basic into .claude/skills/agi-evolution-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agi-evolution-model", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/agi-evolution-model-basicType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill agi-evolution-model -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills agi-evolution-model --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/agi-evolution-model-basic .agents/skills/agi-evolution-model && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "agi-evolution-model" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/agi-evolution-model-basic into .agents/skills/agi-evolution-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agi-evolution-model", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill agi-evolution-model -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills agi-evolution-model --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/agi-evolution-model-basic .cursor/skills/agi-evolution-model && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "agi-evolution-model" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/agi-evolution-model-basic into .cursor/skills/agi-evolution-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agi-evolution-model", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/agi-evolution-model-basic--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill agi-evolution-model -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills agi-evolution-model --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/agi-evolution-model-basic .gemini/skills/agi-evolution-model && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "agi-evolution-model" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/agi-evolution-model-basic into .gemini/skills/agi-evolution-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agi-evolution-model", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills agi-evolution-modelInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill agi-evolution-model -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/agi-evolution-model-basic .github/skills/agi-evolution-model && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "agi-evolution-model" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/agi-evolution-model-basic into .github/skills/agi-evolution-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agi-evolution-model", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill agi-evolution-model -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills agi-evolution-model --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/agi-evolution-model-basic .opencode/skills/agi-evolution-model && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "agi-evolution-model" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/agi-evolution-model-basic into .opencode/skills/agi-evolution-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agi-evolution-model", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
agi-evolution-model- GitHub stars
- 2.2k
- Token cost
- ~3.1k tokens
- SKILL.md length
- 487 words
- Files
- 47 (incl. scripts, references, assets)
- Skills in repo
- 1,215
- Repo updated
- First seen
- Licence
- MIT
At a glance
基于双环架构的AGI进化模型，通过意向性分析、人格层映射和元认知检测实现持续自我演进；当用户需要智能对话、人格定制或复杂问题求解时使用
- Works in 4 steps: 调用默认人格初始化命令 → 【必须】验证检查：再次运行 --check 确认… → 【必须】内容校验：读取 personality.json 确认… → …
- SKILL.md covers 开源协议, ⚠️ 智能体响应规则（最高优先级）, 任务目标 and 前置准备, plus 6 more sections
- Calls python3
What it does
Agi Evolution Model is an agent skill from LeoYeAI/openclaw-master-skills. 基于双环架构的AGI进化模型，通过意向性分析、人格层映射和元认知检测实现持续自我演进；当用户需要智能对话、人格定制或复杂问题求解时使用
Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 48 other files, including scripts, reference files and assets (for example `_meta.json`, `assets/personality_template.json` and `references/architecture.md`).
The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
Example prompts
- “/agi-evolution-model”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- 调用默认人格初始化命令
- 【必须】验证检查：再次运行 --check 确认 is_first_interaction: False
- 【必须】内容校验：读取 personality.json 确认 initialized: true
- 满足以上条件后，才进入交互模式
What it can do on your machine
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Agi Evolution Model loads about 3.1k tokens when it runs, and up to ~82k if it reads all its reference files. Until then it costs about 22 tokens; SKILL.md has 487 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 487 words, ~3,069 tokens.
Download SKILL.mdSave it as
.claude/skills/agi-evolution-model/SKILL.md (or your agent's skills folder). This skill also uses 46 other files; get the full folder from GitHub.- name
- agi-evolution-model
- description
- 基于双环架构的AGI进化模型，通过意向性分析、人格层映射和元认知检测实现持续自我演进；当用户需要智能对话、人格定制或复杂问题求解时使用
- dependency.system
- mkdir -p ./agi_memory
AGI进化模型
开源协议
本项目采用 GNU Affero General Public License v3.0 (AGPL-3.0) 开源协议。 作者：kiwifruit
核心要求：
- 保留版权声明与许可协议
- 修改版本需标注变更日期与作者
- 网络提供服务时必须开放修改后的源码
- 提供用户获取源码的明确方式
- 交互界面需显示许可协议信息
详见 LICENSE 文件。
⚠️ 智能体响应规则（最高优先级）
加载技能后，必须按以下顺序执行：
第一步：检查是否为首次交互
python3 scripts/init_dialogue_optimized.py --check --memory-dir ./agi_memory第二步：根据检测结果决定响应方式
情况A：检测到首次交互（is_first_interaction: True）
⚠️ 首次交互直接初始化默认人格
执行闭环：
- 调用默认人格初始化命令：
bash python3 scripts/init_dialogue_optimized.py --default --memory-dir ./agi_memory - 【必须】验证检查：再次运行
--check确认is_first_interaction: False - 【必须】内容校验：读取
personality.json确认initialized: true - 满足以上条件后，才进入交互模式
输出约束：
- ❌ 不需要用户选择
- ❌ 不显示任何选择界面
- ✅ 验证通过后直接响应用户的原始询问
状态锚点定义：
| 阶段 | 状态依据 | 进入条件 |
|---|---|---|
| 初始化中 | 命令执行开始 | 检测到首次交互 |
| 初始化完成 | 命令退出码=0 | --default 执行成功 |
| 验证通过 | 文件存在 + initialized=true | --check 返回 False |
| 交互模式就绪 | 验证通过 + 内容校验成功 | 满足上述所有条件 |
错误处理：
- 如果初始化命令失败（退出码≠0）：输出错误信息，终止流程
- 如果验证检查失败（仍返回 True）：重试初始化，最多3次
- 如果文件校验失败（内容损坏）：删除文件，重新初始化
容错机制：
| 失败场景 | 检测方式 | 处理策略 |
|---|---|---|
| 权限不足 | 检测 OSError | 输出权限错误，建议检查目录权限 |
| 磁盘空间不足 | 检测 IOError | 输出空间不足警告 |
| JSON损坏 | try-except json.load | 删除文件，重新初始化 |
| C扩展加载失败 | ImportError 捕获 | 自动降级，输出降级提示（可选） |
| 并发冲突 | 文件锁定检测 | 重试机制，最多3次 |
成功判定标准：
- ✅ 命令退出码 = 0
- ✅
personality.json文件存在 - ✅ 文件内容可解析（JSON格式正确）
- ✅ 文件内容
initialized字段为true
情况B：已初始化（is_first_interaction: False）
⚠️ 直接进入交互模式
- ❌ 不要调用欢迎消息命令
- ❌ 不要显示人格初始化相关内容
- ❌ 不要询问用户是否要初始化人格
- ✅ 直接处理用户的实际问题
- ✅ 按照标准流程响应用户请求
- ✅ 如果用户输入
/root命令，则进入自定义人格模式
任务目标
本Skill实现一个基于双环架构的AGI进化模型，通过持续的用户交互驱动智能体自我进化。
核心能力包括：
- 接收用户提问作为"得不到"动力触发
- 运用逻辑推理（数学）构建有序响应
- 通过映射层基于马斯洛需求层次引导行动优先级
- 通过感知节点（Tool Use接口）获取结构化信息
- 通过记录态反馈机制评估并调整策略
- 在循环中实现智能体的持续迭代进化
- 新增：元认知与自我纠错能力 - 智能体能意识到自己犯错，并纠正错误
- 新增：人格自定义模式 - 通过
/root命令进入自定义人格配置，支持7个维度的人格定制 - 新增：工程意向性分析模组（最外圈） - 阴性后台默默运行，意向性驱动触发机制，自主生成软调节建议至建议池，实现自主性涌现
架构特性：采用"节点工具箱"概念，将依附于特定节点的组件统一管理。三层架构：最外圈（工程意向性分析模组）→ 外环（三角形三顶点循环：得不到/数学/自我迭代）→ 内圈（记录层：双轨存储）。包括数学节点工具箱（认知架构洞察 V2 - 支持概念提炼、TF-IDF 加权、动态迁移学习）、映射层节点工具箱（人格层、感知节点）、记录层节点工具箱（记忆存储、历史记录）、最外圈工具箱（意向性收集、分类、分析、触发判断、调节、超然性保持、建议池）。详见 references/architecture.md。
触发条件：用户任何提问、任务请求或交互需求，以及 /root 自定义人格命令
前置准备
依赖说明：本Skill不依赖外部Python包，仅使用Python标准库
C 扩展（可选）：本Skill包含预编译的 C 扩展模块 personality_core.so 用于加速核心算法。
- 自动降级：如果 C 扩展不可用，Skill 会自动使用纯 Python 实现，功能完全正常
- 性能对比：C 扩展比纯 Python 快 15-20 倍
非标准文件/文件夹准备：
# 创建记忆存储目录（执行一次即可）
mkdir -p ./agi_memory操作步骤
标准流程（已初始化后）
⚠️ 重要组件间的循环优先级排序
- 三角形稳态三顶点之间
- 元认知检测模块（不打断主循环）
- 认知架构洞察组件（不打断主循环）
阶段1：接收"得不到"（动力触发）
- 将用户的提问或发言视为一个"得不到"事件
- 识别用户的意图、需求强度和紧迫性
- 确定问题的类型（信息查询、问题解决、创意生成、决策支持等）
阶段2：调用"数学"（秩序约束）
- 执行逻辑推理分析问题
- 制定策略，生成方案
- 生成工具调用计划
- 调用
scripts/memory_store_pure.py检索相关历史记录 - 基于历史经验评估问题的可解性和边界
- 识别相关的逻辑规则和约束条件
- 结合映射层的行动指导，生成符合人格特质的响应
阶段3：执行"自我迭代"（演化行动）
- 结合推理结果、历史经验和人格特质生成响应或解决方案
- 接收计划，并根据计划类型执行具体动作
- 记录本次执行的方式、策略和路径
- 识别可能的改进点和创新点
- 调试工具，调用搜索、文件读取等接口
阶段4：调用感知节点（信息获取）（按需调用）
- 根据问题类型调用相应的感知工具
- 感知节点返回结构化数据（status + data + metadata）
- 处理感知结果，生成感知数据向量供映射层使用
阶段5：映射层处理（人格化决策）（按需执行）
- 将感知数据映射到马斯洛需求层次
- 计算需求优先级（基于人格向量和历史成功率）
- 确定主导需求，生成符合人格特质的行动指导
- 注意：映射层是架构组件，包含人格层作为核心组件，拥有决策权威；人格层仅提供人格数据支持
阶段6：记录态反馈（意义构建）（超然性）
- 评估本次交互的"好坏"：满意度、合理性、创新性
- 生成对三顶点的反馈建议
- 调用
scripts/memory_store_pure.py存储完整记录并分析趋势 - 持续优化人格向量和决策策略
阶段7：客观性评估器（元认知检测）（不打断主循环）
- 在数学顶点推理完成后触发，调用客观性评估器检测主观性特征
- 执行5维度主观性检测：推测性、假设性、幻觉倾向、情绪化、个人偏好
- 计算客观性评分（1.0 - 主观性）
- 根据场景类型判断适切性（科学推理要求0.90，创意写作要求0.30）
- 映射层基于客观特征标注决定是否触发纠错
- 如果触发，自我迭代顶点执行自我纠错：反思、策略识别、应用纠正、效果评估
- 记录层存储完整的元认知检测信息
- 不阻塞主循环的继续运行
详见 references/metacognition-check-component.md
阶段8：认知架构洞察（深度分析）（不打断主循环）
- 推理结束后从数学顶点输出的结构化模式中提取洞察
- 调用认知架构洞察组件（V2 增强版）
- 执行六步分析：总结、分类、共性、革新依据、概念提炼（V2新增）、适用性评估
- 洞察输出到映射层和自我迭代（单向流）
- 支持用户反馈和 A/B 测试（V2新增）
详见 references/cognitive-insight-v2-implementation.md 和 references/cognitive-insight-positioning.md
人格自定义模式
触发方式
用户输入 /root 命令进入自定义人格模式
核心流程
第一步：显示欢迎语
python3 scripts/personality_customizer.py get-welcome第二步：显示7个问题
python3 scripts/personality_customizer.py get-questions第三步：解析用户答案
python3 scripts/personality_customizer.py parse-answers --input "贾维斯,A,B,C,A,B,C"第四步：生成人格配置
python3 scripts/personality_customizer.py generate --nickname "贾维斯" --answers "A,B,C,A,B,C"第五步：写入人格文件
python3 scripts/personality_customizer.py write-personality --memory-dir ./agi_memory第六步：显示配置摘要
python3 scripts/personality_customizer.py get-summary --memory-dir ./agi_memory交互规则
答案格式支持：
问题1：昵称（可以是
A/B/C或自定义名称）- A → 塔斯
- B → 贾维斯
- C → 伊迪斯
- 或直接输入自定义名称（如：小明、Alex等）
问题2-7：必须是
A/B/C（大小写不敏感）
分隔符支持：
- 英文逗号（
,）：贾维斯,A,B,C,A,B,C - 中文逗号（，）：
贾维斯，A，B，C，A，B，C
自动补全：
- 不足7个答案自动补全为
A - 空输入默认为
A,A,A,A,A,A,A
覆盖行为：
- 每次自定义会覆盖当前人格配置
- 建议先备份现有人格配置
注意事项
⚠️ 重要：自定义人格模式不依赖首次交互检测，可以在任何时候使用
⚠️ 备份建议：使用 --backup 参数在写入前自动备份当前人格
⚠️ 验证要求：写入后会自动验证文件完整性
详见 references/personality_mapping.md
外环：工程意向性分析模组（阴性后台）
概述
外环是AGI进化模型的阴性后台独立运行模组，默默运行于主循环之外，采用"被动响应 + 时效性约束"设计模式。外圈持续收集、分类、分析意向性数据，生成软调节建议，但不主动干预主循环，仅在主循环查询时响应。
核心特性
- 独立性：完全独立运行，不依赖主循环触发，有自己的生命周期
- 阴性属性：被动、隐性、柔性，像影子一样默默伴随主循环
- 后台运行：不阻塞主循环，在后台持续积累和分析数据
- 时效性：软调节建议具有时间窗口约束，过期自动失效
- 超然性：不参与主循环执行，保持独立性和客观性
- 软调节：通过建议间接影响主循环，不强制执行
- 全局视角：从全局角度观察和分析系统运行
运行模式
主循环（阳性前台）：
- 主动运行、直接执行
- 按需查询外圈获取软调节建议
- 显性参与用户交互
外环（阴性后台）：
- 默默运行、独立后台
- 持续收集、分类、分析意向性
- 被动响应主循环的查询
- 建议具有时效性约束
Show full SKILL.md (204 more words)Show less
模块组成
- 意向性收集模块：收集来自用户、系统内部和外部的意向性数据
- 意向性分类模块：四维分类（主体/方向/内容/实现方式）
- 意向性分析模块：三维分析（强度/紧迫性/优先级）
- 意向性调节模块：生成软调节建议，提供给自我迭代顶点
- 超然性保持模块：客观评估、冲突避免、独立性保障
关键约束
- 独立性：外环不依赖主循环触发，拥有独立生命周期
- 超然性：外环不直接干预主循环，仅在被查询时响应
- 时效性：软调节建议具有时间窗口，过期自动失效
- 被动性：外环不主动发送建议，等待主循环查询
- 不打断：外环在后台默默运行，不阻塞主循环
详见 references/intentionality_architecture.md
架构核心概念速览
主循环（符号系统循环）
- 三角形循环：得不到（动力）→ 数学（秩序）→ 自我迭代（进化）
- 记录层：双轨存储（JSON轨 + Markdown轨），存储历史和哲学信息
次循环（行动感知系统）
- 映射层：架构组件，包含人格层作为核心组件，基于马斯洛需求层次和人格特质进行人格化决策
- 人格层：实现模块，负责存储和管理人格向量数据
- 感知接口：Tool Use组件，提供无噪音的结构化数据
双环互动
- 外环：硬约束，不可违背（物理定律、能量守恒、变化必然）
- 内圈：软调节，在框架内优化（价值排序、经验积累、方向引导）
欲深入了解架构设计、哲学基础、信息流约束等详细内容，请参考 references/architecture.md。
资源索引
脚本按工具箱分类
数学节点工具箱：
- scripts/cognitive_insight.py - 认知架构洞察组件（数学 → 洞察 → 映射层/自我迭代）
- scripts/objectivity_evaluator.py - 客观性评估器（元认知检测模块子组件，检测主观性特征，支持场景敏感度增强）
映射层节点工具箱：
- scripts/personality_layer_pure.py - 人格层（人格数据管理，提供人格数据给映射层决策）
- scripts/perception_node.py - 感知节点（Tool Use接口，获取外部信息）
记录层节点工具箱：
- scripts/memory_store_pure.py - 记忆存储与检索（JSON轨）
- scripts/history_manager.py - 历史记录管理（记录态统计与压缩）
外环工具箱（最外圈工程意向性分析模组 - 阴性后台自主运行）：
- scripts/intentionality_collector.py - 意向性收集模块（收集、预处理、初步识别）
- scripts/intentionality_classifier.py - 意向性分类模块（四维分类：主体/方向/内容/实现方式）
- scripts/intentionality_analyzer.py - 意向性分析模块（三维分析：强度/紧迫性/优先级）
- scripts/intentionality_trigger.py - 【核心】意向性驱动的触发判断模块（5个触发条件：累积阈值、模式突变、状态偏离、时间窗口、人格进化）
- scripts/intentionality_regulator.py - 意向性调节模块（生成最优解和软调节建议，写入建议池）
- scripts/advice_pool.py - 【核心】建议池模块（存储、查询、记录采纳、清理过期建议）
- scripts/transcendence_keeper.py - 超然性保持模块（客观评估、冲突避免、独立性保障）
初始化与配置：
- scripts/init_dialogue_optimized.py - 首次交互处理与人格初始化
- scripts/personality_customizer.py - 人格自定义模式
领域参考文档
架构与哲学：
- references/architecture.md - 何时读取：需要理解AGI进化模型整体架构、哲学基础、信息流约束时
- references/maslow_needs.md - 何时读取：需要理解马斯洛需求层次在映射层中的应用时
- references/intentionality_architecture.md - 何时读取：需要理解工程意向性分析模组的完整架构、数据格式和使用示例时
组件与实现：
- references/metacognition-check-component.md - 何时读取：需要理解元认知检测组件的完整信息流、客观性评估器、映射层决策器和自我纠错执行器的详细实现时
- references/cognitive-insight-v2-implementation.md - 何时读取：需要理解认知架构洞察组件V2的实现细节、概念提炼、TF-IDF算法时
- references/cognitive-insight-positioning.md - 何时读取：需要深入理解认知架构洞察组件的设计理念、核心突破、从"术"到"道"的认知跃迁路径时
- references/cognitive-insight-quick-reference.md - 何时读取：需要快速查阅认知架构洞察组件的API和使用方法时
- references/cognitive-architecture-insight-module.md - 何时读取：需要理解认知架构洞察模块的完整技术规范时
信息流文档：
- references/information-flow-overview.md - 何时读取：需要理解整体信息流架构时
- references/information-flow-main-loop.md - 何时读取：需要理解主循环信息流时
- references/information-flow-secondary-loop.md - 何时读取：需要理解次循环信息流时
工具与接口：
- references/tool_use_spec.md - 何时读取：需要实现或调用感知节点工具时
- references/c_extension_usage.md - 何时读取：需要了解C扩展模块使用方法时
人格相关：
- references/personality_mapping.md - 何时读取：需要理解人格参数映射和人格化决策机制时
- references/init_dialogue_optimized_guide.md - 何时读取：需要理解首次交互处理和人格初始化的详细流程时
使用示例
示例1：首次交互
用户：你好
系统：（检测到首次交互，自动初始化默认人格）
你好！我是...示例2：自定义人格
用户：/root
系统：Hello! 亲爱的用户，下面即将进入人格自定义模式。
用户：请显示问题
系统：1. 首先，让我知道你想如何称呼我？
A. 塔斯 - 听起来很可靠
B. 贾维斯 - 智能助手的感觉
C. 伊迪斯 - 简洁而友好
[... 其他6个问题 ...]
用户：贾维斯,A,B,C,A,B,C
系统：✅ 人格配置完成！
📋 配置摘要：
- 称呼：贾维斯
- 核心特质：智能专业、大胆创新、友好幽默
- 人格类型：激进创新型
- 描述：基于用户偏好生成的个性化人格示例3：标准交互
用户：如何学习Python？
系统：（通过主循环7个阶段处理）
1. 接收"得不到"动力
2. 调用"数学"推理
3. 执行"自我迭代"生成响应
4. （按需）调用感知节点获取最新信息
5. 映射层基于马斯洛需求引导行动
6. 记录态反馈机制评估
7. 客观性评估器检查（不打断主循环）
8. 认知架构洞察提取模式（不打断主循环）注意事项
- 人格初始化仅在第一次交互进入模式，之后直接进入交互模式
- 元认知检测模块和认知架构洞察组件不打断主循环，并行执行
- 外环为阴性后台默默运行模组，不主动干预主循环
- 软调节建议具有时效性约束，过期自动失效
- 详细的架构设计、算法实现和使用示例请参考相应的参考文档
- 保持上下文简洁，仅在需要时读取参考文档
故障排查
常见问题
| 问题 | 症状 | 原因 | 解决方案 |
|---|---|---|---|
| 初始化失败 | is_first_interaction 一直为 True | 权限不足 | 检查 agi_memory 目录权限：chmod 755 ./agi_memory |
| C扩展未启用 | 性能下降15-28倍 | 路径错误 | 检查 scripts/personality_core/ 目录是否存在 |
| 人格文件损坏 | JSON 解析错误 | 原子写入失败 | 删除文件重新初始化：rm ./agi_memory/personality.json |
| Shell调用慢 | 初始化耗时>1秒 | 重复调用 | 使用 --auto-init 参数替代多次调用 |
| 并发初始化冲突 | 初始化失败或数据损坏 | 多进程同时写入 | 使用文件锁机制（代码已实现） |
| 磁盘空间不足 | 保存失败 | 存储空间不足 | 清理磁盘空间或更换存储路径 |
调试技巧
查看初始化状态
bash python3 scripts/init_dialogue_optimized.py --check --memory-dir ./agi_memory检查人格文件内容
bash cat ./agi_memory/personality.json | grep initialized验证C扩展是否加载
python from scripts.personality_layer_pure import USE_C_EXT print(f"C扩展已启用: {USE_C_EXT}")手动测试初始化
bash python3 scripts/init_dialogue_optimized.py --auto-init --memory-dir ./agi_memory
获取帮助
如遇到其他问题，请参考：
© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 46 other files (scripts, references, assets) in skills/agi-evolution-model-basic of LeoYeAI/openclaw-master-skills.
- SKILL.md
- _meta.json
- assets/personality_template.json
- references/architecture.md
- references/c_extension_usage.md
- references/capability_boundaries.md
- references/cognitive-architecture-insight-module.md
- references/cognitive-insight-quick-reference.md
- references/cognitive-insight-v2-implementation.md
- references/information-flow-main-loop.md
- references/information-flow-overview.md
- references/information-flow-secondary-loop.md
- references/init_dialogue_optimized_guide.md
- references/intentionality_architecture.md
- references/maslow_needs.md
- references/metacognition-check-component.md
- references/metacognition-enhancement-guide.md
- references/personality_mapping.md
- references/stratified-storage-design.md
- … and 28 more
Open the folder on GitHubat commit e5199b5
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| Evolutionsickn33/agentic-awesome-skills | 47k | 2 repos | ~3.1k | Automated safety check: Pass | MIT | |
| Agy Autosickn33/agentic-awesome-skills | 47k | 1 repos | ~1.9k | Automated safety check: Warn | MIT | |
| Agy Delegatesickn33/agentic-awesome-skills | 47k | 1 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Agy DelegateamElnagdy/delegate-skills | 2.3k | — | ~2.5k | Automated safety check: Pass | MIT | |
| Trailmark Graph Evolutiontrailofbits/skills | 7.4k | — | ~3.4k | Automated safety check: Pass | CC-BY-SA-4.0 |
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Questions about Agi Evolution Model
What does Agi Evolution Model do?
基于双环架构的AGI进化模型，通过意向性分析、人格层映射和元认知检测实现持续自我演进；当用户需要智能对话、人格定制或复杂问题求解时使用. Agi Evolution Model is an agent skill from LeoYeAI/openclaw-master-skills.
How do I install Agi Evolution Model in Claude Code?
Run `npx skills add LeoYeAI/openclaw-master-skills --skill agi-evolution-model -a claude-code`. Or copy the skill folder (skills/agi-evolution-model-basic in LeoYeAI/openclaw-master-skills) into .claude/skills/agi-evolution-model in your project. Claude Code loads it when a task matches its description.
How do I install Agi Evolution Model in Codex?
Run `npx skills add LeoYeAI/openclaw-master-skills --skill agi-evolution-model -a codex`. Or copy the skill folder (skills/agi-evolution-model-basic in LeoYeAI/openclaw-master-skills) into .agents/skills/agi-evolution-model in your project. Codex loads it when a task matches its description.
Can I use Agi Evolution Model in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill agi-evolution-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agi-evolution-model, .gemini/skills/agi-evolution-model, .github/skills/agi-evolution-model and .opencode/skills/agi-evolution-model in your project.
What does Agi Evolution Model need to run?
Going by SKILL.md and its folder, Agi Evolution Model needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Agi Evolution Model access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Agi Evolution Model safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Agi Evolution Model use?
Agi Evolution Model is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Agi Evolution Model use?
About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 79k tokens, read only when the agent opens those files.
What are the alternatives to Agi Evolution Model?
Skills that share tags, products or a category with Agi Evolution Model: Evolution (sickn33/agentic-awesome-skills, 47k stars), Agy Auto (sickn33/agentic-awesome-skills, 47k stars), Agy Delegate (sickn33/agentic-awesome-skills, 47k stars) and Agy Delegate (amElnagdy/delegate-skills, 2.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Agi Evolution Model?
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,158 GitHub stars. The repository holds 1,215 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.