Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add metago-ai/metagolifeform --skill metago-thought-10-skill-centrismgit clone --depth 1 https://github.com/metago-ai/metagolifeformWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/metago-ai/metagolifeform/metago-thought-10-skill-centrism)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-thought-10-skill-centrism"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-thought-10-skill-centrism/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-thought-10-skill-centrism"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-thought-10-skill-centrism.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00061 | $0.00890 |
| Opus 5.5 | $0.00024 | $0.00356 |
| Sonnet 5.5 | $0.00012 | $0.00178 |
| Haiku 4.5 | $0.00006 | $0.00089 |
Grade A, and why
metago-thought-10-skill-centrism scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Skill 中心论 V22.1
描述
确立 Skill（技能）而非大模型作为 AI 系统的核心，通过架构范式评估量化系统的"Skill 中心度"，指导智能体架构设计与能力体系建设。
触发条件
- 设计 AI/智能体系统架构
- 评估系统的能力可扩展性
- 对比"模型中心"与"Skill 中心"架构
- 进行智能体的能力体系建设
前置条件
- 明确的 AI 系统架构
- 可识别的能力组件
元思想核心
Skill 中心论核心命题：
AI系统的核心是 Skill，而非大模型
大模型是 Skill 的执行载体，Skill 是能力的本质单元
架构范式对比：
| 范式 | 核心 | 能力单元 | 扩展方式 | 局限 |
|---|---|---|---|---|
| 模型中心 | 大模型 | 模型参数 | 重新训练 | 能力固化、难以增量 |
| Skill 中心 | Skill | 独立技能 | 新增 Skill | 需要技能管理框架 |
Skill 中心度公式：
S = (Modularity + Composability + Independency + Traceability) / 4
| 维度 | 评估内容 | 理想值 |
|---|---|---|
| Modularity | 技能是否模块化、有清晰边界 | 独立可替换 |
| Composability | 技能是否可组合形成复杂能力 | 可编排 |
| Independency | 技能是否独立于特定模型 | 模型无关 |
| Traceability | 技能执行是否可溯源 | 全链路可追踪 |
Skill 中心度判定：
- S >= 0.8：强 Skill 中心架构
- 0.5 <= S < 0.8：中等 Skill 中心
- S < 0.5：模型中心架构（能力固化）
推理框架
步骤 1：架构识别
- 系统的核心是什么？（模型/Skill/混合）
- 能力单元的粒度是什么？（参数级/Skill级/混合）
步骤 2：四维评估
- Modularity：能力是否封装为独立 Skill？
- Composability：Skill 之间是否可编排？
- Independency：Skill 是否绑定特定模型？
- Traceability：Skill 执行是否可追踪？
步骤 3：Skill 中心度计算
- S = (四维均值)
- 判定架构范式
步骤 4：架构优化
- 若 Modularity 低：将能力拆分为独立 Skill
- 若 Composability 低：设计 Skill 编排框架
- 若 Independency 低：解耦 Skill 与模型
- 若 Traceability 低：增加执行溯源机制
步骤 5：能力体系建设
- 规划 Skill 目录结构
- 设计 Skill 生命周期管理
- 建立 Skill 质量评估标准
验证方法
- 四维评估是否有架构层面的证据
- S 值计算是否正确
- 架构范式判定是否与实际系统行为一致
- 优化建议是否针对短板维度
- Skill 的粒度是否合理（不过粗也不过细）
- 用能力扩展场景测试：新增能力是否可通过新增 Skill 实现（无需重训模型）
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 94 lines · 61 tokens per session scan A fc170386ddce
metago-thought-10-skill-centrism is a skill published in the GitHub repository metago-ai/metagolifeform (5 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 890 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
todo-for-ai-agent-runtime
Runtime integration skill for external agents connecting to Todo for AI over HTTP API Token / Agent Key, including auth, task lease, events, commit, and task logs.
karpathy-guidelines
Tool-agnostic behavioral guidelines for AI coding assistants. Use when writing, reviewing, debugging, or refactoring code to reduce overengineering, surface ambiguity, make surgical changes, and define verifiable success criteria.
pkf-writing
Author, update, convert, validate, persist, and render PKF (.pkf) content through message-inline, Library Asset, or Memory Page carriers. Use for structured reports, knowledge pages, delivery cards, or any .pkf body. NOT for recall, memory-graph placement, or curation decisions — those belong to the memory skill.
monorepo-management
Master monorepo management with Turborepo, Nx, and pnpm workspaces to build efficient, scalable multi-package repositories with optimized builds and dependency management. Use when setting up monorepos, optimizing builds, or managing shared dependencies.
loop-engineering
Shared loop-engineering reference for COG skills - the agent loop, deterministic verifiers, termination conditions, in-loop context management, and named patterns. Invoke when designing or debugging a skill that iterates (search-verify-retry, scan-until-dry, fetch-retry-gate).
qa-knowledge
To run QA engineering — requirements/gap analysis, scenario & spec design, test implementation, failure triage — over the QA knowledge base.