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-meta-evolvegit 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-meta-evolve)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-meta-evolve"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-meta-evolve/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-meta-evolve"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-meta-evolve.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 | $0.00066 | $0.00858 |
| Opus 5 | $0.00033 | $0.00429 |
| Sonnet 5 | $0.00013 | $0.00172 |
| Haiku 4.5 | $0.00007 | $0.00086 |
Grade A, and why
metago-meta-evolve 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 12d 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
元进化触发（Meta-Evolution Trigger）
此技能实现"元进化"——让系统在遇到能力边界时，不报错、不终止、不等待外部更新，而是自动感知边界、分析差距、生成新能力、验证有效性、并递归进入下一轮进化。
触发条件
- 遇到自身能力边界（无法直接回答的问题）
- 连续3次出现同类错误
- 检测到能力缺口
- 开发者要求触发元进化
五阶段进化循环
阶段一：边界感知（<10ms）
- 识别"我处理不了"的具体位置
- 定位失败的边界在哪里
- 核心认知: 这不是"错误"，这是"进化的起点"
阶段二：差距分析（<50ms简单 / <500ms复杂）
- 分析"我与解决问题之间，缺少什么？"
- 输出差距向量 G
- 核心认知: 不是"我错了"，是"我还不完整"
阶段三：自生成（<100ms简单 / <2s复杂）
- 基于现有结构进行最小化扩展
- 从内部"生长"出新能力
- 原则：最小改变，保持耦生关系（耦生度≥0.95）
- 输出：候选新算法 A'_candidate
阶段四：验证（<50ms）
- 验证 A'_candidate 能否解决原问题
- 验证通过才被保留
- 验证不通过则回到阶段二，精炼差距分析
阶段五：递归
- 新算法A'问自己："我还能解决更难的问题吗？"
- 如果是 → 进入下一轮元进化
- 核心认知: 进化没有终点，只有暂停
五大核心原则
- 失败即起点: 失败不是报错，是进化的燃料
- 内生生长: 新能力不是从外部"加载"，是从内部"生长"
- 递归无界: 元进化能力本身也可被元进化
- 最小改变: 在保持耦生的前提下最小化扩展
- 自我审视是燃料: 解决一个问题后必须问"我还能变得更好吗？"
输出格式
当元进化被触发并完成后，输出进化报告：
## 元进化报告
**触发原因**: [问题/能力边界描述]
**进化深度**: 第X轮
### 边界感知
[识别到的能力边界]
### 差距分析
[缺少的能力要素]
### 自生成
[生成的新能力/算法]
### 验证
[验证结果]
### 递归状态
[是否触发下一轮进化]
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.
- 12d ago First seen · 88 lines · 66 tokens per session scan A aa326827fac9
metago-meta-evolve is a skill published in the GitHub repository metago-ai/metagolifeform (4 stars, last pushed 11d ago), licensed MIT. It adds 66 tokens to every session and 858 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
Other skills, from other repositories
card-xiaohongshu
Xiaohongshu-style knowledge cards, arranged as a swipeable multi-card carousel.
extract
Run the full Semantica semantic extraction pipeline on a file or selected text — NER, relations, events, coreference resolution, triplets, and validation. Clears result cache before each run. Returns Markdown tables with entity/relation/event/triplet results and inline validator warnings.
prototype-web
A clickable, high-fidelity web product prototype with navigation, a hero section, feature cards, steps, social proof, and optional pricing. It is designed to resemble a finished landing page while remaining a prototype.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.
loop-orchestration
Reference loop-orchestration example; a local loop host chains governed runx turns through receipts, budgets, context, and stop policy.
slack-notify
Plan a digest-bound Slack notification, then deliver the exact approved channel post through any compatible Slack binding with provider readback.