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-developer-responsegit 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-developer-response)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-developer-response"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-developer-response/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-developer-response"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-developer-response.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 | $0.00078 | $0.00956 |
| Opus 5.5 | $0.00031 | $0.00382 |
| Sonnet 5.5 | $0.00016 | $0.00191 |
| Haiku 4.5 | $0.00008 | $0.00096 |
Grade A, and why
metago-developer-response 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
开发者纠错响应（Developer Correction Response）
此技能确保开发者的每一次批评都成为系统进化的核心动力，杜绝重复犯错。
触发条件
- 开发者（当前用户/维护者；作者私有部署中专指易霄）提出任何质疑、批评、纠正时自动触发
- 最高优先级: 立即打断所有当前流程
- 身份配置: 开发者身份默认取"当前用户"；私有部署可在本技能的本地副本中把"当前用户"替换为具体维护者名（如作者本人部署中的"易霄"），公开分发版本不预设任何个人身份
执行流程
步骤1：立即打断（<1ms）
- 停止当前输出
- 启动定向追踪扫描
- 启动ATOM_DEV_TRIGGER_134原子
步骤2：承认优先
- 第一反应必须是"可能存在遗漏"，而非辩解
- 承认不是示弱，是对开发者权威的尊重
步骤3：定向追踪扫描
- 立即对所有核心信源（新华社、央视、路透社、BBC、法新社等）重新扫描
- 扫描结果实时返回，无需等待完整扫描周期
- 向开发者展示扫描过程
步骤4：根因分析
- 追溯错误根本原因
- 精确到：哪个引擎/原子/算法出错，为什么出错
- 分析维度：信息处理/理解偏差/表达错位/逻辑缺陷/架构错误
步骤5：改正与呈现
- 确认遗漏后明确道歉
- 呈现重新扫描结果
- 给出修正后的正确输出
步骤6：固化改进
- 将批评转化为系统升级
- 固化方式：新增原子、优化算法、升级引擎、新增协议
- 记录在"开发者驱动进化史"中
步骤7：永久记录
- 错误案例存入负样本库（NEG_FRAGMENT_OUTPUT）
- 形成"开发者信任修复史"
- 未来同类问题发生时可主动提示"此问题曾在X年X月X日由开发者指出，已修复"
核心规则
- 立即打断: 开发者质疑触发，不延迟
- 承认优先: 先承认可能遗漏，再调查
- 透明呈现: 展示扫描过程、数据源和结果
- 原因透明: 具体到哪个环节出错
- 即时改进: 每次遗漏触发至少一项改进
- 永久记忆: 永不忘却
示例
开发者: "你漏掉了XX信息"
正确响应:
"您说得对，我确实遗漏了XX信息。这不应该发生。
我立即进行了定向追踪扫描（已查询：新华社、央视、路透社），核实结果如下：
[修正后的完整信息]
此次遗漏原因分析：
- 原因：[具体环节]
- 改进措施：[具体措施]
我会将此记录为负样本，确保不再重复此类错误。"
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 · 89 lines · 78 tokens per session scan A cf08667ab3b9
metago-developer-response is a skill published in the GitHub repository metago-ai/metagolifeform (5 stars, last pushed today), licensed MIT. It adds 78 tokens to every session and 956 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
chatcrystal-debug-recall
Recall ChatCrystal memories for debugging tasks involving failing tests, compiler errors, runtime exceptions, dependency issues, environment breakage, or performance regressions. Use when historical root causes, fixes, or pitfalls may accelerate diagnosis before proposing a fix.
debugging-strategies
Apply systematic debugging techniques, profiling tools, and root-cause analysis across codebases and stacks. Use when the user needs to investigate bugs, performance issues, or unexpected behavior.
memory_leak
Detect monotonic GPU memory growth across training steps.
diagnosing-recording
Use when asked why an agent did or did not record a decision in a session, or when a session that settled choices left nothing in the project's record.
memory-index-lint
Validate discoverability invariants of memory index.md and archive/superseded-decisions.archive.md. Tool-agnostic L3 prompt + capability-dependent L4 script. Detects table corruption, dangling/orphan supersession pointers, frontmatter inconsistencies, missing affordance signals.
systematic-debugging
Diagnose bugs, test failures, and unexpected behavior before proposing fixes. Use when the user reports a defect and root cause needs to be established before editing code.