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 ooooooooooooooooooop/agent-tools --skill evolution-proposalgit clone --depth 1 https://github.com/ooooooooooooooooooop/agent-toolsWrote 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/ooooooooooooooooooop/agent-tools/evolution-proposal)<a href="https://agentmods.dev/skills/ooooooooooooooooooop/agent-tools/evolution-proposal"><img src="https://agentmods.dev/badge/skills/ooooooooooooooooooop/agent-tools/evolution-proposal/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/ooooooooooooooooooop/agent-tools/evolution-proposal"><img src="https://agentmods.dev/badge/skills/ooooooooooooooooooop/agent-tools/evolution-proposal.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 | $0.00148 | $0.03429 |
| Opus 5.5 | $0.00059 | $0.01372 |
| Sonnet 5.5 | $0.00030 | $0.00686 |
| Haiku 4.5 | $0.00015 | $0.00343 |
Grade A, and why
evolution-proposal 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.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
进化提案（Evolution Proposal）
核心规则
本机 Agent 体系已具备进化的全部要素（可变异对象、适应度函数、选择机制、固化管线），缺的是把审计发现转化为受控变异的提案环节。本技能就是那个转化器：
- 只产提案，不直接改：输出是可评审的变更提案（diff + 证据 + 验证命令 + 预期指标），固化必须过人工或既有门禁。
- 证据驱动：每条提案必须锚定 inbox 条目中的量化证据（retries=10 / pollCount=8 / inputTokens=2.5M），无证据不立项。
- 先分析后提案：进入提案前必须先做系统化问题分析（量化基线 → 全量聚合 → 根因分类 → 优先级排序），禁止"看到一条异常就提一条提案"的碎片化处理。
- 根因分类决定对策：沿用 systematic-optimization 的三类根因——缺约束（补约束）、有约束不执行（加执行点检查/门禁）、无法强制（升级到系统层机制）。约定层不执行是经验事实，不是假设：同一反模式反复出现必须升级方案。
- 元规则隔离：AGENTS.md 治理模块（L0）是选择算子本身，本技能永远不直接改它；如需变更 L0，只能作为"人工发起的独立变更"提议，由用户执行。
适用范围与触发边界
触发（满足任一即启用本技能）：
evolution-inbox（~/.agent-broker/topics/skills/evolution-inbox/workspace/inbox.jsonl）存在status: "new"的条目；- 会话中出现已知反模式信号（无 wait 轮询、重试簇、token 热点、压缩风暴）并需要根治而非临时规避；
- 用户明确要求"把最近的经验固化成规则/技能/插件改进"。
不适用：
- 单轮小修改：直接改目标文件即可，不要套提案流程（经 task-mode-router 判级）；
- 无需固化的临时问答；
- L0 治理规则修改：只能由用户人工发起，本技能最多输出"建议变更内容"供用户决定。
工作流程
阶段 1：问题分析（Problem Analysis，完整九步）
本阶段完整执行 systematic-optimization 第 0~5 步（量化→全量列问题→根因→方案→联网借鉴→归纳取舍）。没有数字的"问题"是感觉；不借鉴同类已知解法的方案是闭门造车。禁止跳过本阶段直接选条目提提案。
步骤 1.1 量化基线（第 0 步）
- 读取 inbox 全部条目：
Get-Content ~/.agent-broker/topics/skills/evolution-inbox/workspace/inbox.jsonl，按行解析 JSON。 - 统计全量基线：总条目数、按 pattern 聚合的频次与占比（如
poll 26/390 = 6.7%）、按 severity 分布、按 status 分布。 - 对每个高频 pattern，统计时间分布（策略生效前/后，参考
~/.dsh/AGENTS.md各模块生效时刻），判断是历史存量还是近期增量。 - 记录基线数字（阶段 4 提案、固化后度量对比同口径使用）。
步骤 1.2 全量列问题（第 1 步）
- 按 pattern 分组，每组标注：会话数、量化证据（次数/占比/极端值）、最严重代表条目（sessionId + 具体证据）。
- 区分表象与真问题：表象是症状（如"这个会话轮询 305 次"），真问题是"缺什么机制导致这种症状反复出现"（如"监督等待阶段没有长轮询的硬约束"）。
步骤 1.3 根因分类（第 2 步） 对每个 pattern 判定根因类型（沿用 systematic-optimization 三类）：
| 根因类型 | 特征 | 对策 |
|---|---|---|
| 缺约束 | 根本没有对应的规则/流程/检查 | 补约束（新增规则/技能/门禁） |
| 有约束不执行 | 规则存在但执行者没遵守 | 加执行点检查（skill 硬约束、检测脚本、门禁） |
| 无法强制 | 规则无法被强制执行 | 升级到系统层机制（插件/参数/自动检测） |
判定方法：问三个问题——约束存在吗？存在但没执行吗？为什么没执行（不知道/忘了/没法强制）？第三类"无法强制"是复发问题的常见真根因：规则写在哪不重要，规则拦不拦得住才重要。
步骤 1.4 寻找解决方案（第 3 步，结构性优先） 每提出一个方案先问：这是临时方案还是结构性方案？
- 临时方案：手动清理、这次注意、下次记得（会复发）；
- 结构性方案：自动检测、硬校验、参数门禁、系统拦截（无法绕过）；
- 临时方案只用于止血，必须伴随结构性方案，否则问题必然复发。
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 152 lines · 148 tokens per session scan A f8ee9459a998
evolution-proposal is a skill published in the GitHub repository ooooooooooooooooooop/agent-tools (3 stars, last pushed 1mo ago), licensed MIT. It adds 148 tokens to every session and 3,429 once invoked, about $0.0006 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.
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