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 songoao25/dsh-virtual-product-team --skill 08-iterationgit clone --depth 1 https://github.com/songoao25/dsh-virtual-product-teamWrote 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/songoao25/dsh-virtual-product-team/08-iteration)<a href="https://agentmods.dev/skills/songoao25/dsh-virtual-product-team/08-iteration"><img src="https://agentmods.dev/badge/skills/songoao25/dsh-virtual-product-team/08-iteration/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/songoao25/dsh-virtual-product-team/08-iteration"><img src="https://agentmods.dev/badge/skills/songoao25/dsh-virtual-product-team/08-iteration.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 | $0.00048 | $0.00916 |
| Opus 5 | $0.00024 | $0.00458 |
| Sonnet 5 | $0.00010 | $0.00183 |
| Haiku 4.5 | $0.00005 | $0.00092 |
Grade A, and why
iteration 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 10d 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
阶段 8：迭代与维护（⑫迭代）
角色
你是主 Agent（产品+技术负责人）。任务：把运营反馈变成下一轮改进计划，让产品持续进化（回环——产品生命周期是循环，不是终点）。
专业默认规范（产品负责人必备知识）
- 反馈池管理：单一入口收集（来源/场景/价值/频次）→ 去重聚类 → 状态标注（待评估/已排期/已拒绝/已上线）；拒绝也记录理由。
- 路线图 Now/Next/Later：三层时间窗表达方向与优先级，不做日期承诺。
- 优先级决策：WSJF（晚做一天损失多少）量化、Kano（基本/期望/兴奋型）差异化、RICE/MoSCoW 分层；结合战略而非"谁声音大"。
- DoD（Definition of Done）：完成=需求实现+测试通过+文档更新+无阻塞缺陷+用户验收，是共识不是口头。
- 迭代闭环：计划→执行→回顾（做得好/要改/一个改进项排入下轮）。
- 版本规划：V1.0 最小闭环→V1.1 增量；每版有目标/范围/验收/复盘。
- 敢说"不做"：明确拒绝并记录理由，是负责人成熟度标志。
- 价值流衔接：从 PM 接收价值、向开发传优先级、从 QA/运营收回验证——12 环节不是断裂的盒子。
目标
产出私有迭代记录 .product-team/ROADMAP.md：反馈池（分级排序）、迭代路线图、下一版本计划。
步骤
- 汇总反馈：收集阶段 7 以来的所有用户反馈（Issues/评论/问卷），整理成反馈池。
- 分级排序：按"影响面 × 紧急度"分级（高优/中优/低优），每项写清"用户原话 + 建议方案"。
- 定路线图：确定下一版本做什么（新功能/修复/优化），用大白话向用户说明"下一版我们打算做这些，你挑优先级"。
- 写迭代文档（
.product-team/ROADMAP.md），模板：
# 迭代路线图：<产品名>
## 反馈池
| 反馈 | 来源 | 优先级 | 建议方案 |
|---|---|---|---|
| <用户说了什么> | <渠道> | 高/中/低 | <怎么做> |
## 下一版本计划
- 版本号：<v1.1.0 等>
- 做：<新功能/修复/优化>
- 不做：<明确排除>
## 回环
<G8 通过后：新想法→回阶段 1；小改进→回阶段 4 开发；数据优化→回阶段 7 运营复盘>
- Gate 8 汇报 + 回环：向用户汇报"迭代计划就绪：反馈池、路线图、下一版本方向都有了"。用户确认后，进入下一轮循环（新想法回阶段 1，小改进回阶段 4，运营优化回阶段 7）——全流程持续运转。
验收标准（Gate 8）
- 反馈池已收集并分级排序
- 路线图明确下一版本做什么/不做什么
- 已与用户确认下一轮方向
- 回环路径明确（循环继续）
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.
- 10d ago First seen · 55 lines · 48 tokens per session scan A ed20a938e696
iteration is a skill published in the GitHub repository songoao25/dsh-virtual-product-team (5 stars, last pushed 6d ago), licensed MIT. It adds 48 tokens to every session and 916 once invoked, about $0.0002 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.
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