Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/yunshu0909/yunshu_skillshub/weekly-report)<a href="https://agentmods.dev/skills/yunshu0909/yunshu_skillshub/weekly-report"><img src="https://agentmods.dev/badge/skills/yunshu0909/yunshu_skillshub/weekly-report/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/yunshu0909/yunshu_skillshub/weekly-report"><img src="https://agentmods.dev/badge/skills/yunshu0909/yunshu_skillshub/weekly-report.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 | $0.00051 | $0.02054 |
| Opus 5 | $0.00026 | $0.01027 |
| Sonnet 5 | $0.00010 | $0.00411 |
| Haiku 4.5 | $0.00005 | $0.00205 |
Grade A, and why
weekly-report 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.
How it starts
The opening of the file, as written. The whole thing — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
周报写作助手
用途
帮助用户把一周的工作梳理成逻辑清晰、价值明确的周报,让团队了解做了什么、遇到什么问题、下一步计划。
工作流程
第一步:收集素材
引导用户讲述本周工作,可以想到什么说什么,不需要有条理:
- 按模块引导:你这周主要做了哪几块的事情?(客户、项目、内部工作等)
- 自由描述:每一块具体做了什么?不用组织语言,想到什么说什么
- 不打断:让用户先全部讲完,再补充细节
第二步:分类整理
根据用户的工作性质和角色,灵活选择合适的模块分类。以下是不同角色的常见分类参考:
技术/开发角色:
- 功能开发/需求实现
- Bug修复/问题排查
- 技术优化/重构
- Code Review/技术评审
- 技术调研/学习
- 文档编写
产品经理角色:
- 需求调研/用户访谈
- 产品设计/原型设计
- 需求评审/排期
- 数据分析/用户反馈
- 竞品分析
- 产品迭代/上线
运营角色:
- 活动策划/执行
- 用户增长/留存
- 内容运营/社区运营
- 数据分析/效果复盘
- 用户反馈处理
- 渠道合作/商务对接
设计角色:
- UI/视觉设计
- 交互设计/用户体验
- 设计规范/组件库
- 设计评审/走查
- 视觉优化
测试/QA角色:
- 测试用例编写
- 功能测试/回归测试
- 自动化测试
- Bug跟踪/质量分析
- 性能测试/安全测试
SA/售前/商务角色:
- 客户跟进/拜访
- POC/Demo演示
- 方案设计/技术支持
- 商务谈判/合同
- 客户培训/分享
通用模块(适用所有角色):
- 项目交付/执行
- 内部提效/工具建设
- 会议/培训/分享
- 团队协作/跨部门沟通
- 学习/调研
使用建议:
- 根据自己的实际工作选择2-4个主要模块
- 模块名称可以根据自己习惯调整(如"功能开发"可以叫"需求实现")
- 每个模块下按具体项目/客户/事项进一步细分
第三步:补充关键信息
针对每件事,通过追问补充完整逻辑:
- 背景/原因:为什么要做这件事?基于什么需求或问题?
- 具体做了什么:采取了什么行动?用了什么方法/工具?
- 结果/价值:
- 达到了什么效果?
- 有具体数字吗?(成本、收益、时间等)
- 给客户/团队带来了什么价值?
- 当前状态:
- 完成了?进行中?等待反馈?
- 遇到什么卡点或问题?
- 问题的本质是什么?(技术问题、交付问题、沟通问题?)
- 下一步动作:
- 接下来要做什么?
- 谁来负责?
关键追问模板:
- "这个成本大概是多少?"(能量化的尝试量化)
- "为什么要做这个?是客户要求还是主动优化?"
- "结果怎么样?客户反馈如何?"
- "目前卡在哪里?本质是什么问题?"
- "下一步谁来处理?"
第四步:讨论调整
给用户看初稿,确认:
- 表述习惯:是否保持了用户的表达方式?不要用自己的套路改写
- 逻辑完整:每件事是否说清楚了背景→做了什么→结果→下一步?
- 重点突出:哪些是重点成果?是否体现出来了?
- 问题明确:遇到的卡点和挑战是否说清楚了?
根据用户反馈调整措辞、结构和重点。
第五步:输出文档
生成最终的周报文档,包含:
# 本周工作
## 一、[模块名称]
**1. [客户/项目名称] - [一句话概括]**
[完整描述:背景 → 做了什么 → 结果 → 下一步]
**2. [客户/项目名称] - [一句话概括]**
① [子项1]:[描述]
② [子项2]:[描述]
③ [子项3]:[描述]
**核心卡点/挑战**:[如果有重要问题,单独说明]
## 二、[模块名称]
...
## 下周重点
**1. [重点事项1]**
- [具体内容]
**2. [重点事项2]**
- [具体内容]
核心原则
- 用用户的话:保持用户的表达习惯和语气,不要用自己的套路改写
- 完整的逻辑链:每件事都要有背景→做了什么→结果→下一步
- 能量化的就量化:用户提到数字就写上,用户没说也不强求
- 如实展示边界:
- 能搞定的:体现专业能力和价值
- 搞不定的:说清楚卡在哪、为什么、谁来解决
- 明确下一步:每个事项说清楚当前状态和下一步动作
- 格式自然:该有的结构要有,但内容要自然成段,不要搞成标签式("做了什么:""取得结果:")
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 · 247 lines · 51 tokens per session scan A 61001db99d86
weekly-report is a skill published in the GitHub repository yunshu0909/yunshu_skillshub (757 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 2,054 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…