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 Lion-1209/coderio --skill task-breakdowngit clone --depth 1 https://github.com/Lion-1209/coderioWrote 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/lion-1209/coderio/task-breakdown)<a href="https://agentmods.dev/skills/lion-1209/coderio/task-breakdown"><img src="https://agentmods.dev/badge/skills/lion-1209/coderio/task-breakdown/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/lion-1209/coderio/task-breakdown"><img src="https://agentmods.dev/badge/skills/lion-1209/coderio/task-breakdown.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 | $0.00018 | $0.03248 |
| Opus 5.5 | $0.00007 | $0.01299 |
| Sonnet 5.5 | $0.00004 | $0.00650 |
| Haiku 4.5 | $0.00002 | $0.00325 |
Grade A, and why
task-breakdown 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- task-breakdown — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Breakdown
概述
把一个需求拆成"今天就能动手、做完就能验证"的小任务。核心:每个任务是一次端到端的价值交付,而不是一层架构的横切——前者让你随时有可验证的进展,后者让你做到最后才看见东西跑起来。
何时使用
- 拿到一个需求/用户故事,要转成开发任务
- 面对一个大需求,不知从何下手
- 已有任务清单,想审查拆得对不对
- 要排开发顺序和依赖关系
不该用:需求本身已经是一个明确的小改动(直接做);纯调研性命题(用 spike 流程,见下文)。
与相邻 skill 的衔接:task-breakdown 在"写 spec → 拆任务 → 执行"流水线的中间。理想入口是拿到一份定稿的 spec(用 spec-writing 产出)——方案决策已定,拆出的任务才有稳固依据;只有模糊需求时,先澄清/写 spec 再拆,否则任务建立在假设沙地上。
核心内容
先判断任务大小
不是所有需求都该拆,也不是越细越好。拿到需求先问两个方向:
- 拆不够:它能不能在一次提交里端到端做完、且做完就能验证?能 → 别拆,直接做;不能 → 用下面的流程拆。
- 拆过细:清单里有没有一堆"半小时以内的微步骤"(如"建表"和"加字段"分开、"写接口"和"加路由"分开)?有 → 合并回可独立验证的粒度。每个任务都有管理开销(上下文切换、状态追踪),碎任务的成本会淹没价值。
过度拆分比不拆还糟。粒度的尺子:一个任务做完后,能独立验证、独立合并、独立回滚——到这个粒度就停,再大不好做、再小是浪费。
澄清未知
模糊需求直接拆,会拆出一堆基于错误假设的任务。拆之前先把"会影响切片方式的未知"问清楚,不要凭猜往下走。典型该问的:
- 范围边界:哪些在 scope 内、哪些不在?("支持邮件通知"——只发交易邮件还是也发营销?)
- 规模/量级:影响技术选型和切片顺序(日活 100 vs 100 万,缓存策略天差地别)
- 完成标准:怎样算"做完了"?(性能要求、合规要求、可观测性要求)
- 已知约束:必须用的技术、不能动的部分、截止时间
分优先级问,别一次性甩给用户。未知通常很多,一次列十几条会淹没用户。按"是否阻塞第一刀"分两类:
- 阻塞项(不答就没法切第一片)——先问。例:通知系统"发什么类型、给谁发"不答,第一刀都没法落。
- 非阻塞项(先用合理默认假设推进,遇到再问)——后问或直接用假设。例:通知系统"是否多语言"——先用"只做中文"的假设推进,等做到模板那片再确认。
问的方式:把你的假设显式写出来让用户确认("我假设 X,不对就纠正我"),而不是连环追问——前者用户一句话就能校正方向,后者消耗耐心。每条最好带一句"为什么问"(这个未知如何影响切片),并在意控量(一次别超 6-8 条);spec-writing 的"澄清问题怎么问"对此有更细的展开,可参考。
反例:用户说"做个通知系统",你直接拆成"建表/写 API/写 UI"——所有切片都建立在"通知是什么、发给谁、怎么算成功"都未定义的沙地上。
选切片方向:垂直切片,别横切
这是拆任务最关键的决策。有两种切法:
- 横切层(糟糕的默认):按架构层切——"先建数据库表 → 再写后端 API → 再写前端 → 最后联调"。直觉上像"循序渐进",实则把价值验证堆到最后:你做完前 3 个任务,什么可运行的东西都没有,直到联调那一刻才暴露所有集成错误。
- 垂直切片(推荐):按端到端的价值切——"邮件通知:从 DB 读用户 → 调邮件服务发送 → 用户能在收件箱看到"。每个切片自己走完所有层,做完就有一个可验证的、跑通的功能增量。
为什么垂直切片更优:
- 早验证:第一个切片做完就能演示,错也错得早、错得便宜。
- 早集成:集成风险被摊到每个切片,而不是堆到最后爆炸。
- 进度可见:完成 N 个切片 = N 个可演示功能,而不是"后端 80% 完成"这种无法验证的进度。
一个完整的垂直切片长这样:选一个最小但真实的数据 → 走通它的 DB schema + API + 业务逻辑 + UI + 测试。第一片可以很糙(hardcode 数据、丑 UI),但要端到端跑通。
重构/迁移场景:价值换成"风险消化",切片换成"渐进可回滚"。上面定义的切片是"新功能语言"(DB+API+UI+测试、用户可演示的价值)。但重构、迁移、框架升级、性能改造这类任务没有用户可感知的新价值——用户看不到"项目变成了 TS"或"换了状态管理库"。这时机械套用"垂直切片"会卡壳,但别因此退化成横切。
What ships with it
1 file 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 · 135 lines · 18 tokens per session scan A 3d0ea591b2ab
task-breakdown is a skill published in the GitHub repository Lion-1209/coderio (9 stars, last pushed 7d ago), licensed MIT. It adds 18 tokens to every session and 3,248 once invoked, about $0.0001 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
playwright-cli
Automates browser interactions for web testing, form filling, screenshots, and data extraction using playwright-cli. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, extract information from web pages, mock network requests, manage browser…
stuck
Diagnose frozen, stuck, or slow Qwen Code sessions on this machine. Scans for problematic processes, high CPU/memory usage, hung subprocesses, and debug logs. Use /stuck or /stuck to focus on a specific process.
structured-debugging
Hypothesis-driven debugging methodology for hard bugs. Use this skill whenever you're investigating non-trivial bugs, unexpected behavior, flaky tests, or tracing issues through complex systems. Activate proactively when debugging requires more than a quick glance — especially when the first attempt at a fix didn't…
desktop-brand-builder
Generate a branded Qwen Code desktop package from the Tauri desktop shell using a minimal brandId and logo. Use when the user wants a custom, white-label, or rebranded desktop client, installer, DMG/EXE/AppImage/deb, or one-click brand build on top of packages/desktop.
batch-api
Prepare a many-file, single-turn transform (translate, rewrite, extract) as a plan and submit it to the asynchronous, half-price DashScope Batch API; results are delivered as new files hours later. Invoke explicitly with /batch-api.
repo-hygiene
Use when the scheduled repo-hygiene workflow runs from GitHub Actions (or an operator dry-run) to scan the repository for small, certain docs/test/code hygiene issues and fix them as one batched branch.