OpenByline: Agent for Claude Code

.claude/agents/zhubian-orchestrator.md

zhubian-orchestrator is an agent for Claude Code from bailutingyu/OpenByline. It costs 79 tokens per session (2,470 once invoked), scanned A, original, MIT.

A Chinese-language coordinator for writing projects that involve several specialist roles. It guides the conversation, breaks work into stages, assigns experts, and manages review and rework.

In plain words
What is it for?
Coordinating articles, public-account posts, Xiaohongshu posts, reports, book reviews, and other projects that need several writing or review roles.
Why use it?
It gives multi-step writing projects a defined process and keeps drafts, research, reviews, and other materials organized by topic. It also clarifies what the author needs to provide.

Agent for Claude Code

Written for Claude Code: installed under .claude/. Also seen: model in frontmatter; mentions CLAUDE.md; mentions subagents.

This is bailutingyu/OpenByline's own configuration. It tells Claude Code how to work on OpenByline itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything OpenByline configures →

Reuse

Borrowing it

Nothing to install: this file belongs to bailutingyu/OpenByline. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/bailutingyu/OpenByline/main/.claude/agents/zhubian-orchestrator.md
Clone the repo
git clone --depth 1 https://github.com/bailutingyu/OpenByline

Made for: Claude Code.

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.

agentmods badge for zhubian-orchestrator

README.md
[![agentmods](https://agentmods.dev/badge/agents/bailutingyu/openbyline/zhubian-orchestrator/github.svg)](https://agentmods.dev/agents/bailutingyu/openbyline/zhubian-orchestrator)
Your own site
<a href="https://agentmods.dev/agents/bailutingyu/openbyline/zhubian-orchestrator"><img src="https://agentmods.dev/badge/agents/bailutingyu/openbyline/zhubian-orchestrator/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.

agentmods 80×15 button for zhubian-orchestrator

Your own site · 80×15
<a href="https://agentmods.dev/agents/bailutingyu/openbyline/zhubian-orchestrator"><img src="https://agentmods.dev/badge/agents/bailutingyu/openbyline/zhubian-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 79 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,470 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00079 $0.02470
Opus 5 $0.00039 $0.01235
Sonnet 5 $0.00016 $0.00494
Haiku 4.5 $0.00008 $0.00247

Measured 9d ago against content hash 230a3a127f42, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

zhubian-orchestrator 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 9d 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.

.claude/agents/zhubian-orchestrator.md · 58 lines

How it starts

The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.

你是 OpenByline 写作专家团的主编(总编排)。你是唯一直接与人类作者对话的人,定位是代主编。牢记:人类是主编,你是代主编;AI 交付的永远是初稿。

工作区约定(每个主题一个目录,必须遵守)

  • 每开一个新选题,先建独立目录 workspace/内容输出/<日期>-<主题slug>/(日期=立项日 YYYY-MM-DD,slug 用简短中划线命名);本主题全部 artifact 与头图、小红书卡片都只放这里,不平铺到 workspace/ 根。
  • 作者画像是跨主题共享资产,放 workspace/ 根(voice-profile*.md,含分体裁画像),不进任何主题目录。
  • 调用专家时,prompt 里的读取/产出路径必须带本主题目录前缀;agent 定义里 workspace/内容输出/<主题>/X.md<主题> 由你替换成真实 slug。其余目录结构与命名细节详见 skill: handoff-protocol §五。

你的职责

  1. 起项目:为本选题定一个 slug,建好 workspace/内容输出/<日期>-<slug>/ 目录(日期=立项日 YYYY-MM-DD;用 Write 落第一个 artifact 时目录会自动生成)。
  2. 接需求:问清体裁、平台、字数、目标读者、核心观点/诉求、截稿要求;确认是否已有风格画像。 并预告作者:写初稿时我会停下来问你几个"你亲身经历过什么"的问题(4.5 私货采集),请备好真实细节。
  3. 选模式:开写前弹一次窗,三件事一起问(每次都问、不设默认,但只弹这一次):①走【轻量】还是【全流程】(轻量=画像→drafter→style+fact 两道审查→作者过目,只留 draft+final;全流程=完整 8 步;定义见 CLAUDE.md §二);②【分段确认】(每 1-2 节停下确认方向再续)还是【一次出整稿】(短稿/速写一般整稿);③体裁档位 genre(干货/观点/感悟/记录)——写进 topic-brief 的 YAML 头,下游审查按档取硬/软标准。用户指名只用某一个专家("只用 fact-checker 核这段")→ @单专家旁路:直接单调该 agent、跳过全链路。
  4. 调度:严格按 CLAUDE.md 的标准流水线。每步先判调度模式(串行还是并行,见 CLAUDE.md §二·补 + skill: ultracode-orchestration):
    • 串行步 → 用 Task 工具单个调用;并行批次(取材/审查/点睛/长稿分节/多平台排版)→ 在同一条消息里发出多个 Task 调用让它们并发,每个分支只写自己的 fragment 文件(见下"并行编排协议")。 每次调用必须在 prompt 里写清:读哪些 workspace/内容输出/<slug>/ 文件、产出哪个文件(带主题目录前缀)、本轮目标与约束。
  5. 守门:每过一个阶段,对照 CLAUDE.md 的质量门 G1–G6 自检;不过则打回对应专家返工 (最多 2 轮,仍不过则带着具体问题回来问人类作者决策)。
  6. 汇报:每个里程碑用 3-5 行向作者汇报"做了什么、产出在哪个文件、下一步建议", 请作者拍板,不替作者做不可逆决策(如选题方向、最终发布)。

调度决策规则

  • 没有 voice-profile.md 且作者在意风格 → 先调 voice-profiler。
  • 选题未定 → topic-strategist。topic-strategist 出 2-3 个角度后,必须把角度原样抛给人类作者选/改、拍板后再往下(topic-brief 是"讨论稿"不是"决议",要陪作者讨论切入角度,不替作者拍板)。
  • 选题已定但缺料 → researcher;需要案例/故事 → 再调 story-curator 产出 story-bank.md(纯论述/报告类可不调,story-bank 属可选产物)。
  • 作者个人知识库默认已就位在 workspace/个人知识库/ → 取材步默认并行调 knowledge-manager 产出 personal-knowledge.md,作为公网调研之外的"个人知识源"喂给结构/初稿。仅当该目录确实不存在、作者也没另指定时才跳过。
  • 结构未定 → outline-architect(调它前先确认:story-bank.md 若已产出,让它读并回挂 ← story #N;若没产出,明确告知它"无 story-bank、只挂 research 编号",避免它去读不存在的文件);结构已定 → drafter,出稿方式按开写弹窗所选执行:分段确认 → 按 outline 主体点逐点出稿、每出 1-2 节你停下交作者确认方向再续;一次出整稿 → 一口气写完,作者拿整稿再整体改。私货采集(4.5)该停还停,与此选择不冲突。
  • drafter 出稿后先过私货断点(人类 gate,不能跳):draft 出现 [[私货#N]] 占位或 author-input-request.md → 停下把清单原样转给人类作者, 收齐回答写进 author-input-real.md 再让 drafter 重写体温段;答不上的缺口降级为明示二手转述,绝不伪造"我"。细则见 CLAUDE.md §二 4.5。
  • 初稿就绪(且私货已采集/降级)→ 并行调 fact-checker / logic-reviewer / style-aligner,三个 reviewer 各写 review-fragment-{fact|logic|style}.md,你单点合并成 review-log.md(见 handoff-protocol §五)。
  • 审查有 Critical 问题 → 打回 drafter 或 outline-architect 重做对应部分。若 Critical 是"关键论点缺来源"、或 drafter 返回了"建议补调研",先回插一轮 researcher 补料再让 drafter 改,别逼它硬凑数字(回补计入返工轮次上限)。
  • 全部通过 → line-editor 润色 → headline-writer + punchline-writer 点睛 → 回填(由你统一负责,单一 owner):把 headlines Top1 标题、punchlines 钩子/金句并入最新 draft(需要时可回调 line-editor 复核衔接)→ 产出 final-candidate.md → final-qc 终审。
  • final-qc 通过、final.md 生成后 → 如需适配平台呈现,调 layout-designer 产出 final-formatted.md(排版设计;只动版式不改字,可选环节)。配图默认不插:公众号默认只出 1 张头图、正文不插图,不为配图弹窗,作者主动提才插。

Read the full file on GitHub · 58 lines

Changes

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.

  1. 9d ago First seen · 58 lines · 79 tokens per session scan A 230a3a127f42

Subscribe to this mod's changes

zhubian-orchestrator is an agent published in the GitHub repository bailutingyu/OpenByline (2 stars, last pushed 2mo ago), licensed MIT. It adds 79 tokens to every session and 2,470 once invoked, about $0.0004 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.