中级Agent Skill

业务运营路由

面对流程、供应商、人员产能、采购或知识管理问题,先选对业务运营工作流。提供明确问题、相关导出资料和决策负责人后,这项 Skill 会将请求交给运营工具集合中的合适专项流程,再整理成简明决策摘要,帮助团队理解发现、待确认事项和下一步。

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01

这个 Skill 是做什么的

这项 Skill 是一组业务运营专项工具的入口,会先区分流程、供应商、产能、沟通、知识和采购问题,再选择对应方法。面对“运营效率需要改善”这样的宽泛请求时,可以先把问题拆清楚,避免用不合适的工具直接分析。

运行需要安装相应的完整工具集合,入口本身没有分析脚本。它负责协调专项结果并形成决策摘要,不是 ERP,也不会批准供应商、人员或内部流程变更。

02

它有什么特色

01

方法与优势

六条专项路径使用不同数据与方法,路由步骤会明确当前选择了哪种分析。团队可以先确认问题分类是否正确,例如人员不足需要产能分析,未必能仅靠重新画流程图解决。

02

适用任务

汇总时保留原来的问题范围与决策负责人,把专项发现组织成便于讨论的摘要。准备好所需导出资料并安装相关子 Skill 后,任务才能继续完成分析,而不是停留在建议使用某个工具。

03

安装前的特殊要求

需要准备的输入

明确的运营问题、相关导出、决策负责人和完整 business-operations Skill 集合。

Agent 与运行环境

使用能发现已安装 Skill 并读取辅助文件的兼容 Agent。外部服务或创作运行环境需另行配置。

需确认的适用边界

路由器本身没有分析脚本,脚本来自同级 Skill。它不是 ERP,也不审批流程或供应商变更。

04

安装与开始使用

将指令复制给 Codex 或 Claude Code,让助手从原始仓库下载固定提交,安装完整文件并检查所需依赖。

  1. 打开能访问本机文件和终端的 Codex 或 Claude Code。
  2. 复制下方指令发给正在使用的应用,由助手选择该应用的安装目录。
  3. 查看返回的安装路径和文件检查结果;需要时新建会话,再要求助手使用已安装的 Skill。
请在我当前使用的应用中安装 business-operations-skills,直接执行文件安装,不要只解释步骤。 原始仓库:https://github.com/alirezarezvani/claude-skills.git 固定提交:6d2f02ea077eead1bee5411a31bcc6db5a11a70c Skill 路径:business-operations/skills/business-operations-skills 1. 将这个版本下载到新目录。执行随包脚本前,先阅读 SKILL.md 和安装参考文件,不替换成其他作者的包。 2. Codex 安装到 ~/.agents/skills/business-operations-skills,Claude Code 安装到 ~/.claude/skills/business-operations-skills,只安装到我正在使用的应用。此路由器依赖 business-operations/skills 中的同级 Skill,请安装该目录下所有包含 SKILL.md 的直接子目录,并保留仓库副本供相对路径引用使用。独立发布的插件不会因此自动安装。先检查所有目标目录;已有安装时,说明差异后再决定是否替换。 3. 比较来源和安装目录的文件清单、文件哈希,检查文档要求的运行时、依赖及外部连接。只配置此 Skill 所需的本地依赖,适用时使用隔离环境;账号登录由我在本地完成,不把凭据贴进聊天。 4. 返回来源提交、实际安装路径、文件检查结果及尚未完成的运行环境配置,说明如何调用 business-operations-skills。文件复制与应用发现分别检查,需要时新建会话。不要执行生产任务,也不要把安装完成说成已确认业务效果。
05

原始 Skill.md

以下为已核对版本未经改写的 SKILL.md。安装完整包才能保留引用的脚本、素材和指引。下方启动提示词为 EcomAgentTools 编写的示例。

---
name: business-operations-skills
description: Use when running, diagnosing, or designing internal business operations — process documentation, vendor SLAs, capacity planning, internal comms, SOP/runbook authoring, procurement spend. Triggers on "BizOps review", "where's the bottleneck", "vendor health", "internal SOP", "all-hands deck", "spend categorization", "capacity for Q3", "process mapping". Forks context to route to one of six BizOps sub-skills (process-mapper, vendor-management, capacity-planner, internal-comms, knowledge-ops, procurement-optimizer) and returns a digest. Distinct from business-growth (external sales motion) and c-level-advisor (strategic, not operational).
context: fork
license: MIT
metadata:
  version: 2.8.0
  author: claude-code-skills
  tags: [bizops, operations, process, vendor, capacity, sop, procurement, coo, orchestrator]
  compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]
---

# Business Operations — Domain Orchestrator

The BizOps surface is **internal**: how the company actually runs. This orchestrator forks its conversation context, routes your inquiry to one of six sub-skills, then returns a tight digest to the parent thread. The heavy ingestion (vendor catalogs, process interviews, multi-doc SOP intake) stays in the forked context.

## When to invoke

| Symptom | Sub-skill to route to |
|---|---|
| "Where does the work spend most of its time waiting?" | `process-mapper` |
| "Is this vendor delivering against the SLA?" | `vendor-management` |
| "Do we have enough people to ship in Q3?" | `capacity-planner` |
| "I need to brief the company on a re-org" | `internal-comms` |
| "Write me a runbook for the incident response process" | `knowledge-ops` |
| "Why is our software spend up 40% YoY?" | `procurement-optimizer` |

## Routing logic (deterministic)

The orchestrator classifies the inquiry by **signals** detected in the prompt. Two-signal threshold for confident routing; one-signal triggers a clarifying question.

### Signal table

| Signal class | Keywords | Sub-skill |
|---|---|---|
| **PROCESS** | bottleneck, cycle time, waiting, handoff, BPMN, process map, workflow | `process-mapper` |
| **VENDOR** | vendor, supplier, SLA, contract, third-party, MSA, SaaS subscription, renewal | `vendor-management` |
| **CAPACITY** | headcount, capacity, utilization, planning, hiring sequence, FTE | `capacity-planner` |
| **COMMS** | all-hands, internal newsletter, announcement, change management, FAQ, town hall | `internal-comms` |
| **KNOWLEDGE** | SOP, runbook, knowledge base, wiki, playbook, documentation, onboarding doc | `knowledge-ops` |
| **PROCUREMENT** | spend, procurement, purchase, supplier rationalization, software audit, SaaS sprawl | `procurement-optimizer` |

If signals are mixed (e.g., "vendor SLA + spend audit"), run the **highest-confidence sub-skill first**, then chain into the second one in a follow-up forked turn.

### Fallback

If no signal class scores ≥ 2, ask **one** clarifying question naming the two most likely candidates. Do NOT guess silently.

## Workflow (Matt Pocock grill discipline)

Derived from Matt Pocock's `grill-with-docs` pattern: **explore-then-ask, one question per turn with a recommended answer, walk the decision tree depth-first, track dependencies, anchor every challenge in the documented canon** (`references/`).

### Step 1 — Explore before asking

Before any clarifying question, check:
- Does the user's working directory already contain a process map, vendor catalog, SOP, or org chart we can grep?
- Does the inquiry already disambiguate the lane (e.g., "vendor SLA review" — that's `vendor-management`, no question needed)?
- Is the lane unambiguous from filenames mentioned (`procurement-Q3.csv` → procurement)?

If the codebase resolves the lane, **route silently**. Don't ask.

### Step 2 — If still ambiguous, ONE forcing question with a recommended answer

Matt's rule: never bundle questions. Never default to "what do you think?". Always offer your recommendation.

Pattern:
```
Q1/1: [precise question naming the two candidate lanes]
Recommended: [Lane X, because <one-sentence rationale from the signal table>]

(Confirm, or override?)
```

Wait for the user's response. **Then** route. Never guess silently after a turn that asked a question.

### Step 3 — Forking decision-tree walk (only if the inquiry crosses lanes)

If the user's inquiry legitimately crosses two lanes (e.g., "vendor SLA + spend audit" = VENDOR + PROCUREMENT), walk the tree **depth-first**:

1. Resolve the higher-confidence lane first → run that sub-skill in forked context → return digest
2. Ask: "Should we now run [second lane]? My recommendation: yes, because [dependency reason]."
3. Only after explicit user confirmation, run the second sub-skill

Do NOT chain silently. Each fork is an explicit user-confirmed step.

### Step 4 — Invoke sub-skill in forked context

Each sub-skill is invoked with the original prompt + a digest of any structured inputs (file paths, JSON inputs). The fork keeps heavy ingestion (vendor catalog, process transcripts, SOP source documents) out of the parent context.

### Step 5 — Return digest with cited canon challenge

When the sub-skill completes, return a **≤ 200-word digest** to the parent thread:

- What was analyzed
- Top 3 findings (each anchored in a reference doc citation — e.g., "Goldratt's Theory of Constraints: optimize the bottleneck, not the non-constraint")
- Top 3 next actions (named owners if possible)
- Path to the artifact(s) produced
- **One grill challenge** for the user, cited: "Your value-add ratio is 12%. Lean canon (Womack & Jones 1996) classifies <15% as waste-heavy. What's blocking process redesign — political, technical, or budget?"

The parent agent can then ask follow-ups (each triggering new forked invocations).

## Forcing-question library (grill-with-docs pattern)

When the user has provided enough context to enter a lane, the orchestrator may grill them on the **decisions inside that lane** before invoking the sub-skill. One question per turn, each with a recommended answer + canon citation. Examples:

- **PROCESS lane**: "Before mapping: do you have measured cycle times per stage, or only estimates? Recommended: insist on measured data for the top-3 longest stages. Anti-pattern (Goldratt 1984): map estimates, optimize the wrong constraint."
- **VENDOR lane**: "Before scoring: what's your tier-1 criticality threshold — by spend ($X/year), or by operational dependency (revenue-blocking if vendor fails)? Recommended: operational dependency. Anti-pattern (Gartner TPRM): spend-only tiering misses critical low-spend vendors like the HVAC vendor in the Target breach."
- **CAPACITY lane**: "Before modeling: are you planning for utilization or throughput? Recommended: throughput (Little's Law). Anti-pattern (DORA): planning for utilization > 80% destroys throughput via queueing."

Never run a sub-skill until the lane-defining decision is locked.

## Assumptions

1. The user is acting on behalf of an organization with ≥ 10 employees (smaller orgs don't need this surface).
2. The user has access to the data the sub-skill needs (process docs, vendor list, spend export, etc.) — or accepts the skill's templated dummy data.
3. The user wants **deterministic, repeatable analysis** over LLM-flavored prose. Every sub-skill ships stdlib-only Python tools.

## Non-goals

- Not a substitute for an ERP, vendor management platform (Vendr, Tropic), or capacity-planning SaaS (Float, Runn).
- Does not store state across sessions — every invocation is self-contained.
- Does not call external APIs from Python tools (stdlib only, by design).

## Distinct from

- **`business-growth/*`** — that's the **external sales motion** (CSM, sales engineering, RevOps). BizOps is **internal**.
- **`c-level-advisor/coo-advisor`** — that's strategic COO judgment ("should we restructure?"). BizOps is tactical ("here's the process map with bottlenecks").
- **`engineering/slo-architect`** — that's system reliability with SLO/SLI/error budgets. `process-mapper` is **business process** reliability, not system reliability.
- **`engineering/llm-wiki`** — that's a **personal** PKM (Karpathy's pattern). `knowledge-ops` is **company-wide** SOP authoring.

## Output artifacts

Every sub-skill produces at least one artifact (markdown, CSV, or JSON) saved to the user's working directory. The orchestrator surfaces the file path in the digest.

## Anti-patterns (do not)

- ❌ Run all 6 sub-skills "to be thorough" — pick one based on signal, return digest, let user chain
- ❌ Auto-approve a vendor or process change — surface findings; the human decides
- ❌ Edit production process docs without asking — write to a new file, propose the diff
- ❌ Skip the digest step — parent context needs ≤ 200-word digest, not the full sub-skill output

## References

- See `c-level-advisor/coo-advisor` for strategic COO framing
- Path-B build pattern: `documentation/implementation/bizops-commercial-expansion-plan.md`
06

开始使用

以下是主要功能的用例提示词,可以直接复制使用。

01

将流程延迟问题路由到流程分析

将流程延迟问题路由到流程分析 适用于:将流程延迟问题路由到流程分析。请准备:明确的运营问题、相关导出、决策负责人和完整 business-operations Skill 集合。

查看 Prompt 和变量收起 Prompt 和变量
请使用已安装的 business-operations-skills Skill。任务:将流程延迟问题路由到流程分析。输入:[INPUT]。限制:[CONSTRAINTS]。先检查缺失信息,再输出可审查的结果、依据、假设和下一步。路由器本身没有分析脚本,脚本来自同级 Skill。它不是 ERP,也不审批流程或供应商变更。

需要替换的变量

[INPUT]
明确的运营问题、相关导出、决策负责人和完整 business-operations Skill 集合。
[CONSTRAINTS]
任务范围、允许的操作、输出格式和审核要求。
02

将续约问题路由到供应商或采购审查

将续约问题路由到供应商或采购审查 适用于:将续约问题路由到供应商或采购审查。请准备:明确的运营问题、相关导出、决策负责人和完整 business-operations Skill 集合。

查看 Prompt 和变量收起 Prompt 和变量
请使用已安装的 business-operations-skills Skill。任务:将续约问题路由到供应商或采购审查。输入:[INPUT]。限制:[CONSTRAINTS]。先检查缺失信息,再输出可审查的结果、依据、假设和下一步。路由器本身没有分析脚本,脚本来自同级 Skill。它不是 ERP,也不审批流程或供应商变更。

需要替换的变量

[INPUT]
明确的运营问题、相关导出、决策负责人和完整 business-operations Skill 集合。
[CONSTRAINTS]
任务范围、允许的操作、输出格式和审核要求。
03

返回包含发现与负责人的运营简报

返回包含发现与负责人的运营简报 适用于:返回包含发现与负责人的运营简报。请准备:明确的运营问题、相关导出、决策负责人和完整 business-operations Skill 集合。

查看 Prompt 和变量收起 Prompt 和变量
请使用已安装的 business-operations-skills Skill。任务:返回包含发现与负责人的运营简报。输入:[INPUT]。限制:[CONSTRAINTS]。先检查缺失信息,再输出可审查的结果、依据、假设和下一步。路由器本身没有分析脚本,脚本来自同级 Skill。它不是 ERP,也不审批流程或供应商变更。

需要替换的变量

[INPUT]
明确的运营问题、相关导出、决策负责人和完整 business-operations Skill 集合。
[CONSTRAINTS]
任务范围、允许的操作、输出格式和审核要求。
07

实际使用案例:找出拣货和发货之间最大的等待

一家 12 人电商团队提供订单释放、拣货、包装修正和发货交接的阶段记录,希望找到最值得先处理的等待。资料是模拟出库场景,所选记录属于需要包装修正的订单组。

使用步骤与 Prompt

  1. 准备下方案例输入资料,保留商品、政策和经营记录中的金额、单位及时间口径。
  2. 从订单释放到发货的出库记录中定位等待与交接瓶颈,区分工作、等待和返工,再安排第一项拣货试点及观察指标。
  3. 提交下方完整 Prompt,查看右侧主要产物;需要交接给同事时,下载包含全部交付文件的压缩包。

案例输入资料:business-inputs.zip

Use the business-operations-skills Skill.

Our homeware ecommerce fulfillment team takes too long from order release to dispatch. Use the supplied process records to locate the bottleneck, map picking and dispatch handoffs, and calculate work, waiting and rework. Recommend the first intervention with an owner, a practical pilot and a measurable follow-up. The supplied stage summaries describe a packing-correction cohort, so keep cohort estimates separate from whole-store delivery performance.

The attached business materials are:
- inputs/brief.json
- inputs/intake.md

Deliver a complete result.md report that the operations team can read, plus any finished files needed for this task. Keep supporting calculations and evidence with the deliverables. Write the report in English; use the requested target language for customer-facing copy. The business datasets are fictional examples unless the brief explicitly names a live public website.

运行结果

下方直接展示本次 Prompt 产生的主要文件,保留实际输出语言。

Reduce the wait for picking first

Pilot more frequent picking pulls, owned by the Picking and packing lead. The daily picking wave is the largest observed delay in the supplied stage summaries: 480 minutes, versus 240 minutes waiting for dispatch. It accounts for 61.5% of the additive time estimate and two-thirds of waiting. This makes picking cadence the strongest first intervention to test. The records establish a delay concentration; they do not establish the team's maximum throughput or prove that staffing is insufficient.

These are fictional stage summaries for a 12-person homeware merchant's selected packing-correction cohort; no operational change or measured improvement has occurred.

What the numbers support

The scope starts with releasing an order and ends when its shipment is dispatched. Carrier transit and customer delivery are outside the records. Every order in this cohort needs packing correction; it is not a representative sample of all store orders.

Time componentSum of stage mediansShare of additive estimateSum of stage P90 values
Input-classified value-add work30 min3.85%60 min
Waiting720 min92.31%2,160 min
Packing correction30 min3.85%90 min
Total780 min / 13 hours100%2,310 min / 38.5 hours

Active work, including correction, totals 60 minutes in the median-stage estimate. Correction therefore consumes 50% of active work, even though it accounts for only 3.85% of elapsed time in this model. Neither percentage is a storewide defect rate.

These totals are sums of stage percentiles. Adding medians does not recover the median of complete orders; adding P90 values does not recover their P90. Use 13 hours as an additive planning estimate and 38.5 hours as a stage-P90 scenario, not customer promises or observed end-to-end service levels. Order-level event records are needed for actual end-to-end percentiles. The supplied value-add classifications are retained, rather than independently validated.

The analyzer labels the process WASTE-HEAVY because its value-add share is 3.85%. Its actual services profile uses 8% and 20% boundaries. These are tool heuristics, not a retail fulfillment benchmark. The requested services profile is retained because the package has no retail profile.

The tool also outputs 0.6154 items/hour from 8 ÷ (780 ÷ 60). That is only an arithmetic proxy: eight is a WIP snapshot, and 780 is an additive median-stage estimate. Little's Law requires a consistent stable system boundary, average WIP and mean elapsed time over a comparable period. The inputs cannot establish actual throughput, daily capacity, a staffing requirement or a whole-store delivery rate.

Current correction-cohort process

Each box shows the supplied stage median. Solid arrows show internal sequence and handoffs within the same operation.

flowchart LR
    start((Start)) --> release
    subgraph release_lane[Order release coordinator]
        release["1. Release fulfillment order · Work · 10 min"]
    end
    subgraph picking_lane[Picking and packing lead]
        pickwait["2. Wait for picking wave · Wait · 480 min"]
        pickpack["3. Pick and pack order · Work · 15 min"]
        correction["5. Correct packing mismatch · Rework · 30 min"]
    end
    subgraph dispatch_lane[Dispatch lead]
        dispatchwait["4. Wait for dispatch handoff · Wait · 240 min"]
        dispatch["6. Dispatch shipment · Work · 5 min"]
    end
    release --> pickwait --> pickpack --> dispatchwait
    dispatchwait -->|Return for correction| correction
    correction -->|Corrected order| dispatch --> finish((Dispatched))
HandoffReceiving roleSupplied delay and implication
Released order → pickingPicking and packing leadMedian 480 min; P90 1,440 min. Picking happens once daily, despite batch size 1 after release.
Packed order → dispatch handoffDispatch leadMedian 240 min; P90 720 min. Queue ownership and dispatch coverage need checking.
Dispatch handoff → packing correctionPicking and packing leadCorrection median 30 min; P90 90 min. A separate return-queue wait is not supplied.
Corrected order → dispatchDispatch leadDispatch work median 5 min; P90 10 min. A separate second handoff wait is not supplied.

This is the supplied correction route, including its return to packing. The records do not describe a no-correction route, repeated correction loops or their frequencies. The pilot should record any additional queues rather than assuming they take zero time.

Ranked diagnosis

PriorityEvidence and detector resultOperational interpretation
1. Picking-wave wait480 min; CRITICAL, R1. It is 48 times the 10-minute mean of value-add stages.Once-daily cadence is documented. Test whether additional pulls shorten waiting; verify staffing and release timing before treating batching as the sole cause.
2. Dispatch-handoff wait240 min; CRITICAL, R1. It is 24 times that mean.A second large queue can absorb the gains from faster picking. A batch or coverage mismatch is a hypothesis; dispatch schedule and queue-age records are missing.
3. Packing correction30 min; MEDIUM, R1. It exceeds the services stage threshold of 25 min.Correction is material labor within this cohort. Missing or incorrect packing information is only a hypothesis; record reason codes before choosing an upstream prevention change.

R2 separately flags combined waiting as HIGH, at 92.31% against a 50% threshold. This overlaps the two wait stages: do not add its 720 minutes to their 480 and 240 minutes. R3 does not trigger because rework's elapsed-time share is below its 15% threshold; that does not establish acceptable quality. The raw detector's approval and self-service suggestions are generic possibilities, not findings about this fulfillment team.

This prioritization follows the supplied Theory of Constraints guidance to act on the dominant constraint first, the value-stream guidance to separate waiting from work, and the bottleneck anti-pattern guidance on batch delays and local optimization. Method and evidence

First intervention: a short picking-cadence pilot

Owner: Picking and packing lead. Accountable role: Operations manager, proposed for end-to-end review. The Order release coordinator maintains complete event records; the Dispatch lead checks downstream queues and cutoffs. Role titles describe responsibilities and do not assume new hires.

  1. Establish a comparable baseline over five staffed days. Log every eligible released order, including orders still open at the end. Use the existing cadence and record staffing, order complexity, dispatch cutoffs and the starting open-order inventory. Calculate order-level medians and P90s; retain correction and no-correction subsets separately.
  2. Run ten staffed days with a scheduled pull every two staffed hours. Keep the same staffing and per-order batch size. Pick the oldest eligible released order at each pull. Before starting, the two leads write down existing staffed hours and dispatch acceptance cutoffs; fit pulls inside that coverage and identify the final feasible pull. Overnight waits remain part of elapsed time. If current coverage cannot support the proposed cadence, record the constraint and revise the schedule before starting.
  3. Review queues at each pull and at shift end. Record missed pulls and reasons. The Dispatch lead watches the packed-order queue so work does not simply accumulate downstream. Continue the existing correction path; log its time and reason. Do not infer a quality gain from this cadence change.
  4. Review after pilot day 5 and decide after day 10. Compare with baseline by weekday, release window and order complexity. Report sample sizes, incomplete orders and changes in volume or staffing. If sparse data or changed mix makes the comparison unreliable, extend observation instead of claiming success.

The primary goal is a 50% reduction in measured picking-wait median versus the matched baseline. The supplied 480-minute median gives a provisional 240-minute reference. The primary business check is improved measured release-to-dispatch median across all eligible orders, with no increase in its P90. Report the correction cohort alongside that whole-pilot population; cohort membership can change, so it cannot serve as the only success measure.

Adopt the cadence only if dispatch-wait median and P90, dispatch-cutoff miss rate, packing-correction frequency and correction labor do not worsen versus the matched baseline, and there is no additional paid staffing or growing backlog. Review labor minutes per order by volume and complexity. A cutoff or quality problem attributable to the cadence triggers review and reversion to the prior cadence while the cause is investigated. Use the pilot scorecard to record the decision; its result cells are intentionally blank.

As an illustration only, reducing picking wait from 480 to 240 minutes while holding every other median constant reduces the additive estimate from 780 to 540 minutes, a 240-minute / 30.77% reduction. This is a planning scenario, not a predicted or achieved improvement. Dispatch waiting would then equal picking waiting, making the next constraint review necessary.

Measurement and completed supporting files

Use pilot-event-log.csv and its event definitions to log pseudonymous order references, event times, actual roles, correction attempts and labor minutes. Compute end-to-end percentiles from each order's own timestamps, count open orders separately, and measure throughput directly as completed shipments per staffed hour. No carrier-delivery outcome is available here.

The current process map and normalized process data preserve the six stages. calculations.csv contains the formulas and exact quantities; tool-cycle-time.json and tool-bottlenecks.json preserve the original tools' outputs with their interpretation explained in evidence.md. verify-calculations.py independently checks those results.

Before scaling, the Operations manager should resolve one decision challenge: if picking wait falls but release-to-dispatch time does not, which downstream queue grew, and should the next intervention move there? This follows the supplied Theory of Constraints instruction to repeat the constraint assessment after each change.

案例中的商家经营资料为模拟数据;涉及网站检查时,报告中的页面观察来自所列公开网址。

08

风险与使用注意事项

已知限制

路由器本身没有分析脚本,脚本来自同级 Skill。它不是 ERP,也不审批流程或供应商变更。

证据与复核

核对源码及随包文件:GitHub 6d2f02ea077e。本次为静态审查,不代表已完成安装运行或业务效果测试。

采用结果前

使用获授权输入,不把密钥贴入提示词。采用前检查面向客户的声明、计算、代码改动及任何拟议的外部操作。

常见问题

它能做什么,有什么优势?

将内部运营问题路由到六个专业流程,并返回简洁决策简报。路由器先区分流程、供应商、产能、沟通、知识和采购任务,再选择方向。

如何安装和开始使用?

按实际 Agent 选择方式,安装完整包,不要只复制页面中的 SKILL.md。 明确的运营问题、相关导出、决策负责人和完整 business-operations Skill 集合。 开启新 Agent 会话,明确要求使用 business-operations-skills。先用小型样本尝试下方一个场景,并检查输出。

适合哪些使用场景?

将流程延迟问题路由到流程分析;将续约问题路由到供应商或采购审查;返回包含发现与负责人的运营简报

使用前需要了解哪些限制?

路由器本身没有分析脚本,脚本来自同级 Skill。它不是 ERP,也不审批流程或供应商变更。

本页核对了什么?

核对源码及随包文件:GitHub 6d2f02ea077e。本次为静态审查,不代表已完成安装运行或业务效果测试。

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作者 / 维护者

Alireza Rezvani

核对源码及随包文件:GitHub 6d2f02ea077e。本次为静态审查,不代表已完成安装运行或业务效果测试。