01
季度概览
展示投入 30,000、归因收入 105,000 与 ROAS 3.50x，先呈现核实后的关键指标。
SYNTHETIC DEMONSTRATION
从投放数据，到三页客户汇报
示例面向 Agency 客户负责人：先核对季度投入和回报，再说明归因口径，最后约定验证步骤与行动。全部数据为合成演示数据，不是真实客户报告或效果承诺。
文档更新:
输入数据与计算
2026 年 7–9 月，金额单位 USD。固定 7 日点击归因窗口；不含代理费、自然收入和退款。ROAS = 归因收入 ÷ 投放金额。
| 月份 | 投放金额 | 归因收入 | ROAS |
|---|---|---|---|
| July | 10,000 | 30,000 | 3.00x |
| August | 10,000 | 35,000 | 3.50x |
| September | 10,000 | 40,000 | 4.00x |
| 季度合计 | 30,000 | 105,000 | 3.50x |
投入合计 10,000 × 3 = 30,000；收入合计 30,000 + 35,000 + 40,000 = 105,000；季度 ROAS = 105,000 ÷ 30,000 = 3.50。月度回报上升不能据此归因于某个渠道或证明增量收入。
三页报告怎么组织？
以下是完整大纲的内容摘要，不是已经生成的 MCP 报告截图。实际报告链接在创建任务后返回。
02
数据口径
列出数据来源、时间范围、月度数字与归因窗口，说明费用和退款排除项。
03
发现与行动
指出效率趋势和证据限制，安排归因核对、渠道拆解与受控预算测试。
在 Agent 中生成这个示例
先完成接入与授权，再复制下面的英文请求。该请求明确要求创建一次报告，会使用授权工作区的可用额度；本网页本身不会发起创建。
Read https://www.algforce.com/mcp.md and its complete Skill and outline reference. After MCP authorization, use the complete three-page synthetic agency outline provided there to create one English demonstration report. The input is synthetic July-September 2026 data: monthly spend USD 10,000 each; monthly attributed revenue USD 30,000, 35,000, and 40,000; fixed 7-day click attribution. Verify spend USD 30,000, revenue USD 105,000, and quarterly ROAS 3.50x. Clearly label synthetic data, exclude agency fees, organic revenue, and refunds, and do not infer causality. Show the exact returned report_url immediately, retain job_id, and poll get_report serially using poll_after_seconds until completed or failed. This request authorizes one report creation, which is subject to workspace limits. Do not create again to check progress.- Agent 读取完整 Skill 和大纲规范，验证输入数字，使用 outline 和 locale 调用 create_report。
- 立即展示实际返回的 report_url，保存 job_id；按 poll_after_seconds 查询 get_report，直到 completed 或 failed。
- 预览页逐步显示报告内容；完成后可分享。编辑与导出在官网编辑器完成，需要相应登录和权限。
完整英文大纲
与 /mcp.md 和工具文档中的演示大纲使用同一份源数据。
查看完整大纲与页面布局
Agency Client Performance Review
<!-- meta goal: Review campaign efficiency and agree on next month's priorities skill: agency-client-review style: default lang: business pages: 3 audience: Client marketing lead date_range: 2026-07-01 to 2026-09-30 generated: 2026-10-05 -->Synthetic demonstration data for July–September 2026; not a real client report.
Data overview
layout: KPI Ledger
layout_intent: Summarize verified quarterly performance before reviewing definitions.
layout_slots: kpi-summary
- [slot: kpi-summary] Quarterly performance [smart_layout]
Metric Current value Description Ad spend USD 30,000 Total July–September campaign spend Attributed revenue USD 105,000 Revenue attributed using the demo's fixed 7-day click window ROAS 3.50x USD 105,000 / USD 30,000; excludes agency fees Evidence: Monthly ROAS increased from 3.00x in July to 4.00x in September. Attribution: These aggregates show an efficiency trend, but do not establish its cause. Recommendation: Review channel and campaign breakdowns before reallocating budget.
Data definitions
layout: Specification Sheet
layout_intent: Define the source, attribution window and calculation scope.
layout_slots: spec-body
- [slot: spec-body] Measurement scope [smart_layout]
Definition Value Source Synthetic monthly campaign aggregates for this example Scope July–September 2026; all amounts in USD Spend by month July 10,000; August 10,000; September 10,000 Revenue by month July 30,000; August 35,000; September 40,000 ROAS definition Attributed revenue / ad spend; fixed 7-day click window Footnote: Agency fees, organic revenue and refunds are excluded. No causal or incremental-lift conclusion can be made from these aggregates.
Validate the efficiency trend before increasing investment
layout: Closing Statement
layout_intent: Turn the measured trend into specific next steps without asserting causality.
layout_slots: takeaways
- [slot: takeaways] Findings and next steps [smart_layout] Findings: 01. Quarterly ROAS was 3.50x on USD 30,000 of spend. 02. Monthly attributed revenue rose from USD 30,000 to USD 40,000 while spend stayed constant. 03. The aggregate data cannot isolate the drivers or confirm incremental revenue. Actions: P1. Marketing lead: validate attribution and refund handling before the next review. P2. Agency analyst: compare channels and campaigns using consistent attribution definitions. P3. Account lead: agree on a controlled budget test after validating the breakdown. Goal: Confirm a repeatable efficiency improvement before scaling spend.