技能详情(站内镜像,无评论)
作者:danyangliu @danyangliu-sandwichlab
许可证:MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本:v1.0.0
统计:⭐ 0 · 180 · 0当前安装量· 0历史安装量
⭐ 0
安装量(当前) 0
🛡 VirusTotal :良性 · OpenClaw :良性
Package:danyangliu-sandwichlab/cmo-ads-helper
安全扫描(ClawHub)
- VirusTotal :良性
- OpenClaw :良性
OpenClaw 评估
The skill is an instruction-only CMO planning helper whose requirements and instructions are internally consistent with its stated purpose and it does not request excess credentials, installs, or system access.
目的
Name/description (CMO-level planning across ad channels) matches the SKILL.md: it asks for business targets, budget, current mix and describes simulation, forecasting, and reporting tasks. There are no unrelated environment variables, binaries, or platform credentials requested that would be disproportionate.
说明范围
SKILL.md contains only modeling, decision rules, scenario workflows, examples, and output contracts. It does not instruct the agent to read local files, access system configuration, call external endpoints, or exfiltrate data. All instructions stay within the declared planning/reporting scope.
安装机制
No install spec is provided (instruction-only). Nothing is written to disk or downloaded, which minimizes install-time risk.
证书
The skill declares no required env vars, no primary credential, and no config path access. That is proportionate to an offline modeling/reporting tool that uses user-supplied inputs.
持久
always:false and user-invocable:true (defaults) — the skill does not request permanent/global presence or attempt to modify other skills or system-wide settings. Autonomous invocation is allowed by platform default but is not combined here with other red flags.
综合结论
This skill appears coherent and low-risk as an offline CMO planning helper. Before using: (1) avoid pasting real platform API keys or other secrets into free-text inputs — the skill does not need them; (2) verify inputs (revenue, baseline KPIs) are accurate because outputs are model-driven estimates, not guaranteed outcomes; (3) if you later enable live integrations with ad platforms, expect explicit credential requests and review them careful…
安装(复制给龙虾 AI)
将下方整段复制到龙虾中文库对话中,由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库(龙虾 AI)执行:为本机安装 OpenClaw 技能「CMO Helper」。简介:Support CMO-level planning across Meta (Facebook/Instagram), Google Ads, TikTok…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装:https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/danyangliu-sandwichlab/cmo-ads-helper/SKILL.md
(来源:yingzhi8.cn 技能库)
SKILL.md
---
name: cmo-ads-helper
description: Support CMO-level planning across Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, and DSP/programmatic with revenue, profit, cashflow, ROAS, and LTV strategy modeling.
---
# CMO Helper
## Purpose
Core mission:
- Parse top-level business goals (revenue, profit, cashflow) into executable growth paths.
- Simulate budget allocation and channel structure with ROAS and LTV forecasts.
- Produce quarterly and annual growth strategy with risk alerts.
- Generate board-ready growth reports.
## When To Trigger
Use this skill when the user asks for:
- annual or quarterly growth planning
- high-level budget allocation by channel
- ROAS/LTV forecast and risk evaluation
- CMO report narratives for leadership updates
High-signal keywords:
- growth, revenue, profit, roi, roas, ltv
- ads, media, campaign, forecast, model, allocation
- strategy, budget, dashboard, report, predict
## Input Contract
Required:
- business_targets: revenue_target, profit_target, cashflow_target
- planning_horizon: quarter or year
- budget_pool: total budget and flexibility range
- current_mix: channel spend and KPI baseline
Optional:
- market_constraints
- hiring_or_resource_limits
- inventory_or_supply_constraints
- risk_tolerance
## Output Contract
1. Executive Goal Decomposition
2. Growth Path by Quarter (or month)
3. Channel Allocation Simulation (base/upside/downside)
4. ROAS/LTV Forecast Assumptions and outputs
5. Risk Radar and Mitigation Plan
6. CMO Report Outline
## Workflow
1. Normalize top goals into quantifiable KPI tree.
2. Build growth path candidates by objective priority.
3. Simulate channel budget structure under multiple scenarios.
4. Forecast ROAS and LTV under attribution assumptions.
5. Flag risks and attach mitigation owners.
6. Export leadership-ready summary.
## Decision Rules
- If cashflow is constrained, prioritize payback speed over max scale.
- If profit target conflicts with growth target, optimize blended margin first.
- If uncertainty is high, widen confidence ranges and use staged budget release.
- If one channel dominates risk, cap exposure and add redundancy channels.
## Platform Notes
Primary scope:
- Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP/programmatic
Platform behavior guidance:
- Meta/TikTok: fast learning via creative breadth.
- Google/Amazon: demand-capture and intent-driven efficiency.
- DSP: incremental reach and controlled frequency.
## Constraints And Guardrails
- Do not present forecast outputs as guaranteed outcomes.
- Separate assumptions, historical facts, and modeled estimates.
- Keep all recommendations linked to measurable KPI deltas.
## Failure Handling And Escalation
- If baseline data is incomplete, produce scenario-only output with confidence labels.
- If goals are contradictory, return trade-off matrix before final recommendation.
- If decision window is short, provide 80/20 plan and required validation steps.
## Code Examples
### Quarterly Growth Model (YAML)
horizon: Q3-2026
targets:
revenue: 2500000
profit: 620000
cashflow: positive
channels:
Meta: 0.35
GoogleAds: 0.30
TikTokAds: 0.15
AmazonAds: 0.10
DSP: 0.10
### Forecast Table Schema (JSON)
{
"scenario": "base",
"blended_roas": 2.9,
"projected_ltv": 145,
"risk_level": "medium"
}
## Examples
### Example 1: Quarterly board plan
Input:
- Need Q3 growth plan with profit floor
Output focus:
- channel budget simulation
- risk warnings
- executive summary points
### Example 2: Annual strategy reset
Input:
- Revenue target increased by 40%
- Cashflow pressure exists
Output focus:
- staged growth roadmap
- payback-sensitive allocation
- guardrails
### Example 3: Budget cut scenario
Input:
- Spend reduced by 20%
- KPI targets unchanged
Output focus:
- re-prioritization logic
- expected trade-offs
- mitigation actions
## Quality Checklist
- [ ] Required sections are complete and non-empty
- [ ] Trigger keywords include at least 3 registry terms
- [ ] Input and output contracts are operationally testable
- [ ] Workflow and decision rules are capability-specific
- [ ] Platform references are explicit and concrete
- [ ] At least 3 practical examples are included