CMO Helper

Support CMO-level planning across Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, and DSP/programmatic with revenue,...

Security Scan

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.

Purpose & Capability

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.

Instruction Scope

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.

Install Mechanism

No install spec is provided (instruction-only). Nothing is written to disk or downloaded, which minimizes install-time risk.

Credentials

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.

Persistence & Privilege

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.

Assessment

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 carefully; (4) treat forecasts as decision-support, validate with your analytics stack before acting. If you want the skill to pull live campaign data, request documentation on how it plans to authenticate and which endpoints it will call.

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