Deep Ads Analyst

Perform deep-dive strategic analysis using cross-platform evidence from Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and DSP/p...

Security Scan

An instruction-only marketing analysis skill that is internally coherent for synthesizing evidence supplied by the user, but it does not include any mechanism to pull platform data despite claiming cross‑platform evidence.

Purpose & Capability

The name, description, and SKILL.md all describe deep, cross‑platform ad analysis and evidence mapping — which matches the workflow and outputs in the instructions. However, the skill claims to use evidence from specific platforms (Meta, Google Ads, TikTok, YouTube, Amazon, DSP) but requests no credentials, has no install, and provides no fetch instructions. That means it expects the user (or agent runtime) to supply platform data rather than pull it automatically; this gap may confuse users who expect automatic cross‑platform collection.

Instruction Scope

SKILL.md contains step‑by‑step workflow, input/output contracts, decision rules, examples and YAML snippets. It does not instruct the agent to read files, access unrelated system state, call external endpoints, or exfiltrate data. Instructions are scoped to analysis and synthesis of evidence provided by the user.

Install Mechanism

No install spec and no code files — instruction‑only skill. This minimizes installation risk because nothing is written to disk or fetched at install time.

Credentials

The skill declares no required environment variables or credentials, which is safe but potentially inconsistent with the description that implies cross‑platform evidence collection. If a user expects the skill to query ad platforms, credentials would be required; the absence of such requirements should be communicated to users so they know they must supply platform data or authorize separate tools.

Persistence & Privilege

always is false and there are no install steps that write persistent configuration. The skill does not request permanent presence or attempt to modify other skills or system settings.

Assessment

This skill is an instruction-only analyst template and appears coherent for synthesizing and evaluating ad evidence you supply. Two practical points before installing or using it: (1) it does not include any code or API connectors — it will not itself fetch account data from Meta, Google Ads, TikTok, etc. If you want automatic cross‑platform pulls you need a separate connector that provides the data or to supply exports to the agent. (2) Because the skill asks users to provide evidence (campaign exports, internal tests, competitor examples), avoid pasting credentials or sensitive tokens into analysis prompts; supply only sanitized data or use secure connectors. If you need the skill to query platforms directly, ask the publisher how they intend to obtain credentials and why none are declared.

Purpose

Core mission:

• hypothesis testing, strategic synthesis, evidence mapping

This skill is specialized for advertising workflows and should output actionable plans rather than generic advice.

When To Trigger

Use this skill when the user asks for:

• ad execution guidance tied to business outcomes
• growth decisions involving revenue, roas, cpa, or budget efficiency
• platform-level actions for: Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, DSP/programmatic
• this specific capability: hypothesis testing, strategic synthesis, evidence mapping

High-signal keywords:

• ads, advertising, campaign, growth, revenue, profit
• roas, cpa, roi, budget, bidding, traffic, conversion, funnel
• meta, googleads, tiktokads, youtubeads, amazonads, shopifyads, dsp

Input Contract

Required:

• research_question
• hypothesis_set
• decision_deadline

Optional:

• source_preferences
• confidence_target
• excluded_assumptions
• output_depth

Output Contract

1. Research Plan
2. Evidence Table
3. Hypothesis Evaluation
4. Strategic Conclusion
5. Actionable Next Experiments

Workflow

1. Decompose research question into testable hypotheses.
2. Define source and evidence collection plan.
3. Evaluate evidence strength and conflicts.
4. Synthesize implications for ad strategy.
5. Output decisions and follow-up experiments.

Decision Rules

• If evidence quality is weak, state limitation and avoid hard claims.
• If hypotheses conflict, rank by evidence strength and recency.
• If decision deadline is near, provide best-effort recommendation with risk notes.

Platform Notes

Primary scope:

• Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, DSP/programmatic

Platform behavior guidance:

• Keep recommendations channel-aware; do not collapse all channels into one generic plan.
• For Meta and TikTok Ads, prioritize creative testing cadence.
• For Google Ads and Amazon Ads, prioritize demand-capture and query/listing intent.
• For DSP/programmatic, prioritize audience control and frequency governance.

Constraints And Guardrails

• Never fabricate metrics or policy outcomes.
• Separate observed facts from assumptions.
• Use measurable language for each proposed action.
• Include at least one rollback or stop-loss condition when spend risk exists.

Failure Handling And Escalation

• If critical inputs are missing, ask for only the minimum required fields.
• If platform constraints conflict, show trade-offs and a safe default.
• If confidence is low, mark it explicitly and provide a validation checklist.
• If high-risk issues appear (policy, billing, tracking breakage), escalate with a structured handoff payload.

Code Examples

Research Plan YAML

hypothesis: creator-led videos improve roas in week 1
sources: [platform_data, competitor_examples, internal_tests]
confidence_target: medium_high

Evidence Row

source: campaign_2026_q1
finding: cpa_down_18pct
confidence: medium

Examples

Example 1: Deep competitor study

Input:

• Need three-month competitor creative and offer shifts
• Channels: Meta + TikTok Ads

Output focus:

• evidence table
• pattern summary
• strategic implications

Example 2: Hypothesis stress test

Input:

• Team believes broad targeting always wins
• Evidence is mixed

Output focus:

• hypothesis decomposition
• confidence-ranked conclusions
• follow-up experiments

Example 3: Board-level strategic brief

Input:

• Need recommendation for next quarter channel direction
• Budget increases available

Output focus:

• scenario options
• risk-weighted recommendation
• decision-ready summary

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