Ads Traffic Analysis

Analyze traffic composition and quality trends from Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and DSP/programmatic channels.

Security Scan

The skill is instruction-only and its requested scope, inputs, and outputs are consistent with an ads traffic analysis helper; it asks for no credentials or installs and contains no code that contradicts its description.

Purpose & Capability

The name/description (traffic mix decomposition, trend anomaly diagnosis across ad platforms) aligns with the SKILL.md workflow, input/output contract, decision rules, and examples. There are no unexpected requirements (no cloud creds, no unrelated binaries) that would be incoherent with the stated purpose.

Instruction Scope

SKILL.md instructs the agent to disambiguate metrics, build query plans, compute deltas, summarize, and recommend actions. It does not tell the agent to read arbitrary local files, probe system state, or transmit data to hidden endpoints. Escalation/hand-off guidance is limited to structured payloads and is appropriate for analysis workflows.

Install Mechanism

No install spec and no code files are present, so nothing is written to disk and there are no external packages or downloads. This is the lowest-risk model for a skill of this type.

Credentials

The skill declares no required environment variables, credentials, or config paths. That is proportionate for a guidance/analysis skill that expects user-supplied data inputs rather than direct API access. If the agent later needs to call platform APIs, those credentials would need to be provided explicitly at use time.

Persistence & Privilege

always is false and the skill does not request persistent modifications to agent/system settings. There is no attempt to modify other skills or require permanent presence.

Assessment

This skill looks coherent and low-risk: it is instruction-only, asks for no credentials, and stays within the stated ads-analysis purpose. Before installing or using it, consider: (1) If you connect it to real ad platforms later, supply API keys only when needed and use least-privilege credentials; (2) Avoid pasting sensitive account tokens or full raw datasets into chat unless you trust the execution environment; (3) Verify any high-impact recommendations (budget shifts, policy escalations) with a human who has platform access; (4) If you expect the skill to call your analytics/BI systems automatically, confirm where credentials are stored and audited. Overall, the skill appears internally consistent — proceed with normal operational caution.

Purpose

Core mission:

• traffic mix decomposition, trend anomaly diagnosis

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: traffic mix decomposition, trend anomaly diagnosis

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:

• question_or_report_goal
• metric_scope: KPI, dimensions, and date range
• data_source_scope

Optional:

• attribution_window
• benchmark_reference
• dashboard_filters
• confidence_threshold

Output Contract

1. Metric Definition Clarification
2. Query Plan
3. Result Summary
4. Interpretation and Caveats
5. Decision Recommendation

Workflow

1. Disambiguate metric definitions and time window.
2. Build query slices by platform, funnel, and audience.
3. Compute trend deltas and variance drivers.
4. Summarize findings with confidence level.
5. Propose concrete next actions.

Decision Rules

• If metric definitions conflict, lock one canonical definition before analysis.
• If sample size is small, mark result as directional not conclusive.
• If attribution changes materially alter result, show both views.

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

Query Spec Example

metric: roas
dimensions: [platform, campaign]
date_range: last_30d

Result Schema

{
  "platform": "Meta",
  "spend": 12000,
  "revenue": 42000,
  "roas": 3.5
}

Examples

Example 1: Daily report automation

Input:

• Need 9AM daily summary for key campaigns
• KPI: spend, cpa, roas

Output focus:

• report schema
• anomaly highlights
• top next actions

Example 2: Attribution window comparison

Input:

• 1d click vs 7d click disagreement
• Decision needed for budget shift

Output focus:

• side-by-side metric table
• interpretation caveats
• decision recommendation

Example 3: Traffic structure diagnosis

Input:

• Revenue flat but traffic rising
• Suspected quality decline

Output focus:

• source mix decomposition
• quality signal changes
• corrective action plan

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