Ads Asset Generator

v1.0.0

Generate production-ready ad asset specs for Meta (Facebook/Instagram), TikTok Ads, YouTube Ads, Google Ads, Amazon Ads, and Shopify Ads placements.

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Purpose & Capability
The name and description (generate ad asset specs for multiple ad platforms) match the SKILL.md's inputs, outputs, workflow, decision rules, and platform notes. Nothing in the metadata or instructions asks for unrelated access (cloud creds, system files, or other services).
Instruction Scope
SKILL.md contains explicit input/output contracts, workflows, platform-specific guidance, guardrails, and examples; it does not instruct the agent to read files, environment variables, system state, or to send data to any external endpoint. Instructions remain within the stated purpose.
Install Mechanism
No install spec, no code files, and no downloads — the skill is instruction-only, which minimizes filesystem and supply-chain risk.
Credentials
No required environment variables, no credentials, and no config paths are declared or referenced by the SKILL.md. Requested access is proportionate (none) to the skill's purpose.
Persistence & Privilege
always is false and disable-model-invocation is false (normal). The skill does not request permanent presence, nor does it modify other skills or system settings. Autonomous invocation is allowed by default but does not amplify other red flags here.
Assessment
This skill appears coherent and low-risk because it is purely instruction text that generates ad creative briefs and requires no credentials or installs. Before using it in production: (1) validate any policy, billing, or targeting recommendations against your ad accounts and platform policies (the skill purposely notes you should not fabricate policy outcomes), (2) never paste sensitive credentials or account tokens into prompts, (3) test outputs on sample inputs to ensure the suggested specs match your creatives and measurement setup, and (4) if you enable autonomous invocation for an agent, monitor actions that lead to live spend — the skill can produce plans but cannot itself execute ad account operations.

Like a lobster shell, security has layers — review code before you run it.

latestvk975phpea1n54py3z22nvgqhm18268e9
336downloads
1stars
1versions
Updated 1mo ago
v1.0.0
MIT-0

Ads Asset Generator

Purpose

Core mission:

  • asset brief generation, format adaptation, variant plan

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), TikTok Ads, YouTube Ads, Google Ads, Amazon Ads, Shopify Ads
  • this specific capability: asset brief generation, format adaptation, variant plan

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:

  • product_or_offer
  • target_audience
  • placement_scope

Optional:

  • existing_assets
  • brand_tone
  • prohibited_claims
  • creative_constraints

Output Contract

  1. Creative Objective
  2. Angle and Hook Set
  3. Asset Specification
  4. Test Variant Plan
  5. Creative QA Notes

Workflow

  1. Anchor creative goal to funnel stage and KPI.
  2. Generate angle family and hook variants.
  3. Map each angle to placement format requirements.
  4. Define variant matrix and test order.
  5. Add quality and compliance checkpoints.

Decision Rules

  • If audience is cold, prioritize problem-agitation and proof-first hooks.
  • If retargeting stage, prioritize offer clarity and urgency mechanics.
  • If format limits are strict, simplify message hierarchy to one CTA.

Platform Notes

Primary scope:

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

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

Creative Brief Example

creative_id: CR-001
angle: pain_to_outcome
hook: "Stop wasting ad budget in week one"
formats: [9:16_video, 1:1_image]

Variant Matrix

V1: hook_change
V2: CTA_change
V3: visual_proof_change

Examples

Example 1: New hook generation

Input:

  • Existing creatives have high frequency fatigue
  • Need fresh top-funnel hooks

Output focus:

  • new angle families
  • hook library
  • test priorities

Example 2: Asset adaptation by placement

Input:

  • One hero concept, multiple platforms
  • Need format-safe variations

Output focus:

  • per-platform format specs
  • copy-length adaptation
  • QA checklist

Example 3: Pre-launch creative scoring

Input:

  • 12 creatives pending launch
  • Limited review bandwidth

Output focus:

  • quality scores
  • reject/rework decisions
  • launch candidate shortlist

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

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