Suede Ads logo

Suede Ads

Community
JasonColapietro
suede-ads

Suede AI Ads Skills. Use when planning paid acquisition and asked to "audit our paid campaigns" or choose channels, structure campaigns, set budget pacing, retargeting and kill-or-scale rules from verified unit economics and performance. NOT FOR: ad asset production (suede-ad-creative), tracking implementation (suede-analytics), attribution reconciliation (suede-attribution), or landing-page work (suede-site-alchemy).

Overview

PublisherJasonColapietro
Repositorysuede-creator-skills
Skill namesuede-ads
Stars
126
Forks
12
Bundled files
17
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 17 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by JasonColapietro on GitHub. Read the source before you install it.

Installation

Install the Suede Ads AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/JasonColapietro/suede-creator-skills/tree/main/skills/suede-ads
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/JasonColapietro/suede-creator-skills.git /tmp/suede-creator-skills
mkdir -p .claude/skills
cp -r /tmp/suede-creator-skills/skills/suede-ads .claude/skills/suede-ads
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Suede Ads in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Suede Ads on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Suede Ads is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Suede AI Ads Skills

Read the worked delivery contract for a concrete input, expected artifact, failure response and validation example.

Compatibility

Works from campaign exports and business economics; current official platform documentation for changing controls. Live account access is optional for audits and requires existing authorization for changes.

Use this paid-acquisition playbook to create, optimize, and scale campaigns against explicit acquisition economics. Never assume account access.

Before Starting

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Gather this context (ask if not provided):

1. Campaign Goals

  • Which platform(s) are running now, or which do you want to start with?
  • What's the primary objective? (Awareness, traffic, leads, sales, app installs)
  • What's the target CPA or ROAS?
  • What's the monthly/weekly budget?
  • Any constraints? (Brand guidelines, compliance, geographic)

2. Product & Offer

  • What are you promoting? (Product, free trial, lead magnet, demo)
  • What's the landing page URL?
  • What makes this offer compelling?

3. Audience

  • Who is the ideal customer?
  • What problem does your product solve for them?
  • What are they searching for or interested in?
  • Do you have existing customer data for lookalikes?

4. Current State

  • Have you run ads before? What worked/didn't?
  • Do you have existing pixel/conversion data?
  • What's your current funnel conversion rate?
  • Do you have creative assets already, or do they need to be produced?

Reference Routing

This skill's depth lives in references: load by intent. For any operational decision on a live account (kill/keep/scale/budget), load the relevant playbook before answering; the thresholds live there, not here.

User intentLoadCovers
B2B strategy, funnel stages, budget splits, kill rules, lead quality, breakeven mathb2b-paid-playbook.mdDemand lifecycle, leading/lagging signals, kill rules, offline conversion loop, U/B/F lead scoring, scaling quadrant
Meta operations: when to kill/graduate/scale an ad, fatigue, testing structuremeta-decision-system.mdTCPL-anchored decision tree, ad-count ceiling, 80/20 CBO structure, fatigue bands, lead forms, Advantage+ transition
Meta ad sequencing, ordered ad paths, Target frequency setupmeta-ad-sequencing.mdEligible objectives, flight math, hook-problem-proof-offer path, cost trade-off, retargeting workaround for Sales and Leads
LinkedIn operations: bidding, audience sizing, scaling, benchmarks, TLAs, formatslinkedin-b2b-playbook.mdBidding progression, penetration scaling, sizing rules, funnel benchmarks, document/conversation ads, audit shortlist
Google Search: what to spend on first, structure, match types, negatives, PMaxgoogle-search-playbook.mdIntent ladder, account structure, match-type gates (incl. AI Max), negatives, bidding by volume, offline conversions, PMax guardrails
Named-account targeting, pipeline acceleration, cross-channel retargetingabm-playbook.mdLinkedIn/Meta ABM, list mechanics, acceleration campaigns, UTM cross-channel remarketing, ABM measurement
Weekly competitor ad tracker, ad-library monitoring, build vs. buy for ad-spy toolscompetitor-ad-tracker.mdScheduled pull from public ad libraries, creative storage, model tagging, weekly diff, gap questions, longevity as a proxy
Generating Google RSAsrsa-output-spec.mdMandatory output spec: limits, sidecars, template, self-check
Audience setup, tracking setup, launch checklists, copy formulasaudience-targeting.md · conversion-tracking.md · platform-setup-checklists.md · ad-copy-templates.mdExisting foundations

Platform Selection Guide

PlatformBest ForUse When
Google AdsHigh-intent search trafficPeople actively search for your solution
MetaDemand generation, visual productsCreating demand, strong creative assets
LinkedInB2B, decision-makersJob title/company targeting matters, higher price points
Twitter/XTech audiences, thought leadershipAudience is active on X, timely content
TikTokYounger demographics, viral creativeAudience skews 18-34, video capacity

Campaign Structure Best Practices

Account Organization

Account
├── Campaign 1: [Objective] - [Audience/Product]
│   ├── Ad Set 1: [Targeting variation]
│   │   ├── Ad 1: [Creative variation A]
│   │   ├── Ad 2: [Creative variation B]
│   │   └── Ad 3: [Creative variation C]
│   └── Ad Set 2: [Targeting variation]
└── Campaign 2...

Naming Conventions

[Platform]_[Objective]_[Audience]_[Offer]_[Date]

Examples:
META_Conv_Lookalike-Customers_FreeTrial_[YYYYQ#]
GOOG_Search_Brand_Demo_Ongoing
LI_LeadGen_CMOs-SaaS_Whitepaper_[MMMYY]

Budget Allocation

Testing phase (first 2-4 weeks):

  • 70% to proven/safe campaigns
  • 30% to testing new audiences/creative

Scaling phase:

  • Consolidate budget into winning combinations
  • Increase budgets ~20% at a time: never 30%+ in one move (resets platform learning)
  • Wait 3-5 days between increases for algorithm learning

Ad Copy

Copy formulas, angle libraries, and character limits belong to suede-ad-creative. This skill decides which campaign, audience, and offer the copy serves. Quick structures for a brief live in references/ad-copy-templates.md.


Audience Understanding & Targeting

Knowing your audience deeply is still the highest-leverage work in paid ads: demographics, job titles, pain points, fears, hopes, the exact language they use, who they follow, what they've tried, why they failed, what they buy. Gather every identifier you can.

What has changed is where you apply that knowledge. As ad-platform algorithms have gotten dramatically better at finding the right person, jamming all your audience identifiers into the platform's targeting filters underperforms feeding those same identifiers into the creative (headlines, copy, visuals, hooks, examples).

The discipline now: audience knowledge → creative first, targeting filters second. How much that ratio tips toward "creative" varies meaningfully by platform.

Platform-by-platform: where to apply audience knowledge

PlatformAudience knowledge → creativeAudience knowledge → targeting filtersNotes
Meta80%+20%Broad audiences plus distinct creative usually beat interest stacking. See "Meta in the Andromeda era" below.
Google Search40%60%Keywords are still the dominant signal: match-types, search-intent layering, and negative keywords still drive performance. Creative (RSA headlines) matters but is downstream of the keyword.
Google Performance Max / Demand Gen70%30%Audience signals are advisory, not deterministic. Creative + product feed quality dominate.
LinkedIn40%60%Job-title / company / industry filters still produce real precision because LinkedIn's identity data is high-quality. Creative makes the click; firmographics make the right person see it.
TikTok70%30%Algorithm is closer to Meta's model: broad targeting + native-feeling creative wins. Some audience interests help but creative dominates.
Twitter/X50%50%Interest + follower targeting still meaningful, but creative differentiation is high-leverage given lower competition.

These ratios are directional, not precise. Test in your actual account.

For how each identifier becomes a headline, hook, or angle, use suede-ad-creative: it owns the angle-to-copy mapping.

Key Concepts (still apply)

  • Lookalikes: Base on best customers (by LTV), not all customers. Still high-value across platforms.
  • Retargeting: Segment by funnel stage (visitors vs. cart abandoners). See "Retarget with a different offer" and "The four-ad retargeting layer" below.
  • Exclusions: Exclude existing customers and recent converters: showing ads to people who already bought wastes spend.

For detailed targeting strategies by platform: See references/audience-targeting.md


Meta in the Andromeda era

What is verified. Andromeda is Meta's ad retrieval system. Meta Engineering described it on December 2, 2024: for each impression it narrows tens of millions of eligible ads to a few thousand candidates before the ranking models and the auction run. Meta's advertiser guidance in December 2025 called creative diversification a foundational practice: vary format, message, and persona instead of iterating on one concept. Everything else below is operating practice to test, not platform fact.

Operating practice that follows:

  • Creative does most of the targeting. Start broad (country plus customer exclusions) and let distinct creative find distinct buyers. Test it: duplicate a winning interest-stacked ad set, remove the interests, run both for 7 days on equal budget, and compare CPA on the optimization event.
  • Distinct concepts beat near-duplicates. Ten color swaps of one ad teach the system one thing. Ten different angles, formats, and personas teach it ten. Track concept count, not ad count.
  • Make statics part of the mix. Statics are cheap and fast, so they make concept volume affordable. Test them against video on cost per qualified result; do not assume either format wins.
  • Identity-keyword variants. Duplicate a winner and insert the buyer's identity into the headline or first line ("leads per week" becomes "dental leads per week", "law firm leads per week"). The word works on the reader and gives the system a clearer signal. Use only identities the offer truly serves.
  • AI-assisted segment rewrites. Give a model the winning ad and ask it to rewrite for one named segment while keeping voice, structure, and claims identical. Review every variant for invented claims, then route the batch through suede-ad-creative for the pre-delivery self-check.
  • Rescue test for unspent variants. Budget optimization often gives most variants little or no spend. Move the few you have strong evidence for into a separate ad set and give them a fair read before discarding them.
  • Look native. Study what performs organically in the niche on Instagram, TikTok, and YouTube and match that look. A proven organic post run as a partnership or boosted ad is often the cheapest test available.
  • Attribution windows changed. Since January 12, 2026 the Ads Insights API no longer returns 7-day view and 28-day view windows. Available windows are 1-day click, 7-day click, 28-day click, 1-day engaged-view, and 1-day view. Update dashboards and any report that compared view-through windows.

Ad sequencing

Meta ranks the second ad someone sees against the first, so plan two or three ads as a path (hook, problem, proof, offer) rather than testing each alone. Setup limits, flight math, costs, and the retargeting workaround for Sales and Leads objectives live in meta-ad-sequencing.md.

The full kill, graduate, and scale rules live in meta-decision-system.md.

Creative Best Practices

Image Ads

  • Clear product screenshots showing UI
  • Before/after comparisons
  • Stats and numbers as focal point
  • Human faces (real, not stock)
  • Short, legible text overlay. Meta retired its 20% text rule in 2020, but text-heavy images can still deliver worse; test a lighter version

Video Ads Structure (15-30 sec)

  1. Hook (0-3 sec): Pattern interrupt, question, or bold statement
  2. Problem (3-8 sec): Relatable pain point
  3. Solution (8-20 sec): Show product/benefit
  4. CTA (20-30 sec): Clear next step

Production tips:

  • Captions by default: much feed video plays muted, and captions help comprehension either way
  • Vertical for Stories/Reels, square for feed
  • Native feel outperforms polished
  • First 3 seconds determine if they watch

Creative Testing Hierarchy

  1. Concept/angle (biggest impact)
  2. Hook/headline
  3. Visual style
  4. Body copy
  5. CTA

Campaign Optimization

Read optimization and retargeting before proposing budget, bid, audience or retargeting changes. It contains the existing metric, bid progression and funnel-window rules.

Landing Page Alignment (the headline-mirror trick)

The ad platform tests headlines against far more people than ever reach the landing page, so it resolves a winning headline much faster than on-page testing does. Use it that way:

  1. Run 20-40 different headlines as ad variations
  2. Pick the winner by CTR and downstream conversion, not CTR alone
  3. Mirror the winning headline verbatim in the landing page H1, sub-headline, and lead-in copy
  4. Measure the landing-page conversion rate before and after with suede-ab-testing; message match usually helps, but the size of the lift is the account's to prove

Keep at least 3 split tests running somewhere in the funnel (creative, page, offer, or post-conversion flow) at any given time.

Post-click page work itself belongs to suede-site-alchemy.


Reporting & Analysis

Weekly Review

  • Spend vs. budget pacing
  • CPA/ROAS vs. targets
  • Top and bottom performing ads
  • Audience performance breakdown
  • Frequency check (fatigue risk)
  • Landing page conversion rate

Attribution Considerations

  • Every platform counts the same sale; never sum platform-reported conversions
  • Use UTM parameters consistently and reconcile against the CRM or backend
  • Record each platform's attribution window with the number (Meta's API now offers only 1-day view and click windows up to 28 days)
  • Judge on blended CAC and pipeline, not platform CPA alone; route model and reconciliation work to suede-attribution

Scaling discipline (net cash > ROAS percentage)

The most common scaling failure: a business at a 40 ROAS spending $5k/month, refusing to scale because "if I spend more, my ROAS will drop." This is the wrong frame.

Net cash flow > ROAS percentage at the business level:

  • ROAS dropping from 10 → 5 sounds bad
  • But if spend goes from $10k → $100k, you net dramatically more total profit
  • The number to optimize is blended ROAS at the business level, not per-ad-set ROAS
  • Even better: optimize net free cash flow, not ROAS at all

Find your break-even ROAS:

  1. Calculate the absolute maximum you can pay to acquire a customer and still be profitable (factoring LTV)
  2. That's your break-even ROAS / CPA ceiling
  3. Scale until you approach that ceiling, not until your ad-account ROAS drops below an arbitrary preference

The monthly owner review:

  • Block about 3 hours a month for the budget owner to read the raw numbers directly, not a summary from the analyst or media buyer
  • First-hand familiarity with the data is what makes fast scale decisions safe

Outbound-call your leads who didn't convert:

  • Every lead that downloaded a lead magnet or hit your funnel but didn't buy gets a call
  • Ask why they didn't book, what was confusing, what the actual blocker was
  • These verbatim answers become objection-handling ads (see Retargeting section)
  • Massive insight-to-creative loop that most advertisers skip

Platform Setup

Before launching campaigns, ensure proper tracking and account setup.

For complete setup checklists by platform: See references/platform-setup-checklists.md

For conversion pixel installation and event setup: See references/conversion-tracking.md

Universal Pre-Launch Checklist

Each box names the artifact that proves it. A box is checked when the artifact exists, not when it sounds true.

  • Conversion tracking fired: the test conversion is visible in the platform's event manager with a timestamp
  • Landing page loads in <3 sec: cite the PageSpeed / Lighthouse number and the test date
  • Landing page mobile-friendly: a mobile render of the page, not a desktop assumption
  • UTM parameters working: paste the resolved destination URL with parameters intact
  • Budget set correctly: the daily/lifetime figure, against the cap the user stated
  • Targeting matches intended audience: the saved audience definition, read back

Any unchecked box means do not recommend launch. Name the box and what is missing.


Google RSA Output Spec (mandatory when generating RSAs)

When the user requests Google Ads RSAs, load references/rsa-output-spec.md and follow it exactly: hard character limits, required sidecar artifacts (ad groups, negatives, sitelinks, callouts), output order, template shape, CFM medical compliance, and the pre-send self-check. Do not output any RSA that violates it.


Common Mistakes to Avoid

  • Too many campaigns: fragmenting budget across campaigns none of which exit learning
  • Optimizing for the wrong metric: clicks or CTR when the objective is conversions
  • Only one ad per ad set: the algorithm has nothing to allocate between
  • Overlapping audiences competing: the same account bidding against itself

Tool Integrations

This pack does not include ad-network integrations. Work through an authorized platform UI, current export, API, or installed connector; verify current official documentation and the selected account before any mutation.

Platform familyTypical useVerify before execution
Search adsCapture declared intentQuery scope, match behavior, negatives, location, conversion action
Social feed adsCreate or harvest demandAudience controls, placement, creative specs, attribution window
Professional-network adsReach role or account segmentsTargeting availability, minimum audience, lead form and CRM mapping
Short-video adsVisual discovery and creator-style demandPlacement specs, audio rights, age and regional policy

For the measurement contract, use references/conversion-tracking.md and route implementation and firing checks to suede-analytics.


Boundaries

  • Do not create, launch, pause, delete, or change campaigns, bids, audiences, or budgets without explicit authorization.
  • Do not claim ROAS, attribution, or incrementality when conversion tracking and revenue inputs have not been verified.
  • Do not recommend spend above the stated cap; if no cap exists, provide a bounded test budget and wait for approval.
  • Do not target sensitive traits, evade platform policy, or present inferred audience attributes as verified facts.

When authorization is missing. If an authorized account or connector is present and the user asks for a mutation you have no approval for, stop before the call. Name the exact mutation and the account, campaign, or ad set it would hit. Offer: a draft-only change list they can apply themselves, a request for written approval on that specific change, or continuing read-only with an audit instead. Then wait, do not execute on assumed consent. (For the spend-cap case, the standing rule in the third boundary above already applies; don't restate it.)

Routing

  • Need ad copy or visual variants -> use suede-ad-creative.
  • Need conversion tracking implementation -> use suede-analytics; attribution models and cross-platform reconciliation -> use suede-attribution.
  • Need post-click conversion work -> use suede-site-alchemy.
  • Need CRM handoff or offline conversion design -> use suede-revops.
  • From those skills, route paid channel, budget, bid, and campaign decisions back to suede-ads.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Suede Ads AI skill do?

Suede AI Ads Skills. Use when planning paid acquisition and asked to "audit our paid campaigns" or choose channels, structure campaigns, set budget pacing, retargeting and kill-or-scale rules from verified unit economics and performance. NOT FOR: ad asset production (suede-ad-creative), tracking implementation (suede-analytics), attribution reconciliation (suede-attribution), or landing-page work (suede-site-alchemy).

Why use Suede Ads on TypingMind?

Because you install it once and use it with any model. Suede Ads is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Suede Ads in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JasonColapietro/suede-creator-skills/tree/main/skills/suede-ads. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Suede Ads?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Suede Ads?

As many as you like. As long as a model supports skills, you can use Suede Ads with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Suede Ads AI skill free?

Yes. It is published on GitHub by JasonColapietro under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇