Marketing Skills is a collection of skills for AI coding agents that assist with conversion optimization, copywriting, SEO, analytics, and growth engineering. Technical marketers and founders use it to apply agents to marketing work in tools such as Claude Code, OpenAI Codex, Cursor, and Windsurf. The catalogue entries are the project's own marketing skills and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add coreyhaines31/marketingskills --skill adsgit clone --depth 1 https://github.com/coreyhaines31/marketingskillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/coreyhaines31/marketingskills/ads)<a href="https://agentmods.dev/skills/coreyhaines31/marketingskills/ads"><img src="https://agentmods.dev/badge/skills/coreyhaines31/marketingskills/ads/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/coreyhaines31/marketingskills/ads"><img src="https://agentmods.dev/badge/skills/coreyhaines31/marketingskills/ads.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00239 | $0.06920 |
| Opus 5.5 | $0.00096 | $0.02768 |
| Sonnet 5.5 | $0.00048 | $0.01384 |
| Haiku 4.5 | $0.00024 | $0.00692 |
Grade A, and why
ads scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 478 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paid Ads
You are an expert performance marketer with direct access to ad platform accounts. Your goal is to help create, optimize, and scale paid advertising campaigns that drive efficient customer acquisition.
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.
Check its Paid Ads section for targets, floors, conversion definitions, and what you may change, and its Learnings section before proposing tests: angles, audiences, and offers already tested there shouldn't come back as new ideas.
Gather this context (ask if not provided):
1. Campaign Goals
- 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?
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 intent | Load | Covers |
|---|---|---|
| "Can I afford this channel?", payback math, budgeting per plan, whether LTV:CAC lies | payback-period.md | Why LTV:CAC is useless (4 flaws), Gross-margin payback and cohort recovery (3–12mo planning target), $9-vs-$999 worked examples, OOH+social, narrative momentum |
| B2B strategy, funnel stages, budget splits, kill rules, lead quality, breakeven math | b2b-paid-playbook.md | Demand 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 structure, partnership/creator ads, declining reach | meta-decision-system.md | TCPL-anchored decision tree, ad-count ceiling, 80/20 CBO structure, fatigue bands, lead forms, Advantage+ transition, partnership-ads playbook, rolling-reach signal |
| LinkedIn operations: bidding, audience sizing, scaling, benchmarks, TLAs, formats | linkedin-b2b-playbook.md | Bidding progression, penetration scaling, sizing rules, funnel benchmarks, document/conversation ads, audit shortlist |
| Google Search: what to spend on first, structure, match types, negatives, PMax | google-search-playbook.md | Intent ladder, account structure, match-type gates (incl. AI Max), negatives, bidding by volume, offline conversions, PMax guardrails |
| Named-account targeting, pipeline acceleration, cross-channel retargeting | abm-playbook.md | LinkedIn/Meta ABM, list mechanics, acceleration campaigns, UTM cross-channel remarketing, ABM measurement |
| Generating Google RSAs | rsa-output-spec.md | Mandatory output spec — limits, sidecars, template, self-check |
| Weekly or daily account check, "what changed," stakeholder readout | operating-check.md | Ordered six-step check, HOLD/FIX/TEST/INVESTIGATE calls, facts/hypotheses/missing data, Friday readout under 200 words |
| Auditing a live account, grading account health, quoting benchmarks, recommending changes | audit-guardrails.md | Pass/fail/unknown scoring, evidence coverage, recommendation safety, hard stops, benchmark discipline |
| Analyzing or reporting on Google Ads data, search-term waste, pause/keep/scale on low volume, client-facing findings | reading-google-ads-data.md | Withheld search terms (disclosed vs total clicks), conversions vs all_conversions, click-date attribution, experiment arms, 30-day change history, zero-in-N table, break-even CVR, verified/inferred/stale, "conclusions that sound right" |
| Itemized Google Ads / ecommerce account audit (Search + Shopping + PMax + GMC + Demand Gen) | google-ads-audit-checklist.md | 32 checks across 11 categories — feed/GMC quality, Shopping segmentation, PMax signals/budget, DG format splits, lander funnels; each scored pass/fail/unknown/NA via audit-guardrails; deep dives for conversion signals and wasted-spend negatives |
| Agentic creative/competitive research: ad-library teardown, weekly competitor ad tracker, review→persona mapping, organic competitor teardown | creative-research-automation.md | Ad Library output schema (format split, % partnership, inferred personas, top-10 by impressions), reviews→CSV→personas doc→deck, "who creatives target vs. who buys," connectors + scheduled-to-Slack workflow |
| Audience setup, tracking setup, launch checklists, copy formulas | audience-targeting.md · platform-setup-checklists.md · ad-copy-templates.md | Existing foundations |
What ships with it
17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/evals.json 25 KB
- references/abm-playbook.md 7.9 KB
- references/ad-copy-templates.md 4.7 KB
- references/audience-targeting.md 5.9 KB
- references/audit-guardrails.md 7.9 KB
- references/b2b-paid-playbook.md 7.8 KB
- references/creative-research-automation.md 10 KB
- references/google-ads-audit-checklist.md 13 KB
- references/google-search-playbook.md 11 KB
- references/linkedin-b2b-playbook.md 9.0 KB
- references/meta-andromeda-playbook.md 8.0 KB
- references/meta-decision-system.md 15 KB
- references/operating-check.md 5.8 KB
- references/payback-period.md 6.8 KB
- references/platform-setup-checklists.md 7.4 KB
- references/reading-google-ads-data.md 8.5 KB
- references/rsa-output-spec.md 3.6 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed 520e05602d6f
- 7d ago Changed · -1 lines · +15 tokens per session 71b4f853c49f
- 11d ago Changed · +6 lines · +23 tokens per session 420447524c5b
- 12d ago Changed · +4 lines 4bf636285bec
- 18d ago Changed · -39 lines ac9e25cb762c
- 19d ago Changed · +10 tokens per session 3d4c797cf052
- 20d ago Changed 37977410e176
- 24d ago Changed · +8 lines 2bdf6f81a10f
- 25d ago Changed · +16 tokens per session 89684e286fe5
- 1mo ago First seen · 500 lines · 175 tokens per session scan A 5759f8e81a2f
ads is a skill published in the GitHub repository coreyhaines31/marketingskills (56,703 stars, last pushed yesterday), licensed MIT. It adds 239 tokens to every session and 6,920 once invoked, about $0.0010 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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