Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/matteotitta/genesys-skillsnpx agentmods add skills/matteotitta/genesys-skills/paid-strategyWrote 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/matteotitta/genesys-skills/paid-strategy)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/paid-strategy"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/paid-strategy/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/matteotitta/genesys-skills/paid-strategy"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/paid-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00030 | $0.03093 |
| Opus 5 | $0.00015 | $0.01546 |
| Sonnet 5 | $0.00006 | $0.00619 |
| Haiku 4.5 | $0.00003 | $0.00309 |
Grade A, and why
paid-campaign-strategy 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 9d ago.
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 — 305 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paid Campaign Strategy
Design paid advertising campaign architecture for B2B SaaS. Determines which campaigns to run, budget allocation, targeting strategy, and account structure across Google Ads and LinkedIn Ads. This is the upstream skill that feeds all copy and creative skills.
Live LinkedIn Ads data (optional): when the linkedin-ads MCP is authenticated, ground the strategy in the account's actual performance — read current spend, CPL, audience response, and top creatives (get_campaign_performance, get_audience_demographics, compare_performance) before allocating budget or setting KPI targets. Reads are free; write tools gated by .claude/rules/linkedin-ads-spend.md. See .claude/mcp/linkedin-ads/README.md.
Process Flowchart
┌──────────────────────────────────────────────────────────────┐
│ PAID CAMPAIGN STRATEGY PROCESS │
└──────────────────────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ INPUT VALIDATION │
│ Required: │
│ □ product-messaging (value props, differentiators) │
│ □ icp-behavioural (personas, firmographics, pain points) │
│ Optional: competitor-research, funnel-strategy, company-context│
│ □ Monthly budget range │
│ □ Campaign objective (awareness / leads / demos / trials) │
│ → If missing: Suggest upstream skills first │
└──────────────────────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ PHASE 1: PLATFORM SELECTION │
│ Step 1.1: Apply platform selection matrix │
│ Step 1.2: Determine primary vs support platform │
│ Step 1.3: Allocate budget split across platforms │
│ → Output: Platform recommendation with rationale │
│ ✓ Checkpoint: Platform(s) selected with budget split │
└──────────────────────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ PHASE 2: CAMPAIGN ARCHITECTURE │
│ Step 2.1: Google Ads — select from 5-pillar model │
│ Step 2.2: LinkedIn Ads — select funnel stages │
│ Step 2.3: Map campaigns to objectives and budget tiers │
│ Step 2.4: Define ad group structure per campaign │
│ → Output: Campaign list with objectives and budget │
│ ✓ Checkpoint: Every campaign has objective + budget + KPI │
└──────────────────────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ PHASE 3: TARGETING + TRACKING │
│ Step 3.1: Map ICP → Google keyword strategy │
│ Step 3.2: Map ICP → LinkedIn audience filters │
│ Step 3.3: Define UTM taxonomy │
│ Step 3.4: Define negative keyword seed list (Google) │
│ Step 3.5: Set KPI targets per campaign │
│ → Output: Targeting specs + UTM structure + KPI targets │
│ ✓ Checkpoint: Every campaign has targeting + tracking defined │
└──────────────────────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ SELF-EVALUATION │
│ □ Budget allocation totals to 100% │
│ □ Every campaign has objective, budget, targeting, KPI │
│ □ Platform selection justified by ICP signals │
│ □ Negative keywords included for Google │
│ □ UTM taxonomy defined and consistent │
│ □ Timeline accounts for 60-90 day B2B learning window │
└──────────────────────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ CHAIN SUGGESTIONS │
│ → google-ads-copy (generate RSA copy per campaign) │
│ → linkedin-ads-copy (generate ad copy per funnel stage) │
│ → ad-creative-brief (visual direction for designers) │
└──────────────────────────────────────────────────────────────┘
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.
- 9d ago First seen · 305 lines · 145 tokens per session scan A 5fdff604f956
paid-campaign-strategy is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 3,093 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
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