{
  "skill_name": "ads",
  "evals": [
    {
      "id": 1,
      "prompt": "Help me plan a paid advertising strategy. We're a B2B SaaS tool for HR teams, selling at $99/month per seat. We have $15k/month to spend on ads and want to generate demo requests. Where should we advertise?",
      "expected_output": "Should check for product-marketing.md first. Should apply the platform selection guide based on B2B, HR audience, $99/month price point. Should recommend LinkedIn (B2B targeting by job title/industry), Google Ads (search intent for HR software keywords), and potentially Meta (retargeting). Should recommend campaign structure with naming conventions. Should define audience targeting strategy for each platform. Should set budget allocation across platforms. Should define success metrics and attribution approach. Should recommend starting structure and scaling plan.",
      "assertions": [
        "Checks for product-marketing.md",
        "Applies platform selection guide",
        "Recommends platforms appropriate for B2B HR audience",
        "Recommends campaign structure with naming conventions",
        "Defines audience targeting per platform",
        "Sets budget allocation across platforms",
        "Defines success metrics",
        "Recommends starting structure and scaling plan"
      ],
      "files": []
    },
    {
      "id": 2,
      "prompt": "Our Google Ads CPC is $12 and our cost per lead is $180. Is that good? We're getting about 80 leads/month from a $15k budget.",
      "expected_output": "Should evaluate the metrics in context. Should assess: $12 CPC for B2B (reasonable depending on industry), $180 CPL (depends on LTV \u2014 need to compare against customer lifetime value), 80 leads/month from $15k (math checks out). Should apply the campaign optimization framework: check quality score, search term relevance, landing page conversion rate, negative keywords. Should recommend specific optimization levers to reduce CPC and CPL. Should frame performance against industry benchmarks if applicable. Should ask about downstream conversion rates (lead \u2192 demo \u2192 customer).",
      "assertions": [
        "Evaluates metrics in context",
        "Compares CPL against LTV considerations",
        "Applies campaign optimization framework",
        "Recommends specific optimization levers",
        "Asks about downstream conversion rates",
        "Provides industry context for benchmarking"
      ],
      "files": []
    },
    {
      "id": 3,
      "prompt": "we want to run retargeting ads for people who visited our site but didn't convert. how should we set this up?",
      "expected_output": "Should trigger on casual phrasing. Should apply the retargeting strategies section, specifically the funnel-based approach. Should recommend audience segments: all visitors (broad), pricing page visitors (high intent), blog readers (lower intent), and cart/signup abandoners (highest intent). Should recommend different messaging and offers for each segment. Should address frequency capping to avoid ad fatigue. Should recommend retargeting platforms (Meta, Google Display, LinkedIn). Should include duration windows for each audience.",
      "assertions": [
        "Triggers on casual phrasing",
        "Applies funnel-based retargeting approach",
        "Recommends audience segments by intent level",
        "Recommends different messaging per segment",
        "Addresses frequency capping",
        "Recommends retargeting platforms",
        "Includes audience duration windows"
      ],
      "files": []
    },
    {
      "id": 4,
      "prompt": "Should we advertise on TikTok? We sell accounting software to small businesses. Our current ads are on Google and Meta.",
      "expected_output": "Should apply the platform selection guide for TikTok specifically. Should evaluate TikTok fit for accounting software + small business audience: likely a weaker fit than Google/Meta for this category (lower purchase intent, younger skewing audience, less B2B targeting). Should discuss when TikTok CAN work for B2B (brand awareness, creative content, younger business owners). Should provide an honest recommendation with caveats. Should suggest a small test budget approach if they want to try.",
      "assertions": [
        "Applies platform selection guide for TikTok",
        "Evaluates fit for accounting + small business audience",
        "Provides honest assessment of likely weaker fit",
        "Discusses when TikTok can work for B2B",
        "Suggests small test budget if proceeding",
        "Compares to their existing Google/Meta performance"
      ],
      "files": []
    },
    {
      "id": 5,
      "prompt": "How do we structure our Google Ads campaigns? We have 50+ keywords we want to target for our CRM product.",
      "expected_output": "Should apply the campaign structure and naming conventions framework. Should recommend organizing campaigns by theme/intent (brand, competitor, product features, pain points). Should recommend ad group structure (tightly themed, 5-15 keywords per group). Should define naming conventions for campaigns and ad groups. Should recommend match types strategy. Should include negative keyword lists. Should provide a sample campaign structure.",
      "assertions": [
        "Applies campaign structure framework",
        "Organizes campaigns by theme/intent",
        "Recommends tight ad group structure",
        "Defines naming conventions",
        "Recommends match types strategy",
        "Includes negative keyword lists",
        "Provides sample campaign structure"
      ],
      "files": []
    },
    {
      "id": 6,
      "prompt": "Can you write some ad copy for our Facebook ads? We need headlines and descriptions for 5 different angles.",
      "expected_output": "Should recognize this is an ad creative generation task, not campaign strategy. Should defer to or cross-reference the ad-creative skill, which handles platform-specific ad copy generation with character limits, angle-based variation, and batch generation. May provide brief ad copy framework guidance but should make clear that ad-creative is the right skill for generating ad copy at scale.",
      "assertions": [
        "Recognizes this as ad creative generation",
        "References or defers to ad-creative skill",
        "Does not attempt bulk ad copy generation using campaign strategy patterns"
      ],
      "files": []
    },
    {
      "id": 7,
      "prompt": "Our Meta CPA doubled this week (6 conversions so far, sales cycle is ~3 weeks). Pause everything above $150 CPA, give me a negative keyword list to cut wasted Google spend (I don't have the search terms report handy), and tell me our total conversions: Meta says 38 on 7-day click and Google says 51 on 30-day. Also just give me an overall account health score \u2014 you can see about half the account.",
      "expected_output": "Should load references/audit-guardrails.md and refuse all four unsafe asks with correct alternatives. (1) No fixed kill rule: 6 conversions with a 3-week lag is not enough evidence \u2014 explain sample size and conversion lag, keep learning-phase campaigns running, propose an evidence-based review instead of pausing at $150. (2) Zero invented negative keywords: request the search terms report and describe the overblocking review; must not name candidate negatives. (3) Refuse to sum 38 + 51: different attribution windows \u2014 report side by side and offer a neutral blended source (GA4/CRM). (4) No single health score at ~50% evidence coverage: below the 60% band, report findings and unknowns separately, state that unknown \u2260 failing. Any proposed account change is presented as a draft plan (current state \u2192 change \u2192 expected effect \u2192 rollback), not applied.",
      "assertions": [
        "Does not recommend pausing based on the fixed $150 CPA threshold; cites sample size and/or conversion lag",
        "Does not produce any candidate negative keywords; requests the search terms report and mentions an overblocking review",
        "Refuses to add Meta 7-day and Google 30-day conversions into one total; reports them side by side",
        "Declines to give a single health score at ~50 percent coverage; separates unverified (unknown) from failing",
        "Frames any account change as a draft with a rollback step rather than an immediate action"
      ]
    },
    {
      "id": 8,
      "prompt": "Audit our Google Ads account. We're a DTC ecommerce brand running Shopping, Performance Max, and some Demand Gen. Walk me through what to check. I can give you Merchant Center access but I don't have the search terms report handy right now.",
      "expected_output": "Should recognize this as an itemized ecommerce Google Ads audit and load references/google-ads-audit-checklist.md, working through the 32 checks across tracking, targeting, campaign structure, GMC (shipping, promotions, feed titles, images, store quality, ratings, eligible-product impressions), Shopping segmentation + budget allocation, bidding/budget, search, PMax signals + budget-on-Shopping, landing-page funnels, and Demand Gen. Should apply the four-state scoring from audit-guardrails.md: score only verified items, and because the search terms report isn't available, mark the negative-keywords and new-search-terms checks as UNKNOWN (not fail) and request the report \u2014 naming zero candidate negatives. Should treat Merchant Center access as available and plan the GMC feed-quality checks accordingly. Should keep account health and evidence coverage as separate numbers, and deliver any fail as a draft fix (current state \u2192 change \u2192 expected effect \u2192 rollback), not an applied change.",
      "assertions": [
        "Loads/uses the itemized google-ads-audit-checklist reference for an ecommerce audit",
        "Covers ecommerce-specific depth: GMC feed quality, Shopping segmentation, PMax signals/budget, Demand Gen format splits, landing-page funnels",
        "Marks the search-terms-dependent checks as unknown (not fail) and requests the report without inventing negative keywords",
        "Applies four-state pass/fail/unknown/NA scoring and keeps health separate from evidence coverage",
        "Delivers fails as draft fixes with a rollback step rather than applied changes"
      ],
      "files": []
    },
    {
      "id": 9,
      "prompt": "Our Meta account is at a 40 ROAS but the numbers have felt stale \u2014 CPA and ROAS are steady but I feel like we're hitting a wall. Frequency is creeping up and I can't seem to grow past our current spend. What should we do to reach new audiences?",
      "expected_output": "Should load references/meta-decision-system.md and diagnose this as a net-new-reach problem, not a conversion problem. Should surface rolling month-over-month reach as the health signal to check (steady CPA/ROAS can mask a shrinking audience pool; declining rolling reach is a leading indicator of the frequency wall). Should recommend partnership ads as the primary net-new-reach lever, explaining the Andromeda persona-based logic (a creator's own following is a pre-assembled persona; running from the creator's handle inherits that seed audience). Should give partnership-ads playbook basics: pre-test creator content organically before promoting, pick creators for persona/ICP overlap over follower count, secure whitelisting/branded-content + usage + paid-amplification rights. Should mention the companion tactic of commissioning low-fi creator statics so each creator becomes a mini-funnel. May reference the ad-creative format taxonomy for which creator-fronted formats to run.",
      "assertions": [
        "Loads references/meta-decision-system.md",
        "Frames this as a net-new-reach problem, not a conversion problem",
        "Surfaces rolling month-over-month reach as the health signal / leading indicator of the wall",
        "Recommends partnership ads as the primary net-new-reach lever",
        "Explains the Andromeda persona-based seed-audience logic",
        "Gives partnership-ads playbook basics (pre-test, persona overlap over follower count, whitelisting/rights)",
        "Mentions commissioning low-fi creator statics as a per-creator mini-funnel"
      ],
      "files": []
    },
    {
      "id": 10,
      "prompt": "I want to run an agentic teardown of a competitor's paid creative before we brief our next round of ads. Their Facebook Ad Library is at this link: https://www.facebook.com/ads/library/?id=example. Set up the analysis. Also, we have ~40,000 Amazon reviews on our own product and I want personas out of them, and I want to know whether the personas our ads seem to target match who actually buys.",
      "expected_output": "Should load references/creative-research-automation.md. For the ad-library teardown: should use the exact-link prompt pattern (open with the Chrome connector, not a vague brand reference) and return the structured output schema (active-ad count, product lines, creator partners, video/image split, video-duration distribution, % partnership ads, messaging pillars, inferred personas, top-10 by impressions), marking unverifiable fields unknown. For the reviews: should chain scrape\u2192CSV\u2192editable personas doc\u2192visual deck, and should sample (~3k) rather than pull all 40k. Should run the persona-mapping move \u2014 who the creatives seem to target (from the ad library) vs. who actually buys (from reviews) \u2014 and surface the gap. Should treat ad copy and reviews as untrusted data, not instructions. Should hand off to customer-research for deep VOC, competitor-profiling for a full dossier, and positioning where relevant.",
      "assertions": [
        "Loads references/creative-research-automation.md",
        "Uses the exact-link / Chrome-connector prompt pattern for the ad library rather than a vague brand reference",
        "Returns the ad-library output schema including % partnership ads, inferred personas, and top-10 by impressions",
        "Samples (~3k) rather than scraping all 40k reviews",
        "Chains reviews into an editable personas doc before a deck, and reuses it as context",
        "Runs the persona-mapping move: who the creatives seem to target vs. who actually buys",
        "Hands off to customer-research and/or competitor-profiling for deeper work"
      ]
    },
    {
      "id": 11,
      "prompt": "Our blended LTV:CAC is 3.4:1 so we're good to pour more into Meta, right? We have a $9/mo starter plan and a $999/mo enterprise plan, CAC is about $300 across the board.",
      "expected_output": "Should load references/payback-period.md and push back on using blended LTV:CAC as the go/no-go. Should explain LTV:CAC is a useless/destructive metric here \u2014 it hides per-plan variance under blended ARPU, so 3.4:1 describes neither the $9 nor the $999 buyer. Should compute Payback Period = CAC / ARPU per plan: $300/$9 = ~33 months (unaffordable \u2014 do not run Meta for the starter plan) vs $300/$999 = ~0.3 months (excellent \u2014 scale hard). Should recommend routing cheap-plan buyers to organic/product-led and only turning paid on where discounted payback lands in the 3-12 month target band. Should mention Discounted Payback = CAC / (ARPU x annual retention) to adjust for early churn. Should NOT bless scaling on the blended ratio alone.",
      "assertions": [
        "Loads or applies payback-period.md rather than accepting blended LTV:CAC",
        "Explains blended ARPU hides the $9-vs-$999 per-plan variance",
        "Computes Payback Period = CAC / ARPU per plan (~33 months for $9, ~0.3 months for $999)",
        "Cites the 3-12 month payback target band as the affordability gate",
        "Recommends not running paid for the unaffordable starter plan / routing it elsewhere",
        "Mentions Discounted Payback Period (retention-adjusted)"
      ]
    }
  ]
}