Claude Marketing Skills: How to Build Custom AI Tools for Campaign Analysis in 2026

Marketing operations teams are building custom AI skills for Claude to solve problems that generic chatbots can't touch. These skills connect directly to live campaign data, apply business rules specific to your attribution model, and answer questions like "Which campaigns drove the most pipeline last quarter, and what did we spend to get there?" in seconds instead of days.

The difference between a generic AI assistant and a custom Claude skill built for marketing is the difference between asking "How do I calculate CAC?" and asking "What's our actual CAC for enterprise accounts in EMEA this month, broken out by channel?" The first gives you a textbook answer. The second gives you a number you can act on.

This guide shows you how to build Claude marketing skills that marketing operations managers actually use: skills that query unified data, enforce governance rules, and surface insights without requiring a data science team. You'll see real implementation patterns, deployment examples, and the infrastructure decisions that determine whether your skill becomes indispensable or gets abandoned after the first week.

Key Takeaways

✓ Custom Claude skills outperform generic AI assistants because they query your live marketing warehouse, apply your attribution model, and enforce governance rules consistently — generic chatbots can do none of these.

✓ The highest-value use cases are cross-channel attribution, budget pacing alerts, and campaign naming validation — questions too complex for static dashboards and too time-sensitive to wait for manual analysis.

✓ Business logic (attribution model, channel taxonomy, naming conventions) must be encoded as queryable metadata. Claude cannot infer it from context — every team that skipped this step ended up with skills that gave confidently wrong answers.

✓ Skills break the moment a platform renames a field unless you put an abstraction layer between Claude and raw platform data. The unified schema, not the skill itself, absorbs API changes.

✓ Three deployment patterns dominate production usage: ad-hoc query agents (exploratory analysis), scheduled insight bots (proactive alerts), and pre-launch validation tools (campaign governance).

✓ Without a unified marketing data warehouse and documented business rules, building a Claude skill exposes how fragmented your stack is rather than fixing it — invest in foundational infrastructure first.

✓ Trust dies fast: validate every skill against known-correct data sets, test business logic consistency across query phrasings, and stress-test error handling on ambiguous inputs before you ship.

✓ You don't have to start from scratch. Improvado's campaign-launcher-oss is a working open-source skill you can fork today — and it's one of 11+ public Claude marketing skill collections mapped further down this article.

What Claude Marketing Skills Are (and Why They Matter Now)

A Claude marketing skill is a custom-built tool that lets Claude access, analyze, and act on your marketing data using natural language queries. Unlike general-purpose AI assistants that work with static information, marketing skills connect directly to live data sources — your ad platforms, CRM, attribution models, and marketing data warehouse.

The breakthrough happened when Anthropic released Claude Code in early 2026. Enterprise adoption jumped: business subscription share reached 24.4%, and the enterprise market share rose from 18% to 29%. Claude Code is now on a multi-billion-dollar revenue run rate because it solved a specific problem — AI that can actually do technical work, not just talk about it.

Marketing operations teams saw the potential immediately. Instead of waiting for a data analyst to write SQL queries or build custom dashboards, they could build Claude skills that let anyone on the team ask questions like:

• "Which paid social campaigns generated the most MQLs last month, and what did we spend per MQL?"

• "Show me attribution breakdown for all deals that closed in Q4, grouped by first-touch channel."

• "What's our current budget pacing across all channels, and which campaigns are overspending?"

The skill queries your data warehouse, applies your attribution model, enforces your governance rules, and returns an answer in seconds. No spreadsheets, no tickets to the data team, no waiting three days for a report.

Why Generic AI Assistants Fail for Marketing Operations

Generic AI tools fail marketing operations teams because they don't understand your specific data structure, attribution logic, or business rules. When you ask ChatGPT or a general-purpose AI assistant about campaign performance, it can only give you conceptual advice or force you to manually upload data extracts.

The problems compound fast:

• No live data access. Generic AI can't query your marketing data warehouse, so every answer requires manual data exports.

• No context retention. It doesn't know your attribution model, channel taxonomy, or campaign naming conventions.

• No governance enforcement. It can't apply your data quality rules or validate that calculations match your internal definitions.

• No historical continuity. When a platform changes its API or a metric gets redefined, generic AI has no way to reconcile historical data.

Marketing operations teams need AI that understands their specific data landscape. That's what custom Claude skills provide.

What Makes Claude Code Different for Marketing Teams

Claude Code introduced extended context windows and the ability to execute code against live data sources. For marketing operations, this means you can build skills that:

• Query your marketing data warehouse directly using natural language

• Apply business logic and attribution rules consistently across all queries

• Return formatted results — tables, charts, or raw data — based on what the user needs

• Preserve context across multiple queries so follow-up questions work naturally

The technical foundation matters because marketing data is messy. Platforms change their schemas, metrics get redefined, and attribution models evolve. A Claude skill built properly handles these changes without breaking.

Use Cases Where Custom Claude Skills Outperform Standard Tools

Not every marketing task needs a custom Claude skill. Some problems are better solved with dashboards, scheduled reports, or existing BI tools. But there's a category of questions that are too complex for static dashboards and too time-sensitive to wait for manual analysis.

Cross-Channel Attribution Analysis

Attribution is one of the highest-value use cases for custom Claude skills because it requires combining data from multiple sources, applying complex business logic, and answering follow-up questions that change based on the initial findings.

A marketing operations manager might ask: "Which channels contributed to our top 10 deals last quarter, using W-shaped attribution?" A Claude skill can:

• Query the unified data warehouse for all touchpoints across those 10 deals

• Apply the W-shaped attribution model (40% first touch, 40% opportunity creation, 20% distributed across touches in between)

• Return a table showing channel contribution by deal, total spend per channel, and cost per attributed dollar

The follow-up question matters just as much: "Now show me the same breakdown but exclude retargeting touches." The skill adjusts the calculation instantly. No new SQL query, no waiting for a data analyst, no rebuilding a dashboard.

Budget Pacing and Spend Alerts

Marketing teams need to know if they're on track to hit budget targets, but they don't need a dashboard for it — they need an answer when they ask. A Claude skill can query current spend across all channels, compare it to planned budgets, and flag campaigns that are pacing too fast or too slow.

The skill can also enforce business rules that aren't built into ad platforms. For example: "Show me all campaigns where cost per MQL is above our cost-per-MQL threshold and suggest budget reallocation."

Campaign Naming Validation and Governance

Marketing operations teams spend hours cleaning up campaign naming errors — missing UTM parameters, inconsistent taxonomy, typos in source/medium tags. A Claude skill can validate campaign names against your naming convention rules before campaigns launch.

The skill checks:

• UTM parameter completeness (source, medium, campaign, content, term)

• Adherence to your taxonomy (approved channel names, region codes, product lines)

• Consistency with budget allocation (campaigns that don't map to approved budget line items get flagged)

This happens in real-time, not after campaigns have been running for weeks with bad data.

How to Build Claude Skills That Connect to Marketing Data

Building a Claude skill that works reliably with marketing data requires solving three problems: data access, business logic enforcement, and schema change resilience. Marketing operations teams that skip any of these steps end up with skills that break when platforms update their APIs or return incorrect results when attribution models change.

Data Access: Connecting Claude to Your Marketing Warehouse

A Claude skill can't query data it can't reach. The first step is establishing a secure connection between Claude and your marketing data warehouse — Snowflake, BigQuery, Redshift, or whatever platform you use to store unified marketing data.

Most marketing operations teams use an API layer between Claude and the warehouse rather than giving Claude direct database credentials. This API layer:

• Enforces access controls (which users can query which data)

• Rate-limits queries to prevent accidental resource exhaustion

• Logs all queries for audit and debugging purposes

• Validates queries before execution to catch syntax errors or dangerous operations

The technical pattern is straightforward: Claude generates a SQL query based on the user's natural language request, sends it to the API layer, receives the results, and formats them for the user. The API layer handles authentication, query validation, and execution.

Business Logic Enforcement: Teaching Claude Your Rules

The hardest part of building marketing skills isn't connecting to data — it's ensuring Claude applies your specific business rules consistently. Your attribution model, channel taxonomy, campaign naming conventions, and data quality thresholds are not generic. They're specific to your company, and they change over time.

This is where most custom AI implementations fail. Teams assume Claude will "figure out" their business logic from context, but it can't. You need to explicitly encode rules.

The most reliable approach is storing business rules as structured metadata that Claude can query before executing any analysis:

When a user asks, "What's our cost per MQL for paid social last month?", the Claude skill:

1. Queries the channel taxonomy to confirm "paid social" maps to specific platforms (Facebook, LinkedIn, etc.)

2. Queries the MQL definition to ensure it's using the current scoring criteria (not an outdated definition)

3. Constructs a SQL query that calculates cost per MQL using the approved formula

4. Executes the query and returns results

5. Checks results against data quality thresholds and flags any anomalies

This process ensures every answer is consistent with your current business logic, even if that logic changed last week.

Schema Change Resilience: Handling Platform Updates

Marketing platforms change their APIs constantly. Facebook renames metrics, Google Ads deprecates fields, Salesforce adds new objects. If your Claude skill is hardcoded to expect specific column names or data structures, it breaks every time a platform updates.

The solution is abstraction. Instead of having Claude query raw platform data directly, you build a unified schema — a consistent data model that maps platform-specific fields to standardized names your business uses.

When Facebook renames "link_clicks" to "outbound_clicks", you update the mapping in your unified schema. Claude continues querying "clicks" (your internal name), and the mapping layer translates that to whatever Facebook currently calls it.

This approach also preserves historical data. When a metric gets redefined, you store both the old and new versions, tagged with the date range each definition was active. Claude can then answer questions like, "Compare Q4 2025 and Q4 2026 performance using the current definition of MQL, not the old one."

Infrastructure Requirements for Claude Marketing Skills

Marketing operations teams building Claude skills need infrastructure that handles three things well: data unification, query execution, and governance enforcement. The specific tools matter less than the architecture — you need a pipeline that brings marketing data from 1,000+ sources into a queryable format, applies business rules consistently, and updates fast enough that users get near-real-time answers.

Data Unification Layer

Claude can't query data that's scattered across Google Ads, Meta, Salesforce, HubSpot, and 20 other platforms. It needs a unified marketing data warehouse where all platform data is normalized, deduplicated, and mapped to a consistent schema.

Building this layer internally takes months. You need:

• Connectors to every marketing and sales platform you use (ad platforms, social media, CRM, marketing automation, web analytics, etc.)

• ETL pipelines that extract data on a schedule fast enough to support near-real-time queries

• Schema mapping logic that translates platform-specific field names to your internal taxonomy

• Deduplication and data quality checks to catch errors before they contaminate your warehouse

• Historical data preservation so schema changes don't break time-series comparisons

Most marketing operations teams use a marketing data integration platform rather than building this from scratch. The alternative is assigning engineering resources full-time to connector maintenance, which doesn't scale.

Query Execution and API Layer

Once data is unified, Claude needs a way to query it securely. You don't give Claude direct database credentials — you build an API layer that accepts natural language or SQL queries, validates them, executes them against the warehouse, and returns results.

This layer handles:

• Authentication and access control. Which users can query which data.

• Query validation. Catching syntax errors, dangerous operations (DROP TABLE), or queries that would exhaust resources.

• Rate limiting. Preventing accidental runaway queries from overwhelming the warehouse.

• Logging and audit trails. Recording every query for debugging and compliance.

The API layer is where you enforce governance rules. Before executing a query, the layer checks that the user has permission to access the requested data, that the query adheres to data quality standards, and that results will be returned in a format the user can act on.

Governance and Rule Enforcement

Marketing data governance isn't optional when you're building AI skills. Without governance, Claude might return answers that are technically correct but strategically wrong — calculating CAC using an outdated MQL definition, attributing revenue to campaigns that shouldn't receive credit, or ignoring data quality issues that invalidate the results.

Governance requires three things:

1. Pre-built validation rules. Check that campaign names follow conventions, UTM parameters are complete, and budget allocations map to approved line items before data enters the warehouse.

2. Business logic versioning. Track when attribution models, MQL definitions, or other business rules changed, so historical queries use the logic that was active at the time.

3. Data quality monitoring. Flag anomalies (cost per click spikes 10x, conversion rate drops to zero, spend exceeds budget by 200%) and surface them in Claude responses so users know when results are suspect.

Marketing operations teams that skip governance end up with Claude skills that give confidently wrong answers. Users lose trust fast, and the skill gets abandoned.

Real Implementation Patterns for Marketing Claude Skills

Marketing operations teams deploy Claude skills using three common patterns: ad-hoc query agents, scheduled insight bots, and pre-launch validation tools. Each pattern solves a different problem, and each requires different infrastructure decisions.

Ad-Hoc Query Agents

This is the most common pattern. A marketing manager opens Claude and asks a question about campaign performance, budget pacing, or attribution. The skill queries the data warehouse, applies business logic, and returns an answer in seconds.

The user experience feels like chatting with a data analyst who has instant access to all your marketing data and never forgets your attribution model or campaign naming conventions.

Implementation requirements:

• Unified marketing data warehouse (Snowflake, BigQuery, Redshift)

• API layer that accepts natural language queries and translates them to SQL

• Business logic stored as queryable metadata (attribution models, channel taxonomy, etc.)

• Authentication system that controls which users can access which data

Example query flow:

1. User asks: "What's our cost per pipeline dollar for paid search last quarter, broken out by region?"

2. Claude queries the channel taxonomy to map "paid search" to specific platforms (Google Ads, Bing Ads)

3. Claude queries the attribution model config to determine how to weight touchpoints

4. Claude generates SQL that calculates spend, attributed pipeline, and cost per dollar for each region

5. API layer validates and executes the query

6. Claude formats results as a table and returns them to the user

This pattern works best for questions that change frequently — exploratory analysis, one-off investigations, and follow-up questions that depend on previous answers.

Scheduled Insight Bots

Some questions need to be answered on a schedule, not just when someone asks. A Claude skill can run daily, weekly, or monthly, surface insights proactively, and alert teams when something needs attention.

Example use cases:

• Daily budget pacing report: "Which campaigns are pacing above 110% of budget, and which are underspending?"

• Weekly anomaly alert: "Flag any campaigns where cost per conversion increased more than 30% week-over-week."

• Monthly attribution summary: "Top 10 channels by attributed pipeline, with spend and ROI."

Implementation requirements are similar to ad-hoc agents, with two additions:

• Scheduler that triggers Claude skills at specified intervals

• Distribution system that delivers insights to Slack, email, or a dashboard

The advantage of scheduled bots is that teams don't need to remember to ask questions. The skill surfaces insights automatically, and users only engage when action is needed.

Pre-Launch Validation Tools

Marketing operations teams can use Claude skills to validate campaigns before they launch. Instead of cleaning up naming errors and UTM mistakes after campaigns run for weeks, the skill catches problems during setup.

A pre-launch validation skill checks:

• UTM parameter completeness (source, medium, campaign, content, term)

• Campaign name adherence to taxonomy (approved channel names, region codes, product identifiers)

• Budget allocation mapping (does this campaign map to an approved budget line item?)

• Duplicate detection (is there already a campaign with this name or UTM combination?)

The skill returns a pass/fail result with specific errors flagged. Users fix errors before launching, not after data is already polluted.

This pattern requires tight integration with campaign creation workflows — either API access to ad platforms or a UI layer where users submit campaign details for validation before creating campaigns in the platform.

How to Test and Validate Claude Skills Before Deployment

Marketing operations teams can't afford to deploy Claude skills that return incorrect results. The trust cost is too high — one bad attribution calculation or one budget pacing report with wrong numbers, and users stop relying on the skill entirely.

Testing Claude skills requires validating three things: query accuracy, business logic consistency, and error handling.

Query Accuracy Testing

Before deploying a skill, run test queries against known data sets where you already know the correct answer. For example:

• Query: "What was our total spend on Google Ads in Q4 2025?"

• Expected answer: $387,240 (verified manually or from Google Ads UI)

• Skill answer: $387,240 ✓

Run 20–30 test queries covering common use cases (spend by channel, cost per conversion by campaign, attribution by region, etc.). If any result deviates from the known correct answer, debug the SQL generation or business logic before deploying.

Business Logic Consistency Testing

Test that the skill applies your business rules consistently across different query formulations. For example:

• Query 1: "What's our W-shaped attribution for paid social in Q4?"

• Query 2: "Show me Q4 attribution for Facebook and LinkedIn using the W-shaped model."

Both queries should return identical results because they're asking the same question using different phrasing. If results differ, the skill isn't applying business logic consistently.

Error Handling Testing

Test how the skill responds to ambiguous, incomplete, or invalid queries:

• Ambiguous query: "What's our spend?" (missing time range, missing channel) → Skill should ask clarifying questions.

• Invalid query: "Show me cost per MQL for a channel that doesn't exist." → Skill should return an error, not an empty result.

• Data quality issue: Query returns results that fall outside acceptable thresholds (CAC spikes 10x). → Skill should flag the anomaly and suggest checking data sources.

Error handling determines whether users trust the skill when something goes wrong. A skill that silently returns wrong answers is worse than no skill at all.

Limitations and When Not to Use Claude Skills

Custom Claude skills are not the right solution for every marketing operations problem. Some tasks are better handled by dashboards, scheduled reports, or manual analysis. Understanding when not to use a Claude skill saves time and avoids building tools that don't get adopted.

When Dashboards Are Better

If the same question gets asked every day by the same people, build a dashboard instead of a Claude skill. Dashboards are better for:

• Daily performance monitoring (today's spend, conversion rate, cost per lead)

• Visual trend analysis (line charts, time-series comparisons)

• Shared visibility across teams (CMO wants a live view of budget pacing)

Claude skills excel at answering questions that change frequently or require follow-up exploration. They don't replace dashboards — they complement them.

When You're Missing Foundational Infrastructure

Claude skills require unified marketing data. If your data is still scattered across platforms with no centralized warehouse, building a skill won't solve the problem — it will just expose how fragmented your data is.

Before building Claude skills, you need:

• A marketing data warehouse (Snowflake, BigQuery, Redshift)

• Connectors that bring data from all platforms into the warehouse

• A unified schema that normalizes field names and data types

• Data quality checks that catch errors before they contaminate the warehouse

If you don't have these foundational pieces, invest in data infrastructure first. Otherwise, the Claude skill will return inconsistent or incorrect answers because the underlying data is unreliable.

When Business Logic Is Still Undefined

If your team doesn't have a documented attribution model, channel taxonomy, or campaign naming convention, a Claude skill can't enforce rules that don't exist. The skill will default to generic calculations, and results won't align with how your team actually measures performance.

Document your business logic first. Then encode it into the skill. Trying to do both simultaneously leads to skills that give different answers depending on when you ask the same question.

Other Open-Source Claude Marketing Skill Collections

Improvado’s campaign-launcher-oss is one entry in a growing ecosystem of public Claude Code skill libraries. Marketing operations teams building their own skills often start by reading other people’s implementations — the table below maps the most active public collections relevant to marketing, advertising, and revenue operations.

Star counts shown reflect snapshots taken in April 2026. The marketing-skill ecosystem is moving fast — expect new collections monthly. The pattern is consistent across all of them: skills are markdown files describing inputs, business logic, and outputs that any Claude-compatible agent can execute.

Conclusion

Custom Claude marketing skills turn AI from a novelty into operational infrastructure. Marketing operations teams use them to query unified data, enforce governance rules, and answer complex attribution questions in seconds instead of days. The difference between a skill that gets adopted and one that gets abandoned comes down to data quality, business logic enforcement, and schema change resilience.

The teams seeing results aren't building generic chatbots. They're building skills that connect to live marketing data, apply their specific attribution models and channel taxonomies, and handle platform updates without breaking. They're solving problems that dashboards can't touch — exploratory analysis, follow-up questions that change based on the previous answer, and validation checks that catch errors before campaigns launch.

If you're considering building Claude skills for marketing operations, start with one high-value use case: cross-channel attribution, budget pacing alerts, or campaign naming validation. Test it against known data sets, validate that business logic is applied consistently, and deploy it to a small group before rolling it out widely. The infrastructure investment — unified data warehouse, API layer, governance rules — pays off across every skill you build after the first one.

FAQ

What is a Claude marketing skill?

A Claude marketing skill is a custom-built AI tool that connects Claude to your marketing data sources and lets you query performance, attribution, and budget data using natural language. Unlike generic AI assistants, marketing skills access live data from your warehouse, apply your specific attribution model and business rules, and return answers based on your actual campaign performance — not generic best practices. Marketing operations teams build skills to answer questions like "What's our cost per MQL for paid social last quarter?" or "Which campaigns are overspending this month?" without writing SQL or waiting for a data analyst.

How are Claude marketing skills different from ChatGPT or other AI tools?

The core difference is data access and business logic enforcement. ChatGPT and most general-purpose AI tools can't query your marketing data warehouse — they only work with information you manually provide or with generic knowledge. Claude marketing skills connect directly to live data sources, apply your attribution model and governance rules, and return results based on your specific campaign performance. They also preserve context across follow-up questions, so you can ask "Now show me the same breakdown but exclude retargeting" and the skill adjusts the calculation without starting over.

What infrastructure do I need to build Claude marketing skills?

You need three foundational pieces: a marketing data warehouse (Snowflake, BigQuery, Redshift, etc.) that unifies data from all your platforms, an API layer that securely connects Claude to the warehouse and validates queries before execution, and documented business logic (attribution model, channel taxonomy, campaign naming conventions) stored as queryable metadata. Most teams also need connectors that extract data from ad platforms, CRM, and analytics tools and load it into the warehouse on a schedule fast enough to support near-real-time queries. Building this infrastructure from scratch takes months, which is why most marketing operations teams use a marketing data integration platform instead.

Can Claude skills enforce marketing data governance rules?

Yes, if you explicitly encode governance rules as queryable metadata. Claude can't infer your business rules from context — you need to store them as structured data (JSON configs, reference tables, validation code) that the skill queries before executing any analysis. For example, you can store your campaign naming convention as regex patterns and validation rules, and the skill will check campaign names against those rules before data enters the warehouse. You can also store data quality thresholds (acceptable ranges for CAC, conversion rate, etc.) and have the skill flag anomalies when query results fall outside those ranges. Governance enforcement is what separates a reliable skill from one that gives confidently wrong answers.

How do Claude skills handle platform API changes?

The best approach is abstraction — building a unified schema that maps platform-specific field names to standardized names your business uses. When a platform renames a metric (Facebook changes "link_clicks" to "outbound_clicks"), you update the mapping in your schema without changing the skill. Claude continues querying "clicks" (your internal name), and the mapping layer translates that to whatever the platform currently calls it. This approach also preserves historical data — when a metric gets redefined, you store both the old and new versions tagged with date ranges, so historical queries use the definition that was active at the time. Without abstraction, your skill breaks every time a platform updates its API.

What marketing use cases are best for Claude skills vs. dashboards?

Claude skills are best for questions that change frequently, require follow-up exploration, or involve complex business logic that's hard to visualize in a dashboard. Examples: cross-channel attribution analysis ("Show me W-shaped attribution for Q4, then exclude retargeting"), budget pacing alerts ("Which campaigns are overspending and what should we reallocate?"), and campaign naming validation ("Check this UTM string for completeness and taxonomy adherence"). Dashboards are better for questions that get asked every day by the same people: daily spend monitoring, visual trend analysis, and shared visibility across teams. Skills complement dashboards — they don't replace them.

How long does it take to build a custom Claude marketing skill?

If you already have unified marketing data and documented business logic, building a basic skill takes a few days — you need to set up the API layer that connects Claude to your warehouse, encode business rules as queryable metadata, and test query accuracy. The hard part is the foundational infrastructure: unifying data from 1,000+ sources, building connectors, normalizing schemas, and implementing governance rules. That work takes months if you build it internally. Most marketing operations teams use a data integration platform to handle the infrastructure and focus on building skills on top of reliable, unified data.

Can Claude skills work with real-time marketing data?

Yes, if your data pipeline updates frequently enough. Real-time doesn't mean instant — it means data is fresh enough that decisions based on it are still relevant. For most marketing operations use cases, "real-time" means data updated every 15 minutes to 1 hour. Budget pacing alerts, anomaly detection, and spend monitoring benefit from near-real-time data. Attribution analysis and historical trend comparisons don't — day-old data is fine. The constraint is usually your ETL pipeline, not Claude. If your connectors pull data from ad platforms every hour, Claude can query that data as soon as it lands in the warehouse.

What are the most common mistakes when building Claude marketing skills?

The most common mistake is assuming Claude will infer your business logic from context. It can't. You need to explicitly encode attribution models, channel taxonomies, and validation rules as queryable metadata, or the skill will default to generic calculations that don't match how your team measures performance. The second mistake is skipping error handling — users lose trust fast when a skill silently returns wrong answers or breaks on ambiguous queries. The third mistake is building skills before your data is unified — if data is scattered across platforms with no consistent schema, the skill will just expose how fragmented your infrastructure is.

How do I test a Claude marketing skill before deploying it?

Run test queries against known data sets where you already know the correct answer. For example, manually verify total Google Ads spend for a specific quarter, then ask the skill the same question and confirm the result matches. Test 20–30 common queries covering spend by channel, cost per conversion, attribution by region, and budget pacing. Also test business logic consistency — ask the same question using different phrasing and confirm the skill returns identical results. Finally, test error handling: give the skill ambiguous queries (missing time range or channel), invalid queries (asking about a channel that doesn't exist), and queries that return anomalous results (CAC spikes 10x). The skill should ask clarifying questions, return helpful errors, and flag data quality issues — not silently fail or return wrong answers.