Best Skills (2026)

1

Manage Headers (Grade A)

Security-tested development skill for Claude AI. Grade A. Inspects and configures the security headers a Power Pages site sends to browsers — Content Security Policy, frame and clickjacking protection

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Manage Headers (Grade A) is a security-tested development skill for Claude AI. It is designed to inspect and configure the security headers a Power Pages site sends to browsers, including Content Security Policy, frame and clickjacking protection, cross-origin sharing, cookie behavior, and related site settings. The skill identifies gaps and walks the user through fixes, making it useful for tasks such as reviewing headers, fixing CSP errors, controlling cross-origin access, hardening cookie settings, or checking whether browser settings are safe. It can be used when the user wants to achieve various security-related goals without specifically mentioning 'security headers.'

• Inspects current security headers
• Configures Content Security Policy
• Enables frame and clickjacking protection
• Manages cross-origin sharing and cookie behavior

2

Using Git Worktrees (Grade A)

Security-tested data-ai skill for Claude AI. Grade A. Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktre

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Use when starting feature work that needs isolation from current workspace or before executing implementation plans. This skill creates isolated git worktrees with smart directory selection and safety verification. Git worktrees create isolated workspaces sharing the same repository, allowing work on multiple branches simultaneously without switching. The core principle is systematic directory selection plus safety verification, leading to reliable isolation. The directory selection process follows these steps: 1. Check for existing directories with priority order: hidden ".worktrees" directory, then "worktrees" directory. If found, use that one. 2. Check if a preference for a worktree directory is specified in the "CLAUDE.md" file. 3. If no directory exists and no CLAUDE.md preference is found, ask the user to choose a directory. The safety verification step is critical and involves checking if the chosen directory is ignored by Git. If not ignored, the skill will add the directory to the ".gitignore" file and commit the changes. Creation steps include: 1. Detecting the project name and creating a full path for the worktree. 2. Creating the worktree with a new branch. 3. Running project setup, which auto-detects and runs the appropriate setup commands (e.g., installing dependencies) based on the project type (Node.js, Rust, Python, or Go). 4. Verifying a clean baseline by running tests. If tests fail, report failures and ask whether to proceed or investigate. 5. Reporting the worktree location and its readiness to implement a feature.

• Isolated workspace creation
• Smart directory selection
• Safety verification
• Automated project setup

3

Ga4 Bigquery Schema (Grade A)

Security-tested data-ai skill for Claude AI. Grade A. GA4 BigQuery Export Schema Reference — complete field reference, nested structures, query patterns, and performance tips

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GA4 BigQuery Export Schema Reference is a security-tested data-ai skill for Claude AI. It provides a complete field reference, nested structures, query patterns, and performance tips for Google Analytics 4 BigQuery export schema. The tool helps users understand the GA4 export schema, write correct BigQuery SQL queries against GA4 data, and follow performance best practices.

• Complete field reference
• Nested structures
• Query patterns
• Performance tips
• Ready-to-use SQL examples

4

Meta Capi (Grade A)

Security-tested data-ai skill for Claude AI. Grade A. Meta Conversions API (CAPI) Setup Reference — architecture, event types, customer information hashing, deduplication, implementation examples, AEM

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Meta Capi is a security-tested data-ai skill for Claude AI, providing a comprehensive reference for setting up Meta Conversions API (CAPI). It covers architecture, event types, customer information hashing, deduplication, implementation examples, and Aggregated Event Measurement. The tool helps users implement CAPI correctly, debug event tracking issues, improve Event Match Quality, and configure deduplication. It offers precise answers with ready-to-use code examples and can utilize Cogny MCP tools to inspect user configurations and provide contextual recommendations.

• CAPI architecture overview
• Event type parameters
• Customer information hashing
• Deduplication strategies
• Aggregated Event Measurement
• Node.js implementation example

5

Callees (Grade A)

Claude AI

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Callees (Grade A) Claude AI . , , , . /plugadvpl:callees , , , . plugadvpl .

• (native ERP, , , )
• (, Markdown)

6

Test Module Name (Grade A)

Security-tested data-ai skill for Claude AI. Grade A. Name Haskell test modules after the module under test with a Spec suffix in the same namespace. Use when writing or reviewing Haskell test module

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Name Haskell test modules after the module under test with a Spec suffix in the same namespace. This is done to clearly separate library and test directories, making it easier to identify dependencies and relationships between modules. In particular, this best practice is beneficial when writing or reviewing Haskell test module names or test file organization. For example, a test module named Env.TypeSpec should be placed in the same namespace as the module it is testing, Env.Type. This ensures that the relationships between modules are clear, and it is particularly useful for modules with complex behavior, such as those that depend on multiple modules or have no dependencies on specific modules.

• Suggests test module names based on the module under test
• Appends 'Spec' to the module name
• Same namespace as the module under test

7

Board Of Directors (Grade A)

Claude AI

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5 . , , , . 5 : CA), CPO), CSO), COO), CXO). , . , , .

• 5
• CA, CPO, CSO, COO, CXO)

8

Advpl Mvc Avancado (Grade A)

Claude AI

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Advpl Mvc Avancado (Grade A) Claude AI . MVC (CNTA300/MATA070/MATA440/MATA460/FINA040 via *STRU) , , , . , Ponto de Entrada (A300STRU, MA440STRU, etc.) , , , , , MATXFIS, MsNewGetDados,

• Entry Points (PE)
• ,
• ,
• (e.g., MATA010, MATA070, MATA440)

9

Advpl Fundamentals (Grade A)

Claude AI

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AdvPL (Grade A) AdvPL/TLPP , Hungarian notation, naming convention, Local/Static/Private/Public , .p 10 .

• Hungarian notation
• Naming conventions
• Local/Static/Private/Public
• 10 .prw/.prx

Workflow Engine (Grade A) is a utility skill that helps with defining a machine-readable workflow DAG for the multi-step agent pipeline. It enables the creation of pipelines with node types, edge conditions, gates, and fan-out patterns. This skill is designed to meet the requirements of a Grade A security-tested environment, suggesting that it is well-suited for use in a high-security devops environment with Claude AI.

• Machine-readable workflow DAG
• Node type definitions
• Edge condition management
• Gate and fan-out pattern support
• Multi-step agent pipeline management