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Inspect suspicious PDFs without opening them in a reader. Use PDFiD, pdf-parser, and peepdf to examine internal objects, extract scripts, embedded files, and URLs, and document how the suspected attack works.

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• Triage suspicious PDF email attachments
• Investigate embedded JavaScript and automatic actions
• Extract embedded files and external URLs

Coordinate specialized agents in Claude Code to divide development and review tasks, pass findings between stages, and combine results into one report with prioritized recommendations and action items.

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• Reviewing code from security, performance, and quality perspectives
• Coordinating frontend, backend, and database work
• Combining specialist findings into a prioritized report

Turn a feature idea into a detailed implementation plan through research, interviews, and reviews by Gemini and Codex. Produce self-contained work sections with dependencies and acceptance criteria, plus files for supported automated implementation workflows.

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• Clarifying requirements before feature development
• Reviewing an implementation plan with multiple AI models
• Splitting development work into self-contained sections

Have a different AI model review code, analysis, or documents against the original skill's stated requirements. Receive evidence-based findings and a comparison of agreement and disagreement, then decide which recommendations to accept.

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• Getting a second opinion on substantial code changes
• Reviewing authentication and other security-sensitive changes
• Checking work before a bulk operation

Choose a web application stack, generate a starter project, and assess existing code quality. Uses team size, release frequency, audience, and budget to guide recommendations, with reports covering security, complexity, dependencies, and prioritized improvements.

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• Generating a starter project for a web application
• Choosing a technology stack within team and budget constraints
• Reviewing code security and complexity

Route code changes and SQL analysis to isolated Kortix work sessions with clear requirements and workspace details. Coordinate parallel investigations or independent pull request reviews, then summarize deliverables, verification results, and differences between findings.

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• Delegate feature implementation or bug fixes to a separate session
• Compare two agents' approaches to a refactor
• Combine findings from two independent pull request reviews

Select relevant code using Trailmark's map of function relationships, then delegate explanation, review, or edit proposals to a smaller model. Limit the supplied source, check cited evidence, and verify proposed changes before applying them.

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• Asking a small model to explain a function
• Tracing callers and paths from entrypoints
• Requesting edit proposals for a specific line range

Build a graph of functions and calls across multiple programming languages to investigate entry points, paths to sensitive operations, and potential impact. Use the results to prioritize security reviews and connect external findings with source code.

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• Tracing call paths from entry points to sensitive functions
• Prioritizing code for a security audit
• Connecting calls across language and RPC boundaries

Develop security design improvements from vulnerability reports, incident documents, source code, or scan results. Compare architectural options with before-and-after diagrams, engineering tradeoffs, and migration plans to support a concrete design decision.

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• Find structural causes shared by several vulnerabilities
• Compare security architecture options and costs
• Explain proposed changes with before-and-after diagrams

Collect Trustpilot reviews for a company domain, with filters for ratings, language, dates, and reply status. Get structured review text, reviewer details, and company responses across pages to support reputation research and customer service analysis.

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• Collect low-rated reviews to study customer complaints
• Gather reviews for competitor reputation comparisons
• Collect company replies and their publication dates

Write and review R analysis scripts for a sewage research project using its established conventions. Estimate requested regression models, add specifications, and save LaTeX tables, figures, and model objects for use in the research paper.

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• Estimate models of sewage spills and property prices
• Add specifications to an existing regression analysis
• Create descriptive plots and maps

Extract statistical values from research PDFs for meta-analysis, which combines results across studies. Track each value’s source and confidence, prioritize review, and manage human corrections and final approval in a shared data structure.

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• Extracting statistics from research PDFs
• Prioritizing uncertain or conflicting values for review
• Tracking human corrections and final approval

Guide Claude Code to check files, documentation, data, and execution results before presenting answers. The skill sets verification steps for coding, analysis, and writing, including checking citations and numbers, diagnosing errors, and clearly stating what cannot be verified.

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• Checking function names and arguments before coding
• Inspecting data columns and file paths before analysis
• Verifying row counts and missing values after joins

Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.

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• Building an end-to-end empirical analysis workflow in Python
• Replicating an applied economics paper
• Producing regression tables and event-study plots

Find STATA code examples for empirical accounting research from published replication files. Adapt regression, matching, event study, and table export syntax to your variables, with citations to the papers behind each example.

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• Implement fixed effects regressions in STATA
• Find published DiD and matching code examples
• Export regression results as formatted tables

Explore scientific data files to understand their structure, statistical patterns, and quality. References cover over 200 formats, with workflows for producing Markdown reports that summarize findings and recommend preprocessing, visualizations, and further analysis.

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• Checking missing values and outliers in experimental tables
• Examining sequence lengths and quality scores in FASTQ files
• Inspecting microscopy image dimensions and metadata

Understand how unfamiliar code works from a security perspective. The skill maps architecture, follows untrusted input through the code, and searches for similar vulnerability patterns, with file and line references to support static analysis and finding validation.

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• Mapping unfamiliar code before security scanning
• Tracing untrusted input to dangerous operations
• Searching for variants of a known vulnerability

Guides authorized source code security reviews across dangerous functions, data flow, authentication, logic flaws, and dependencies. Helps agents prioritize review paths and organize code locations, reproduction evidence, and remediation advice before drawing conclusions.

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• Prioritizing entry points in a large codebase
• Tracing user input to dangerous functions
• Reviewing authentication, logic, and dependency risks

Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing. Uses GPT-5.2 by default for state-of-the-art software engineering.

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Detect hardcoded sensitive data (API keys, access tokens, private keys, passwords, etc.) in publicly accessible code — frontend JavaScript, mobile apps, client-side bundles, and HTML templates. Uses a three-phase approach: recon (find secret candidates), batched verify (confirm real secrets in public code paths, 3 candidates each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/hardcodedsecrets-results.md. Use when asked to find hardcoded secrets, leaked API keys, or exposed credentials.

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Guidelines for introducing and using nullable reference types (NRT) and System.Diagnostics.CodeAnalysis nullable attributes in C# / .NET codebases. Covers the nullability model, flow analysis, the null-forgiving operator, API design rules, the full attribute catalog (AllowNull, DisallowNull, MaybeNull, NotNull, NotNullWhen, MaybeNullWhen, NotNullIfNotNull, MemberNotNull, MemberNotNullWhen, DoesNotReturn, DoesNotReturnIf), the C# 14 field keyword, incremental migration of legacy codebases, and a code-generation checklist.

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Guidelines for making .NET libraries and applications trimming-safe and Native AOT compatible. Covers the trimming/AOT model, the MSBuild properties that enable analysis (IsTrimmable, IsAotCompatible, PublishTrimmed, PublishAot), the trimming attributes (RequiresUnreferencedCode, RequiresDynamicCode, DynamicallyAccessedMembers, UnconditionalSuppressMessage), IL2xxx/IL3xxx warning codes, and a pattern playbook: source generators and UnsafeAccessor, generated-interception diagnostic suppressors, intentional runtime scanning boundaries, trimming-safe islands and feature switches, migration analyzers, and temporary warning-approval baselines. Use when introducing trimming/AOT support, resolving IL2xxx/IL3xxx warnings, or making reflection-heavy code trimming-safe in C# / .NET codebases. For System.Text.Json AOT scenarios, see also the serialization skill.

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Execute Codex CLI for code analysis, refactoring, and automated code changes. Use when you need to delegate complex code tasks to Codex AI with file references (@syntax) and structured output.

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Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use automated-test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for designing a new interface or contract — use design-an-api. Not for planning the change its findings imply — use plan-a-change. Not for discovering bounded contexts, ubiquitous language, or where code boundaries diverge from domain boundaries — use ddd-analysis. Not for writing documentation or architectural decision records.

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