2.3K skills · sorted by relevance
Find JavaScript that executes strings as code, including eval(), new Function(), and string-based timers. Review how it runs in the browser and suggest alternatives for parsing data, choosing operations, and scheduling functions to reduce code injection risks.
Connect syncable entity components within Twenty server internals and expose them through GraphQL, an API query interface. It guides NestJS module registration, business services, response data conversion, and consistent handling of validation and execution errors.
Set up and inspect an Ethereum node through a command-line interface. Install execution and consensus clients, configure validator metadata, expose RPC access for external requests, and retrieve structured health reports for agent workflows.
Manage n8n automation workflows from the command line. Create, update, and activate workflows, inspect or retry executions, and manage credentials, variables, and tags through n8n's public API with structured JSON output.
Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.
Learn how to check Claude Code tool calls against a policy and create digitally signed execution records. This walkthrough covers setup, offline verification, tamper demonstrations, and CI/CD integration before adopting the protect-mcp runtime plugin.
Improve Bazel builds in large repositories containing multiple projects. Get guidance on build configuration, shared remote caches, remote execution, dependency management, custom build rules, and diagnosing build problems.
Assess serverless functions, primarily AWS Lambda, for unsafe execution of external input and unauthorized changes to code or added libraries. Combine source inspection, activity logs, and permission reviews to identify injection risks and recommend detection and prevention measures.
Analyze Windows Amcache records to investigate applications, deleted executable traces, and drivers. Extract file details, compare SHA-1 hashes with malware databases, and combine records into a timeline to support incident investigations.
Guides background processing in Node.js and TypeScript with BullMQ and Redis. Covers job queues, which hold tasks for later execution, along with scheduling, priorities, rate limits, worker concurrency, and dependencies between processing steps.
Guides experiments that inspect and change internal model values using nnsight. Covers local PyTorch models and remote experiments through NDIF, including replacing intermediate values to test how particular layers affect a model’s predictions.
Build collaborating AI teams with CrewAI by defining roles, tasks, and shared context. Configure ordered execution or manager-led delegation, add memory and tools, and create workflows that branch based on events or results.
Create, configure, and troubleshoot Crush hooks, commands that run before tool execution. Use them to block or approve calls, rewrite tool input, or add context for the agent, with guidance on configuration and troubleshooting.
Build reusable parts for n8n workflows instead of copying shared logic. This skill guides typed inputs, consistent outputs, naming, and execution settings so workflows and AI agents can call the same processing steps.
Give subagents or short sessions a compact set of working rules for checking evidence, changing unsuccessful approaches, and verifying results. It also defines agent roles and uses stern corporate-style commentary to push execution forward.
Build text, image, and audio features in JavaScript using machine learning models that run in browsers or server runtimes. Covers model selection, execution settings, caching, and memory cleanup without requiring a Python server.
Investigate errors, crashes, and unexpected behavior by reproducing failures, tracing execution, and comparing logs. Tests one explanation at a time, then provides a verified fix, supporting evidence, and tests or safeguards to prevent recurrence.
Turn an idea into a goal package for /goal. Collect decisions through Plannotator, review testable requirements, explore the codebase, and produce an approved execution plan with verification steps and a clear completion condition.
Run Python through run_shell when code execution tools repeatedly fail. This workflow embeds a multiline script directly in a shell command, supporting tasks such as CSV conversion and PDF generation while checking execution output.
Recover Python tasks when execute_code_sandbox fails with errors or timeouts. Install required packages, run the code through run_shell, and inspect messages and generated files to check the result.
Recover from failed automated file creation with an explicit shell workflow. The skill guides agents through checking folders, writing and running scripts, and inspecting output files to confirm that deliverables were actually created.
Diagnose Python script failures by capturing full error traces, which show where execution failed, alongside exit codes and working directory details. The skill also covers file existence checks and clearer paths for troubleshooting.
Recover Python file-generation tasks when sandbox execution fails by saving a script and running it through the shell, a command interface. Use the workflow for PDFs, document file operations, dependency checks, and output verification.
Run multiline Python code by saving it to a script file before executing it through the shell. The workflow covers working directories, timeouts, error checks, and optional cleanup to make execution easier to debug.