2.9K skills · sorted by relevance
Review a reusable Agent Skill package before implementation. Validate its identity fields, separate instructions from external capabilities and execution timing, and receive a validation report with a minimal set of building blocks and reasons for each choice.
Research existing code, documents, and relevant sources before producing a practical plan for implementation, migration, debugging, or evaluation. The plan explains key decisions, ordered steps, and validation checks, giving users something concrete to review before execution.
Build an interactive app prototype in React with working clicks, form validation, animated transitions, and navigation across multiple steps. Manage changing screen state so users can try realistic flows and see how the interface responds.
Helps organize biological datasets and models in LaminDB. Covers data registration, table validation, annotation with standardized biological terms, metadata searches, and tracking analysis inputs and outputs so researchers can preserve the context needed to repeat their work.
Helps build and review NeuroKit2 research workflows for physiological recordings such as ECG, respiration, and skin conductance. Covers preprocessing, time alignment, event analysis, heart rate variability, and complexity measures, while recording methods and checking data quality.
Generate candidate positions and orientations for small molecules binding to proteins with DiffDock and DiffDock-L. Prepare single or batch docking runs, organize predicted poses by model confidence, and review validation needs without treating confidence as binding affinity.
Run repeatable tests of Cherry Studio’s key workflows with Playwright, which checks application behavior through automated interactions. Collect results on macOS and Windows and produce English reports for release acceptance or development validation.
Plan AWS infrastructure changes by checking application requirements and existing configuration, then selecting guidance from an external skill collection. Produces a reviewable plan covering access permissions, networking, cost assumptions, validation, and rollback.
Implement and review API security within an application's existing authentication and data model. Covers token verification, resource permissions, input validation, request limits, secrets, and logging, with concrete checks for unauthorized access and invalid requests.
Build REST API endpoints, the routes applications use to exchange data, with input validation, authentication, access checks, error handling, and documentation. Covers creating, reading, updating, and deleting records, plus pagination and consistent response formats.
Create consistent records for evaluating AI agents, including task success, invalid tool calls, token use, and actual costs. Produce CSV tables, database definitions, and JSON validation schemas to support reproducible comparisons and checks for performance declines.
Design tools that AI agents can understand and call, covering input schemas, clear descriptions, validation, and error handling. The guidance includes JSON Schema, a format for defining inputs, and MCP, a protocol for connecting agent tools.
Review open commitments in acc and record success or failure with supporting notes. Distinguish self-assessment from owner validation or observed real-world results, and keep commitments open when they are still waiting on an outcome.
Trace uploaded files through extraction, conversion, parsing, and reconstruction of saved objects in a CTF sandbox, an isolated competition environment. Preserve original and derived files, distinguish visible validation from backend behavior, and document the smallest reproducible processing chain.
Investigate how compiled or obfuscated programs work, including binaries, APKs, WASM, and firmware. Combine file inspection, decompilation, and observed execution to understand control flow, validation logic, and mechanisms that hinder analysis.
Investigate signs of intrusion through hypothesis-led log searches, then turn findings into detection rules. Supports Sigma, YARA, and SIEM queries, with attention to false positives, data field mapping, and validation against historical logs.
Implement Python error handling with early input checks, appropriate exception types, and clear messages. Handle individual failures in batch jobs, preserve error context for debugging, and use Pydantic models to validate structured input.
Design machine learning workflows from data preparation through training, validation, deployment, and monitoring. Use templates and guidance to connect stages, track versions and experiments, handle failures, and plan gradual releases with rollback options.
Build reusable Terraform modules for AWS, Azure, GCP, and OCI. Organize cloud infrastructure as code with documented inputs, validation, usage examples, provider version constraints, and Terratest tests for consistent provisioning.
Build and review ViewModels, objects that manage screen state and actions, with CommunityToolkit.Mvvm. Use attributes to generate change notifications and commands, add input validation, and handle asynchronous operations, cancellation, and command availability.
Build Spring Boot applications with guidance on project structure, configuration, APIs, database access, logging, testing, and security. Apply practices such as constructor injection, passing required components through constructors, and consistent handling of validation errors.
Diagnose Fedora Linux problems with a structured troubleshooting plan. Get commands for checking services, logs, and packages, followed by remediation, validation, and rollback steps that account for SELinux and firewalld where relevant.
Document a local codebase with file references, architecture diagrams, and verified project commands. For legacy modernization, assess what still runs and create a phased migration plan with validation methods suited to each component’s current condition.
Turn defects found during Oracle-to-PostgreSQL migration into structured bug reports. Document expected and actual behavior, reproduction steps, severity, root causes, proposed changes, and validation on both databases so teams can act on the findings.