758 skills · sorted by relevance
Apply a code rewrite plan one step at a time through small, reviewable edits. Keep changes within assigned files, check risk restrictions, and record each step’s files, change summary, rationale, and completion status.
Create a one-page HTML onboarding plan for a new employee. Bring together first-day and first-week schedules, manager and buddy details, 30/60/90-day milestones, useful resources, and a checklist of outcomes that show readiness.
Organize software development from requirements and architecture through testing, review, and deployment. Use specialized agent roles, shared memory, and test-driven development, where tests are written before code, to coordinate work across the project.
Manage GitHub issues, synchronize project boards, and plan sprints, short development cycles. Coordinate multiple AI agents to break work into tasks, assign responsibilities, update progress, and produce reports on completion and blockers.
Create a complete first version of a requested file or other deliverable early, then refine it and check its format, required content, location, and usability before reporting the work as finished.
Send prompts to LLMs from a terminal, display answers as they arrive, or hold an ongoing conversation. Use files or standard input for prompts, choose models, and set system instructions through OmniRoute.
Connect AI agents to OmniRoute for text and image generation, speech synthesis, transcription, and web search. Use OpenAI-compatible APIs and create embeddings, numerical representations of text, through documented requests.
Submit groups of AI requests as batch jobs with the OmniRoute command line. Manage input files, monitor or cancel jobs, save results and errors, and incorporate batch processing into automated build and deployment workflows.
Turn complex requests into actionable plans with smaller tasks, dependencies, assigned agents, and time estimates. The skill identifies work that controls the completion date, outlines risks and contingencies, and defines measurable success criteria.
Initialize a group of collaborating AI agents by choosing how they connect, allocating resources, and setting up communication. Establish shared-memory rules so agents record progress, share outputs, check dependencies, and signal completion.
Choose coding challenges matched to a learner’s level and goals within Flow Nexus. Validate submissions, review solution quality and efficiency, and track progress through rankings, achievements, badges, and challenge completion rewards.
Review implemented features against their requirements, add missing tests, and check performance constraints. Produce documentation, a table linking requirements to code and tests, and evidence showing whether the feature is ready for deployment.
Summarizes recorded system metrics for a chosen period, including completed tasks, errors, memory use, and token consumption. It compares values with historical baselines and flags unusual changes to help assess agent team health.
Capture lessons from completed tasks, train patterns from explicit examples, and consolidate accumulated learning into long-term storage. Use SONA and MicroLoRA adaptation tools for individual domains, then verify results in the appropriate stores.
Review an AI team's work against its acceptance criteria using collected evidence. Match requirements to task results or artifacts, distinguish verified facts from agent claims, and identify uncertainty or missing work before recommending completion.
Pause, resume, or complete an AI team while preserving its work evidence. Check the current state before changes, identify unfinished tasks that prevent run completion, and offer an evidence bundle before closing the work.
Organize software development from requirements and design through testing, review, and deployment. Uses Claude Flow to coordinate specialized agents, share decisions across sessions, and guide implementation with tests written before code.
Find optional features that slow the first page load and defer their code until a user interacts. Use dynamic imports, which load code on demand, and add immediate loading feedback while preserving the initial page experience.
Compare an implementation with its OpenSpec specifications, tasks, and design decisions before archiving the change. Get a report on completion, requirement coverage, and consistency, with prioritized issues, actionable recommendations, and explicit limits where evidence is missing.
Finalize an implemented OpenSpec change by checking planning files, task completion, and specification differences. Review whether to merge specification updates, verify any requested merge, then archive the change and report its location and outstanding warnings.
Guides coding work from planning and implementation through checks on actual screens, commands, or APIs. It adjusts verification to the change’s risk and records evidence, decisions, and cleanup so completion can be assessed against the requested outcome.
Review documents before signing or prepare a signing packet with checked recipients, fields, and signing order. Keep sending and signing separately authorized, and verify whether the document is drafted, sent, opened, or fully signed.
Coordinate a task from a verifiable goal through completion. Handle work directly or split independent tasks across separate agent sessions, track progress, verify results, and arrange pull request merging when included in the goal.
Find Ticketmaster events and compare seat options by price, quality, quantity, and budget. Get current ticket prices, group totals, and checkout links for specific seats, with purchase completion left to the user.