209 skills (repositories by rohitg00) · sorted by popularity
Learn to examine compiled programs through one interactive lesson per session. Labs and quizzes cover file formats, machine instructions, program behavior, and analysis tools, while recorded observations provide evidence for completing each lesson and resuming later.
Create a substantial technical manual for one exact system version. Organize authoritative sources, record offline inputs and outputs, explain behavior with figures, and audit the text and supporting evidence for consistency.
Build an interactive page that explains a process as a machine viewed from an angle. A tested simulation keeps movement, numbers, and explanations consistent, while controls let readers step through operations and explore failures.
Create static SVG diagrams, scalable images for lessons, READMEs, and manuals. It uses real system names, nine diagram types, and consistent colors and arrows, with checks for readable labels, accessible descriptions, and valid markup.
Create animated lesson diagrams, sliders that update figures, and interactive panels that explain how systems work. It also builds moving dot patterns for page headers, with rules for readability, themes, and reduced motion.
Apply shared visual and writing rules to course pages, figures, and manuals. Check colors, fonts, labels, layouts, plain technical English, dark themes, keyboard focus, touch targets, and reduced-motion behavior against defined reference values.
Review Orchard deployment manifests, the files describing deployment settings, and keep source paths in the result. The workflow stops when evidence is missing, tying review findings to the available source material.
Find relevant lessons in AI Engineering from Scratch from a topic, question, or bug. Get a short selection of lessons, necessary prerequisites, and the next learning command, with dedicated routes for specialized subjects.
Publish an approved Orchard release while retaining source paths in the result. The skill requires work to stop when supporting evidence is missing, making evidence availability a condition for proceeding.
Study AI engineering through interactive terminal lessons with explanations, coding exercises, and quizzes. The tutor follows your recorded progress, revisits previous material, adapts to your answers, and logs results and topics that need review.
Learn to create, discover, invoke, secure, evaluate, package, and port Agent Skills through one interactive lesson at a time. Practical checkpoints and quizzes track demonstrated progress in a local file, so later sessions resume where you stopped.
Start AI Engineering from Scratch with a brief interview and placement quiz. Create a persistent LEARNING.md study plan recording your goals, starting phase, pace, and learning path for later sessions with the learn skill.
Review database migration files before merging changes. It gathers evidence about what the changes do, rollback limits, database locks, and risks from large data volumes, then reports blockers when evidence needed for a decision is missing.
Review deployment evidence and recovery conditions when assessing a release. The skill directs the agent to consult the accompanying restore reference and include a timestamp in its decision, providing a narrowly defined release review procedure.
Evaluate an Agent Skill bundle before release by checking structure, activation conditions, output quality, scripts, safety, file integrity, and host compatibility. Receive a detailed JSON report that distinguishes local evidence checks from production readiness backed by external attestation.
Design and test rules for activating skills through human requests, AI selection, applications, or other skills. Check eligibility before matching descriptions, then return a JSON decision showing the selected channel, score, and policy reason.
Build a catalog from multiple skill directories using an explicit priority order. Review duplicate names, selection decisions, and omitted entries before loading the instruction text of the chosen skill.
Test your understanding of a phase in AI Engineering from Scratch through eight multiple-choice questions. Receive a score, explanations for incorrect answers, and specific lessons to review before continuing your studies.
Prepare for four Claude certification tracks through guided lessons, practical exercises, quizzes, and mock exams. The tutor checks understanding and learner-created work, records progress, and uses assessment results to guide review.
Take a ten-question interactive quiz across five AI and machine learning areas. Use the results to find a starting point in a 523-lesson curriculum, identify topics to review, and estimate the time for your learning path.
Study Model Context Protocol (MCP), which connects AI applications with external tools, through guided lessons and exercises. Inspect requests and responses, practice implementation and security checks, and record progress alongside observed results.
Review proposed file operations, commands, network access, secret handling, or destructive actions against explicit sandbox rules that define permitted activity. Receive a JSON verdict and the exact rule behind it without performing the proposed action.
Learn AI engineering by building a project one stage at a time. The tutor guides predictions, learner-written code, automated checks, and reflection, then records progress so the next session can continue from the right stage.
Prepare for the MCPA certification through interactive lessons, local Python labs, diagnostic assessments, and mock exams. Track progress, revisit weak domains, and resume learning, with guided support for learners who do not write code.