scaffold-exercises/SKILL.md
Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.
npx skillsauth add kayaman/skills scaffold-exercisesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Create exercise directory structures that pass pnpm ai-hero-cli internal lint, then commit with git commit.
XX-section-name/ inside exercises/ (e.g., 01-retrieval-skill-building)XX.YY-exercise-name/ inside a section (e.g., 01.03-retrieval-with-bm25)XX, exercise number = XX.YYEach exercise needs at least one of these subfolders:
problem/ - student workspace with TODOssolution/ - reference implementationexplainer/ - conceptual material, no TODOsexplainer.1/, explainer.2/, ... - numbered explainers when multiple conceptual sections are needed (same rules as explainer/)When stubbing, default to explainer/ unless the plan specifies otherwise.
Each variant subfolder (problem/, solution/, explainer/, explainer.N/) needs a readme.md that:
When stubbing, create a minimal readme with a title and a description:
# Exercise Title
Description here
If the subfolder has code, it also needs a main.ts (>1 line). But for stubs, a readme-only exercise is fine.
mkdir -p for each pathreadme.md per variant folder with a titlepnpm ai-hero-cli internal lint to validateThe linter (pnpm ai-hero-cli internal lint) checks:
problem/, solution/, explainer/, or explainer.N/)problem/, explainer/, or explainer.1/ existsreadme.md exists and is non-empty in every variant subfolder present.gitkeep filesspeaker-notes.md filespnpm run exercise commands in readmesmain.ts required per subfolder unless it's readme-onlyWhen renumbering or moving exercises:
git mv (not mv) to rename directories - preserves git historyExample:
git mv exercises/01-retrieval/01.03-embeddings exercises/01-retrieval/01.04-embeddings
Given a plan like:
Section 05: Memory Skill Building
- 05.01 Introduction to Memory
- 05.02 Short-term Memory (explainer + problem + solution)
- 05.03 Long-term Memory
Create:
mkdir -p exercises/05-memory-skill-building/05.01-introduction-to-memory/explainer
mkdir -p exercises/05-memory-skill-building/05.02-short-term-memory/{explainer,problem,solution}
mkdir -p exercises/05-memory-skill-building/05.03-long-term-memory/explainer
Then create readme stubs:
exercises/05-memory-skill-building/05.01-introduction-to-memory/explainer/readme.md -> "# Introduction to Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/explainer/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/problem/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/solution/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.03-long-term-memory/explainer/readme.md -> "# Long-term Memory"
tools
Guidance for designing charts, graphs, plots, dashboards, and data visualizations that communicate clearly and persuade. Use when creating or reviewing a visualization, choosing a chart type, picking a color palette, decluttering a busy graphic, fixing misleading axes or proportions, building a dashboard, annotating a figure, or turning data into a presentation, report, or data-driven story. Grounded in the standard data-visualization literature (Knaflic, Tufte, Cleveland & McGill, Cairo, Wilke, Munzner, Few, Berinato). Covers chart selection, graphical perception and encoding, color and accessibility, decluttering, graphical integrity, dashboards, and narrative. Does NOT cover building data pipelines or ETL, statistical modeling or analysis methods, BI tool/vendor selection, or general UI/UX layout (see ux-design-principles). Tool-agnostic, with optional Python recipes.
development
Architect and implement production-grade microservices systems in TypeScript (NestJS) and Python (FastAPI), including resilience, observability, testing, deployment, and migration guidance.
development
--- name: databricks-genie-spaces-best-practices description: Design, configure, curate, govern, monitor, and integrate Databricks AI/BI Genie Spaces — the natural-language-to-SQL surface over Unity Catalog. Covers space scoping, general instructions, parameterized example SQL, SQL functions, trusted assets, JOIN configuration, knowledge store, certified queries, benchmarks, monitoring tab, feedback loops, the Genie Conversation API, governance via Unity Catalog (row filters, column masks, embed
tools
Implement OTP and passwordless authentication on AWS for TypeScript projects using Cognito CUSTOM_AUTH triggers (default) or a custom DynamoDB-backed flow, with SES (email) and SNS (SMS) delivery. Use when the user mentions OTP, one-time password, passwordless login, magic link, Cognito custom auth, DefineAuthChallenge, CreateAuthChallenge, VerifyAuthChallengeResponse, SES verification email, SNS SMS code, or MFA over email/SMS. Covers architecture decision (Cognito vs custom), Lambda trigger handlers, SES/SNS notifiers, DynamoDB schema with TTL, rate limiting, constant-time comparison, threat model (enumeration, replay, brute force), and aws-sdk-client-mock testing.