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Found 328 Skills
Use when working with Lightdash YAML files, dbt models with Lightdash metadata, the lightdash CLI (deploy, upload, download, preview, lint, warehouse-catalog, sql, set-warehouse), or managing charts, dashboards, spaces and access, AI agents, scheduled content, users, groups, custom roles, metrics, and dimensions as code
Guided authoring of an architectural invariant catalog entry through a 6-step interview (elicit, locate, weight, enforce, number, commit). Drives the invariants_scaffold and invariants_add orchestrate verbs — the agent supplies judgment and natural-language elicitation; the verbs own schema validation, file writing, and event emission. Defaults to mode: audit; mode: check is an advanced opt-in. Triggers: 'add an invariant', 'author an invariant', 'enforce an architectural rule', or invariants. Do NOT use for: editing workflow state, running a review, or hand-writing YAML (the verbs write it — never emit catalog YAML yourself).
Convert data between formats (JSON, XML, CSV, YAML, TOML). Use when transforming data structures or migrating between data formats.
This skill guides creating autonomous agents for Claude Code plugins using markdown files with YAML frontmatter. Use when building new agents, designing agent system prompts, or configuring agent behavior.
Generate learning-science-backed flashcard YAML files from lesson content. Use when: (1) a lesson .md file needs flashcards generated, (2) a chapter directory needs flashcards for all lessons, (3) user says "generate flashcards", "create flashcards", "make cards for", or references /generate-flashcards. Produces .flashcards.yaml files adjacent to lesson .md files, consumed by the remark-flashcards plugin and rendered by Flashcards components.
Use when querying, transforming, or editing structured data (JSON, YAML, TOML, XML, CSV). Prefer these tools over grep/sed/awk on structured formats.
Phase 1 of the feature workflow — Draft a design document for the new feature, serving as the sole input for subsequent implementation and acceptance. First gather evidence (read architecture docs, review relevant code, grep to prevent term conflicts, check archives), then write a complete first draft in one go (including YAML frontmatter + three-tier structure + test design), submit it to the user for overall review, and iterate until approval. After approval, extract {slug}-checklist.yaml from {slug}-design.md for use in the next two phases. Trigger scenarios: "Start designing the solution", "Write design doc", "Prepare to implement XX", with the prerequisite that you already know what to do, who it's for, and how to define success.
Guide for adding a new benchmark or training environment to NeMo-Gym. Use when the user asks to add, create, or integrate a benchmark, evaluation, training environment, or resources server into NeMo-Gym. Also use when wrapping an existing 3rd-party benchmark library. Covers the full workflow: data preparation, resources server implementation, agent wiring, YAML config, testing, and reward profiling (baselining). Triggered by: "add benchmark", "new resources server", "integrate benchmark", "wrap benchmark", "add training environment", "add eval".
Audit a WordPress plugin's REST surface and produce a standardized audit document proposing Abilities API registrations. Produces a markdown doc with a YAML schema and prose sections that humans and agents can both consume when planning a registration rollout. Works on any WP plugin.
Generate a publication-quality PDF from any brain page via the gstack make-pdf binary. Strips YAML frontmatter, sanitizes emoji, applies running headers and page numbers. Brain page is always the source of truth; PDF is a rendering.
Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Two responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, and (2) run ruff against any Python files Claude has just generated or edited. Stops at "the touched files pass `ruff check`." TRIGGER when (any of these): (1) a Python file was just created or edited via Write / Edit / MultiEdit — invoke this skill before declaring the task done so ruff is run on the touched files; (2) a fresh ML workspace was just scaffolded by `organize-ml-workspace` and the project has no `ruff.toml` at its root yet — drop the bundled template; (3) the user asks about lint, format, docstring style, or reaches for `black` / `isort` / `flake8` / `pydocstyle` (redirect to ruff — the stack's canonical linter, owned by `data-science-python-stack` Tier 1). SKIP when: the project is non-Python; the only edits in this turn are to Markdown / TOML / JSON / YAML; the file lives in a third-party vendored directory the user doesn't own. HOW TO USE: run ruff manually on the files you just touched — do not configure a PostToolUse hook for this. **Read the "Stop conditions" block and emit the Pre-flight checklist as visible text in your response — both are mandatory before running ruff.**
Guide for exposing PostHog product endpoints as MCP tools. Use when creating new or updating API endpoints, adding MCP tool definitions, scaffolding YAML configs, or writing serializers with good descriptions. Covers the full pipeline from Django serializer to generated TypeScript tool handler.