This skill helps an LLM generate correct DSPy signature code using @ax-llm/ax. Use when the user asks about signatures, s(), f(), field types, string syntax, fluent builder API, validation constraints, or type-safe inputs/outputs.

Use this skill when writing or reviewing Ax bestOfN/refine code, reward functions, thresholds, native sample selection, serial attempts, generated advice, and attempt diagnostics.

Use this for a local Motion film: a short animated video (10–30 s unless asked for more, 900 s at most), such as an explainer, product walkthrough or UI demo, launch teaser, logo sting, social clip, animated chart or title sequence, that plays in the bundled Motion player and exports from there to MP4 or GIF. A narrated one carries a voice track: a take a scene, made here with a speech provider of the user's or recorded by them, spoken and captioned by the player, the motion timed to its words. Everything on screen is code, a pure function of time, written scene by scene into the film's own store with this skill's script while the user watches it fill in. Read it when creating, continuing, finalizing or revising one. Not for editing real footage, subtitles or audio (that is a video editor), a still picture, a slide deck or a static HTML page.

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.

Find and add missing cross-references between wiki pages. Use when pages are disconnected or after large ingestion. This modifies pages to add links; use wiki-lint for report-oriented structural auditing.

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the `partialPrefetching` flag, opt routes in with `export const prefetch = 'partial'`, audit `Link prefetch={true}` behavior, preserve existing prefetched UI with `instant()` tests, or resolve the instant-link-prefetch-partial and instant-shell-url-data insights.

Use whenever adding, modifying, or reviewing any Copilot, agent, LLM, AI, tool, permission, sandbox, MCP, model, telemetry, feature-gate, setting, configuration, or enterprise control—especially anything an organization or administrator may need to manage. Start here to decide whether it belongs in runtime managed settings, a typed SDK contract, VS Code configuration policy, extension policy, or a split implementation. Run on every new Copilot/agent/LLM control and ANY change that adds a `policy:` field.

Use when adding, reviewing, or troubleshooting an interactive release-note Try This experience, including commands, views, editor samples, Chat drafts, guided spotlights, prerequisites, scoped targets, or Agents-window handoff.

Log a CLI in with a browser/device-code flow (aws sso, gh, glab, gcloud, …) and save the result to the keychain so later commands can request it through execute.credentials.

Use when: determining which Primer React API type (config, presentational, base, or utility) should own a specific accessibility responsibility, or documenting that ownership for a new component. Covers a responsibility matrix mapping ARIA requirements to API types, worked examples for Dialog and Tabs, and how to build a matrix for a new ARIA pattern.

Generate images, text, audio, video, and transcribe speech via the Pollinations API using the polli CLI. Use when asked to generate media, call pollinations.ai, check pollen balance, list models, manage API keys, inspect quests, manage agents or my-models, publish a static site, or run polli commands.

Investigate CI failures for dotnet/maui PRs and the nightly/official signed build — build errors, Helix test logs, and binlog analysis. Use when asked about failing checks, CI status, test failures, 'why is CI red', 'build failed', 'what's failing on PR', 'is this PR ready to merge', Helix failures, device test failures, or 'nightly is broken', 'nightly build failing', 'inflight feed stale', 'dogfood feed stopped updating', 'official build failed'.

Story-to-e2e-test workflow that takes a Jira [QE] Story, discovers the project's e2e testing infrastructure, plans test scenarios, writes e2e tests matching project conventions, validates them, and manages review via GitHub PRs. Use when implementing [QE] stories produced by the design workflow. Activated by commands: /ingest, /plan, /revise, /code, /validate, /publish, /respond.

Connect NotFair paid-ad accounts and establish reusable campaign context. Use when setting up paid media in a new workspace, connecting Google, Meta, X, LinkedIn, Reddit, or TikTok Ads, refreshing brand context, or preparing an agent to work on ads safely.

Produce a read-only, evidence-based paid-media performance review across connected platforms or supplied exports. Use for weekly or monthly reports, scorecards, performance, CPA, ROAS, CTR, spend trends, pacing, or conversion-tracking health.

Use when a quest needs a polished milestone chart, paper-facing figure, appendix figure, or a mandatory render-inspect-revise pass before treating a figure as final.