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Found 2,386 Skills
Native visionOS spatial computing, SwiftUI volumetric interfaces, and Liquid Glass design implementation
Query and analyze distributed traces and spans using DataPrime syntax. Use this skill whenever the user wants to investigate request latency, find slow operations, debug service-to-service calls, look up a trace ID, analyze span durations, check error spans, examine distributed traces, investigate OpenTelemetry/Jaeger tracing data, or query Coralogix spans in any way - even if they don't explicitly mention "DataPrime" or "cx spans".
Use the spark CLI to access the user's Spark email data - list emails, search by topic, read threads, check calendar events, find availability, look up contacts, and view team info. Use when the user asks about their emails, calendar, contacts, meetings, or scheduling.
Weekly summary: this week's meetings, unread email breakdown by category, and team assignment status.
Pull every meal you ever logged out of MyFitnessPal — per-food CSV, agent-shaped trends, and a local SQLite store. Trigger phrases: `what did I eat this week`, `export my food diary`, `find every time I logged X`, `top foods driving my protein`, `am I hitting my calorie streak`, `use myfitnesspal`, `run myfitnesspal`.
Examine file system slack space, MFT entries, USN journal, and alternate data streams to recover hidden data and reconstruct file activity on NTFS volumes.
Use when building or maintaining real-time collaborative apps with the DeepSpace SDK on Cloudflare Workers — scaffolding new apps, adding features, debugging a `worker.ts` that imports from `deepspace` / `deepspace/worker` or uses `RecordRoom`, `__DO_MANIFEST__`, or `npx deepspace`. Also use when the user mentions DeepSpace or app.space, or asks for anything involving real-time sync, multiplayer state, live cursors / presence, whiteboards or canvases, collaborative text editing (Yjs), channel-based chat, per-role permissions (RBAC), Durable Object rooms, Stripe-backed subscriptions / paywalls / one-time products / tips / refunds, or one-package deploy to `.app.space` — even if they don't name DeepSpace explicitly.
Decide where files live in an ML experimentation project: reusable code in `src/<pkg>/`, one `# %%` script per experiment in `experiments/`, design notes + index in `journal/`, reports in `reports/`, agent-only probes in `scratch/`, narrative digest in `overview/summary.md`. Owns the layout, the file-creation rules (one file per experiment, ask before editing), and the jupytext `# %%` script convention. Never imposes `data/` — the user owns that. TRIGGER — any of: - Starting a new ML project / scaffolding a workspace. - About to create the first experiment file in a project. - About to create `src/<pkg>/data.py` / `features.py` / `pipeline.py` / `evaluate.py` for the first time. - About to write a `.ipynb` for experimentation — redirect to a `# %%` script under `experiments/`. - User asks where something should live, how to organize the project, or how to set up the workspace. - About to add a new experiment iteration — decide new file vs edit existing (ask the user). SKIP when: the file is clearly part of an already-populated module (e.g., adding a function to existing `features.py`); pure refactor inside a single existing file; pipeline declaration mechanics (`build-ml-pipeline`); evaluation mechanics (`evaluate-ml-pipeline`); skore symbol lookup (`python-api`). HOW TO USE: **first run the Detection table** below — if any signal matches, glue to existing conventions (do not rename or move folders). If no signal matches, scaffold the default layout. **Emit the Pre-flight checklist as visible text and read the Stop conditions before any file is created or edited.** Use templates in `templates/`; copy and adapt, do not rewrite from scratch.
Sparse4D for multi-camera temporal 3D object detection and tracking. Uses sparse queries with deformable attention across camera views and time for end-to-end 3D perception, with an instance bank for temporal tracking. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Sparse4D model. Trigger phrases include "train Sparse4D", "multi-camera 3D detection", "temporal 3D tracker", "sparse query 3D perception".
Use Git worktrees for isolated work environments. Creates separate working directories for parallel development on different branches.
SpacetimeDB development best practices for TypeScript server modules and client SDK. This skill should be used when writing, reviewing, or refactoring SpacetimeDB code to ensure optimal patterns for real-time, multiplayer applications. Triggers on tasks involving SpacetimeDB modules, tables, reducers, subscriptions, or React integration.
Spawning Plan. Use when user wants to spawn agents, create a team, or coordinate multiple agents. Automatically gathers context, asks team topology questions, outputs clean TEAM PLAN markdown, and gets user approval. 3 steps: context gathering → questions → present plan. **CRITICAL**: MUST NOT SPAWN AGENTS SKIPPING THIS SKILL, USE ALWAYS.