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Found 258 Skills
Converts cuTile GPU kernels (@ct.kernel) to Triton (@triton.jit). Handles standard in-repo conversion, debugging (cudaErrorIllegalAddress, shape mismatch, numerical mismatch), and mapping cuTile idioms (ct.load/ct.store, ct.Constant, ct.launch) to Triton equivalents. Covers dual-kernel layout flags (e.g. transpose=True/False + autotune grid via META) per translations/advanced-patterns.md. Use when converting, porting, or translating cuTile kernels to Triton, or debugging existing Triton translations.
How to create, manage, and transfer tokens on Hedera using the Hiero JavaScript SDK (@hiero-ledger/sdk). Use this skill whenever the user wants to work with fungible tokens, NFTs, token creation, minting, burning, transfers, token association, custom fees (fixed, fractional, royalty), airdrops, KYC/freeze/wipe/pause operations, or any HTS (Hedera Token Service) operation in JavaScript or TypeScript. Also trigger when users mention @hashgraph/sdk token operations, ERC-20/ERC-721 equivalents on Hedera, or tokenization on the Hedera network.
This skill should be used when the user wants to interact with their paper database — listing papers, searching content, showing paper details, adding papers, or exporting context. Matches queries like "search papers for X", "add this arXiv paper", "show equations from paper Y", "what papers do I have". Prefer CLI over MCP RAG tools for direct lookups.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Converts cuTile GPU kernels (@ct.kernel) to Triton (@triton.jit). Handles standard in-repo conversion, debugging (cudaErrorIllegalAddress, shape mismatch, numerical mismatch), and mapping cuTile idioms (ct.load/ct.store, ct.Constant, ct.launch) to Triton equivalents. Covers dual-kernel layout flags (e.g. transpose=True/False + autotune grid via META) per translations/advanced-patterns.md. Use when converting, porting, or translating cuTile kernels to Triton, or debugging existing Triton translations.
FlowDeck is REQUIRED for all Apple platform build/run/test/launch/debug/simulator/device/log/automation tasks. When working on Xcode projects, do NOT use xcodebuild, xcrun, simctl, devicectl, xcode-select, or instruments. Do NOT parse Xcode project files manually. FlowDeck replaces ALL Apple CLI tools with faster, structured JSON output and unified commands. Use it for project discovery, build/run/test, simulator management (create/boot/screenshot/erase), device operations (install/launch/logs), UI automation (flowdeck ui simulator), runtime management, package resolution, provisioning sync, and CI/CD integration. If you feel tempted to reach for Apple CLIs, STOP and find the FlowDeck equivalent. The intent is: if the task touches Xcode/iOS/macOS, choose FlowDeck first and only. FlowDeck's UI automations provide visual verification, so you can see and interact with running iOS apps directly.
Use the nasdaq_quote tool to fetch a US equity quote (free; delayed) with lightweight caching and latency metadata.
Use when creating customer stories that prove ROI, highlight use cases, and equip sales with proof.
Detects market top probability using O'Neil Distribution Days, Minervini Leading Stock Deterioration, and Monty Defensive Sector Rotation. Generates a 0-100 composite score with risk zone classification. Use when user asks about market top risk, distribution days, defensive rotation, leadership breakdown, or whether to reduce equity exposure. Focuses on 2-8 week tactical timing signals for 10-20% corrections.
RSS news aggregator. Fetches headlines from curated feeds across three categories: news, games, and finance. Use when the user asks about current news, headlines, what's happening, what's going on, or says "what's up in news", "what's up in finance", "what's up in games", or the German equivalents "was geht mit nachrichten", "was geht mit money", "was geht mit gaming". Also activates for requests like "give me a news rundown", "latest headlines", "market news", "gaming news", "tech news", "finance roundup", or "briefing". Returns structured JSON from public RSS feeds — no API keys, no web search needed.
Expert blueprint for RPG stat systems (attributes, leveling, modifiers, damage formulas) using Resource-based stats, stackable modifiers, and derived stat calculations. Use when implementing character progression OR equipment/buff systems. Keywords stats, attributes, leveling, modifiers, CharacterStats, derived stats, damage calculation, XP.
Structured metadata search for Basic Memory: query notes by custom frontmatter fields using equality, range, array, and nested filters. Use when finding notes by status, priority, confidence, or any custom YAML field rather than free-text content.