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Found 268 Skills
Orchestrates Android development tasks including project creation, deployment, SDK management, and environment diagnostics using the `android` command-line tool.
Graham cigar-butt (NCAV / net-net) single-stock diagnostic. Combines a 100-point static cheapness score (NCAV, PE, PB, dividend yield, debt coverage, earnings stability) with a dynamic adjustment layer (industry cycle, earnings trend, insider activity, NCAV trajectory) to separate real bargains from value traps. Pulls data from Longbridge CLI/MCP first, falls back to WebSearch only for gaps, runs cross-statement reconciliation (勾稽校验) before scoring, and footnotes every figure to its source. Triggers: "格雷厄姆", "捡烟蒂", "烟蒂股", "烟蒂投资", "NCAV", "净流动资产", "清算价值", "安全边际", "价值陷阱", "深度价值", "撿煙蒂", "煙蒂股", "煙蒂投資", "淨流動資產", "清算價值", "安全邊際", "價值陷阱", "深度價值", "Graham", "cigar butt", "net-net", "liquidation value", "value trap", "margin of safety", "deep value", "Benjamin Graham".
Use this skill to validate the "why" before building, run product diagnostics, and pressure-test product direction before the request becomes an implementation contract.
Pre-sprint diagnostic that determines whether a team should run a Design Sprint now, postpone it, or do prerequisite work first. Produces a Go / Conditional Go / Wait verdict with diagnosis, recommended preconditions, attendee list, customer recruiting plan, and pre-sprint activities. Use when a team is considering starting a Design Sprint and wants a fast yes/no diagnosis before committing five days of team time and customer recruiting cost.
Alibaba Cloud PAI-Rec Engine Diagnostic and Configuration Validation Skill. Use for diagnosing PAI-Rec engine interface issues and validating engine configurations. Triggers: "PAI-Rec", "engine diagnosis", "engine config validation", "pairec", "recommendation engine".
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.**
Use when debugging 'files disappeared', 'data missing after restart', 'backup too large', 'can't save file', 'file not found', 'storage full error', 'file inaccessible when locked' - systematic local file storage diagnostics
Get the current Neovim context as JSON (cursor position, current file, visual selection and diagnostics) to help answer questions about code at the current cursor position, visual selections and diagnostics. Use when users ask about "this line", "current file", "selection" or need context about their Neovim editor state.
.NET test execution patterns and diagnostics. Use when running tests, analyzing test failures, or configuring test options.
Helps debug test failures and view logs in the SnapLogic Robot Framework project. Use when the user wants to view test results, check container logs, debug specific test failures, or run environment diagnostics.
Health check for TTS and Telegram bot subsystems. TRIGGERS - tts health, kokoro status, telegram bot check, tts diagnostics.
Debugs Windows applications using WinDbg and crash dump analysis including MCP server integration, live process attach, dump file triage, hang detection, high-CPU diagnosis, memory leak investigation, kernel debugging, symbol configuration, scenario command packs, diagnostic report generation, and SOS extension workflows for .NET runtime inspection.