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Found 281 Skills
Collect Clay debug evidence for support tickets and troubleshooting. Use when encountering persistent issues, preparing support tickets, or collecting diagnostic information for Clay problems. Trigger with phrases like "clay debug", "clay support bundle", "collect clay logs", "clay diagnostic".
Help create and optimize blog posts, articles, and polished writing with rigorous structure, reader expectation management, and SCQA methodology. This skill contains a specialized writing framework (reader personas, concept introduction protocol, diagnostic checklists, Pyramid Principle structure) that cannot be replicated without loading it. You must use this skill in any of these scenarios: (1) writing a blog post or article from notes/materials, (2) reviewing, diagnosing, or optimizing any draft or article for structure, clarity, and readability, (3) polishing or refining notes into publishable form, (4) giving feedback on writing structure, flow, or reader experience, (5) creating outlines for articles. Trigger on keywords: "博客", "文章", "发布", "blog", "写作", "初稿", "打磨", "诊断", "优化文章", "结构", "大纲", "投稿", "公众号", "读者". Also trigger when the user shares a markdown file and asks to improve it, or asks if something "reads well" or "makes sense to readers".
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".
Run end-user cmux diagnostics. Use when cmux hooks, notifications, session restore, settings, browser automation, socket access, CLI control, or agent resume behavior is not working, or when the user asks for a cmux health check, doctor report, or support-safe debug summary.
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.
Pre-consultation diagnostic questionnaire for clients building websites with AI tools (Claude, Codex, Cursor, Bolt, v0, etc.) who have concerns about quality, design, or maintainability. Collects structured answers about their project, tools, pain points, and goals, then generates a consultation brief. Use when preparing for a website review consultation, when a client asks for a site audit, or when someone says their AI-built site has problems. Supports Russian and English — asks the client to choose language first.
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
Health check for TTS and Telegram bot subsystems. TRIGGERS - tts health, kokoro status, telegram bot check, tts diagnostics.
Generate tiered knowledge-verification questions (quiz/exam) at 3 difficulty levels with grading and diagnostics. For testing UNDERSTANDING of code, concepts, or architecture — NOT for writing software tests (use engineering:testing-strategy for that). Triggers on "문제 만들어", "quiz", "검증 문제", "이해도 확인", "knowledge check", "challenge me", "시험 문제", "면접 문제".
Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.
YC office hours skill. Used by founders for product diagnostic with six forcing questions, premise challenges, alternatives, and a design doc. Use when the user wants founder coaching, startup strategy feedback, or a mock office-hours conversation.