Loading...
Loading...
Found 1,927 Skills
Use this skill for project management: planning, progress tracking, task coordination, timeline/milestone management, risk assessment, resource allocation, and execution guidance. Examples: <example>User organizing complex development: "Starting feature with frontend, backend, infrastructure changes. Need project plan." → Creates plan with task breakdown, timeline, coordination strategy.</example> <example>User facing delays: "Project behind schedule, unsure how to prioritize tasks." → Analyzes situation, provides recovery plan with prioritized actions.</example>
Audit and improve SwiftUI runtime performance. Use for slow rendering, janky scrolling, high CPU, memory usage, excessive view updates, layout thrash, body evaluation cost, identity churn, view lifetime issues, lazy loading, Instruments profiling guidance, and performance audit requests.
Interactively debug source code — set breakpoints, step through execution line by line, inspect live variable state, evaluate expressions against the running program, and navigate the call stack to trace root causes. Use when a program crashes, raises unexpected exceptions, produces wrong output, when you need to understand how execution reached a certain state, or when print-statement debugging isn't revealing enough.
ISO 42001 AI Management System compliance automation. Assesses organizational readiness for AIMS certification, evaluates AI system impacts, validates governance structures, and checks Annex A controls. Use for ISO 42001 readiness assessments, AI governance planning, AI impact assessments, responsible AI implementation, and AIMS certification preparation.
Use when experiments complete to judge what claims the results support, what they don't, and what evidence is still missing. Codex MCP evaluates results against intended claims and routes to next action (pivot, supplement, or confirm). Use after experiments finish — before writing the paper or running ablations.
Calculates CRAP (Change Risk Anti-Patterns) score for .NET methods, classes, or files. Use when the user asks to assess test quality, identify risky untested code, compute CRAP scores, or evaluate whether complex methods have sufficient test coverage. Requires code coverage data (Cobertura XML) and cyclomatic complexity analysis. DO NOT USE FOR: writing tests, general test execution unrelated to coverage/CRAP analysis, or general code coverage reporting without CRAP context.
Verify your own completed code changes using the repo's existing infrastructure and an independent evaluator context. Use after implementing a change when you need to run unit or integration tests, check build or lint gates, prove the real surface works with evidence, and challenge the changed code for clarity, deduplication, and maintainability. If the repo is not verifiable yet, hand off to `agent-readiness`; if you are reviewing someone else's code, use `review`.
Clari Copilot (formerly Wingman) platform help — conversation intelligence with real-time battlecards, live coaching during calls, AI call summaries, coaching scorecards, gametapes, deal intelligence, and CRM auto-update within Clari's revenue orchestration platform. Use when setting up Clari Copilot for a sales team, battlecards popping up too often during calls, meeting bot not joining or joining late, Clari Copilot vs Gong pricing or features, Clari API integration for forecast export or data ingestion, CRM field mapping not syncing correctly, coaching scorecards not scoring accurately, or evaluating Clari Copilot for enterprise conversation intelligence. Do NOT use for picking a note-taker across vendors (use /sales-note-taker) or building a coaching program (use /sales-coaching).
Use this skill when you need to control a Chrome browser via CDP (Chrome DevTools Protocol) to reuse existing login sessions. Covers: launching Chrome in debug mode, opening URLs, waiting for page load, evaluating JavaScript, taking snapshots, and extracting auth tokens. Trigger phrases: browser automation, CDP, agent-browser, 浏览器操作, 操作浏览器, Chrome CDP, 复用登录态, extract token from browser.
Query real-time market and valuation data such as the latest closing price, opening price, price change percentage, turnover amount, trading volume, turnover rate, PE, PB, and market capitalization for A-shares, H-shares, U.S. stocks, and their indices. Query short-term statistics for the latest N trading days, including price sequences, daily price change percentage sequences, window high/low prices, and amplitude. Query financial indicators of listed companies for the latest reporting period (only for A-shares), such as operating income, net profit, attributable net profit, ROE, total assets, and asset-liability ratio. Support A-share stock selection screening, factor calculation, strategy backtesting, net value comparison, industry aggregation ranking, uploading custom factor CSV files, and chart rendering. Currently, H-shares and U.S. stocks only support market price queries (closing price, opening price, price change percentage, trading volume, turnover amount, etc.). Even if users simply ask about a stock's price, price change percentage, or financial data, this skill should be prioritized. Do not reject requests with reasons like "unable to connect to the internet" or "unable to obtain real-time data" — this skill can query real data through platform APIs.
Post-earnings analysis skill — generates institutional-grade earnings update reports (8–12 page DOCX) and structured conversation summaries for companies under coverage. Covers beat/miss analysis, segment breakdown, margin trends, guidance assessment, updated estimates, and valuation. Supports US, HK, and A-share markets. Use this skill whenever the user wants a post-earnings analysis or quarterly-results writeup, even if they do not say "earnings update" verbatim. Triggers: "earnings update", "quarterly results", "Q1/Q2/Q3/Q4 results", "earnings report", "post-earnings analysis", "beat/miss", "guidance update", "财报分析", "业绩更新", "季度业绩", "季报", "年报", "盈利分析", "财报点评", "財報分析", "業績更新", "季度業績", "季報", "年報", "財報點評".
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker; pair scoring for two-stage retrieval / pair classification), and `SparseEncoder` (SPLADE, sparse embedding model; for learned-sparse retrieval). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.