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Found 1,266 Skills
Use this skill when a VP Payroll, Head of People Operations, or Payroll Manager needs to manage all employee and contractor compensation flows — including payroll runs, salary administration, statutory deductions, benefits administration, equity grants and vesting, variable pay bonuses, contractor invoice processing, and full payroll compliance across jurisdictions. This skill orchestrates the salary management sub-skill. Trigger when running payroll, onboarding employees with compensation packages, processing salary changes, calculating bonuses, managing equity schedules, processing contractor payments, handling payroll tax filings, or producing total compensation reports for People and Finance leadership.
Check if the development environment is configured properly; if not, first confirm the office location (Chongqing/Beijing), identify the current operating system, system tools, package managers, and eliteforge-* skill environment variable declarations, report them categorized as missing_required, missing_conditional, optional_unset, then attempt to automatically install essential commands and complete configurations such as hosts, Git global settings, Git HTTPS, npm/pip private sources, and pipx packages. Use this skill when the user mentions "check environment configuration", "prepare development environment", "missing commands/hosts/private sources/Git configurations/package management tools/skill environment variables". Trigger threshold: Only use this skill when the user explicitly states that the current project complies with the "璀璨工坊规范" (Bright Workshop Specification) or "eliteforge specification".
Give your AI agents capabilities through tools (function calling). Helps you identify what your AI needs to do, create tool definitions, and attach them to AI Config variations.
Refactor MoonBit code to be idiomatic: shrink public APIs, convert functions to methods, use pattern matching with views, add loop invariants, and ensure test coverage without regressions. Use when updating MoonBit packages or refactoring MoonBit APIs, modules, or tests.
Query DTS (Data Transmission Service) task status and details across all Alibaba Cloud regions. **v12.1: Enhanced reliability** - Timeout increased to 10s, exponential backoff (0.2s, 0.4s) for better timeout handling. Parallel execution remains **6-8x faster** than v10 (39s → 6s with --workers 16). **API retry logic ensures consistent results (no count variations)**. Supports filtering by instance ID or job name. Automatically polls all 27 regions and 3 job types. Strictly filters for PrePaid/PostPaid tasks and outputs a full Chinese report with Region information. Tasks are grouped by type (Migration/Sync/Subscribe) and sorted by CreateTime within each group. **Use this skill when: checking DTS task status, finding migration/sync tasks, verifying task counts, or filtering tasks by instance ID or job name.**
Run a Bayesian A/B test on conversion data using PyMC. Use when the user wants to compare two variants (landing pages, emails, pricing, UI changes) and decide which to ship using posterior probabilities and expected loss instead of p-values. Covers Beta-Binomial model, ROPE, expected loss, sample-size guidance, and ArviZ diagnostics.
Guides the user through building composite score workflows when they ask about composite scores, indexes, multi-variable scores, ranking areas, site scoring, market potential, resilience indexes, risk indexes, weighted scores, PCA, or supervised/unsupervised scoring.
Builds Geographically Weighted Regression (GWR) workflows in CARTO. Triggers when the user mentions GWR, geographically weighted regression, spatially varying relationships, local regression, local coefficients, spatial regression, "what drives X in different areas", "why do prices vary spatially", "local factors affecting Y", varying coefficients, coefficient maps, spatial non-stationarity, or wants to model how the relationship between a dependent variable and predictors changes across geography. Produces per-cell regression coefficients that reveal how predictor importance shifts from place to place.
A-share Market Daily Review System. Actively invoked when users mention needs such as market review, market analysis, or tomorrow's market prediction. Covers: Market Environment, Sentiment Cycle, Main Line Identification, Capital Monitoring, Post-Market Variables, Tomorrow's Combat Map. For research reference only, does not constitute securities investment consulting business or investment advice.
Quantitative signal scanning and position sizing tool based on the original Turtle Trading method. It retrieves market data for A-shares / Hong Kong stocks / US stocks / Singapore stocks via longbridge CLI, and automatically calculates ATR (N value), breakout signals (System 1 / System 2), stop-loss prices, add-on positions, and Unit position sizes. Trigger this tool when users mention 海龟, turtle, 海龟交易, 海龟信号, turtle signal, turtle trading, or ask about breakout signals, ATR, N value, Unit positions, stop-loss prices, add-on positions, S1/S2 signals, 20-day high/low, 55-day breakout, or request to scan watchlists/indexes for trading signals using the turtle system. It also triggers when users say "扫描突破信号", "帮我算Unit", "海龟止损", "海龟系统分析", or any combination of a stock name/code with "海龟". **Applicable scenarios:** - Scan for breakout signals (20-day/55-day high/low breakouts) after daily market close - Calculate ATR, stop-loss prices, and add-on positions for single stocks or batches of targets - Calculate reasonable Unit position sizes based on account net assets - Determine whether existing positions trigger exit or add-on conditions - Scan turtle signals for watchlist stocks / index components **Not applicable for:** - Fundamental analysis (Turtle system is purely technical) - Predicting price direction - Automatic order placement (only outputs signals; users operate on their own) - Short-selling opening operations for A-shares/Hong Kong stocks/Singapore stocks
Design and operate an advanced AI agent memory system on HelixDB using hybrid graph + vector + BM25 search. Use when building long-term memory, user profiles, document/chunk RAG, recall/remember features, memory extraction, deduplication, consolidation, versioning, updating, forgetting/deletion, categorisation, or connector-backed ingestion. Covers tenant-safe Helix data modeling, modality decision rules, the full write/maintain lifecycle, and the product layers an agent must implement around Helix. TypeScript-first (@helix-db/helix-db); a Rust DSL variant is in EXAMPLES.rust.md.
Adversarial senior-engineer review for agent-generated plans, designs, and architectures. Treats the current output as junior work, constructs a senior reviewer whose domain expertise comes from live codebase research plus web research of current best practices, diagnoses altitude failures (too vague or too granular), then rewrites the plan into a scoped, state-of-the-art version. Use when the user says "junior to senior", "senior review", "review this like a staff engineer", when a plan feels hand-wavy or lost in details, or before committing to any agent-written plan.