Skills de Claude Code · página 15

On-chain data analysis — active addresses / whale tracking / TVL / DEX liquidity, interpretation and signal generation using on-chain valuation metrics such as MVRV / NVT / SOPR.

Advanced options strategies: volatility-surface modeling (SABR / Local Vol), dynamic Greeks rebalancing, calendar spreads, volatility arbitrage and skew trading, and option market-making basics.

Option P&L analysis methodology: payoff diagrams, breakeven calculation, multi-leg strategy visualization, and Greeks-based scenario analysis.

Options strategy framework supporting Black-Scholes pricing, Greeks analysis, and multi-leg backtesting. Suitable for cryptocurrency and equity options.

Pair trading strategy. Trades mean reversion using the spread/ratio Z-score of two correlated instruments. Requires at least two instruments.

Performance attribution analysis — Brinson sector/stock-selection attribution, factor alpha/beta decomposition, market-timing evaluation, and benchmark comparison framework.

Perpetual futures funding rate analysis and cash-carry basis trading — funding rate regimes, annualized basis signals, carry trade construction, and funding rate arbitrage between exchanges.

Export backtest strategies to indicator/strategy code for major trading platforms — TradingView, 通达信, 同花顺, 东方财富, MT5.

Quantitative statistical methods: ADF unit-root / cointegration tests, GARCH volatility modeling, regression diagnostics (heteroskedasticity / autocorrelation), Bootstrap, and hypothesis testing.

金融监管知识库：A股涨跌停/ST退市新规/融券、港股T+0/做空机制、美股PDT/熔断、加密监管政策、跨境税务基础

Professional financial research report generation — standard structure (summary / views / main body / risks / recommendation), Markdown formatting standards, rating system, and terminology guide.

Goal-driven finance research workflow: attach a research-only objective, track criteria, and add evidence while avoiding live trading execution.

Risk measurement and stress testing — VaR/CVaR/max drawdown calculation, Monte Carlo simulation, extreme-value tail-risk analysis, and historical scenario stress testing.

Seasonal/calendar-effect strategy. Generates trading signals from time-based patterns such as month-of-year effects and day-of-week effects. Suitable for any OHLCV data.

行业轮动分析——申万行业景气度评分、行业动量排名、产业链传导、估值/盈利/资金流多维比较框架

市场情绪分析——恐贪指数/Put-Call Ratio/融资融券/北向资金信号解读、社交媒体舆情量化框架

Shadow Account — 从用户交割单提炼盈利模式（3-5 条人话规则）→ 跨 A股/港股/美股/crypto 多市场回测 → 差值归因 → 8-section PDF 报告。叙事：你的影子，没有情绪噪音。

Smart Money Concepts (ICT) signal engine. Uses the smartmoneyconcepts library to implement institutional-trading-school analysis of BOS, ChoCH, FVG, and order blocks (OB).

Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.

Stablecoin supply and flow analysis — USDT/USDC mint-burn signals, exchange stablecoin reserves, on-chain stablecoin velocity, and capital rotation indicators for crypto market timing.

Create, modify, and optimize quantitative trading strategies, then backtest and evaluate them.

Core technical indicator collection (trend EMA/ADX + mean-reversion BB/RSI + volume-price OBV/volume ratio), generates a composite signal via three-dimensional voting. Pure pandas implementation for any OHLCV data.

Token unlock schedule analysis and project treasury tracking — vesting cliffs, linear unlocks, team/investor/ecosystem token releases, treasury diversification, and sell pressure forecasting.

Analyze a user's trade journal (CSV/Excel broker export). Parses 同花顺/东方财富/富途/generic formats, produces a trading profile and 4 behavior diagnostics (disposition effect, overtrading, chasing, anchoring). Use the `analyze_trade_journal` tool.

tushare是一个财经数据接口包，拥有丰富的数据内容，如股票、基金、期货、数字货币等行情数据，公司财务、基金经理等基本面数据。该模块通过标准化API方式统一了数据资产的对外服务方式，以帮助有需要的技术用户更实时、简洁、轻量的使用相关数据。

US ETF fund flow analysis, sector rotation breadth, and style factor flows — track institutional capital movement via ETF creation/redemption, sector breadth signals, and thematic momentum.

Valuation methodology — absolute valuation with DCF / DDM / SOTP, relative valuation with PE-Band / PB-ROE / EV-EBITDA, sensitivity analysis, and valuation-trap detection.

Export a Vibe-Trading backtest strategy to a runnable vnpy CtaTemplate Python class — supports A-share equities, futures, and crypto via BarGenerator + ArrayManager.

Volatility strategy. Trades mean reversion based on percentile ranking of historical volatility (HV). Suitable for any OHLCV data.

Read web pages, articles, and document links by converting URLs into Markdown text. Use the `read_url` tool directly, without bash. Sends the full URL to the third-party Jina Reader (r.jina.ai).

yfinance global market data interface — retrieve OHLCV, financials, insider transactions, and institutional holdings for US stocks, HK stocks, ETFs, and indices via Yahoo Finance. Free, no API key required.

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Use when creating, drafting, or grooming a Linear or Superset ticket in this repo. Defines the canonical three-section structure.

Create workspaces, spawn agents, schedule automations, and manage Superset projects/tasks/hosts via the `superset` CLI. Use to orchestrate coding agents across devices from the terminal.

SOP for debugging browser automation failures on complex websites. Use when browser tools fail on specific sites like LinkedIn, Twitter/X, SPAs, or sites with Shadow DOM.

Claim tasks, record step progress, and verify SOP gates in the colony SQLite queue. Applies when your spawn message includes a db_path field.

Proactively extract critical values from tool results into working notes before automatic context pruning destroys them.

Follow a structured recovery decision tree when tool calls fail instead of blindly retrying or giving up.

Maintain a free-form scratchpad of decisions, extracted values, and open questions so context pruning doesn't lose anything you still need.

Periodically self-assess output quality to catch degradation before the judge does.

Author a new Agent Skill for a Hive agent that conforms to the Agent Skills specification (SKILL.md with YAML frontmatter, optional scripts/references/assets directories). Use when the user asks to create, scaffold, add, or package a new skill for a Hive agent.

Required before any browser_* tool call. Teaches the screenshot + browser_click_coordinate workflow that reaches shadow-DOM inputs selectors can't see, the CSS-pixel coordinate rule (not physical px), rich-text editor quirks ("send button stays disabled" failures), and CSP gotchas. Covers Chrome via CDP through the GCU Beeline extension. Skipping this causes repeated failures on LinkedIn / Reddit / X. Verified against real production sites 2026-04-11.

Required reading whenever any chart_* tool is available. Teaches the one-tool embedding contract (call chart_render → live chart appears in chat AND a downloadable PNG lands in the queen session dir), the ECharts (data viz) vs Mermaid (structural diagrams) decision, the BI/financial-grade aesthetic baseline (no chartjunk, restrained palette, proper typography, single message per chart), and the canonical spec patterns for the 12 most-common chart types. Skipping this leads to 1990s-Excel charts, missing downloads, and the agent writing markdown image links by hand instead of letting chart_render drive the UI.

Read before automating LinkedIn with browser_* tools. LinkedIn combines shadow DOM (#interop-outlet), strict Trusted Types CSP that silently drops innerHTML, Lexical composer, native beforeunload dialogs that hang the bridge, and aggressive spam filters — each has bitten us at least once. Verified flows for profile messaging, connection-request acceptance, feed composition, and search. Requires hive.browser-automation. Verified against logged-in production 2026-04-11.

Required reading whenever any shell_* tool is available. Teaches the foreground/background dichotomy (terminal_exec auto-promotes past 30s, returns a job_id you poll with terminal_job_logs), the standard envelope shape (exit_code, stdout, stdout_truncated_bytes, output_handle, semantic_status, warning, auto_backgrounded, job_id), output handle pagination via terminal_output_get, when to read semantic_status instead of raw exit_code (grep/rg/find/diff/test exit 1 is NOT an error), the destructive-warning surface (rm -rf, git push --force, DROP TABLE), tool preference (use files-tools / gcu-tools / hive_tools before raw shell), and the bash-only-on-macOS policy. Skipping this leads to "tool returned no output" surprises, orphaned jobs, and panic over benign grep exit codes.

Use terminal_rg / terminal_find when you need raw filesystem search outside the project tree — system configs, /var/log, /etc, archive contents — or when files-tools.search_files is too project-scoped. Teaches the rg vs find vs terminal_exec("ls/du/tree") split, common rg flag combos for code/logs/configs, find predicates for mtime/size/type queries, and the rule that for tree views or single-file stat info you should just use terminal_exec instead of inventing a tool. Read before reaching for raw shell to grep or find anything.

Use when launching anything that runs longer than a minute, anything that streams logs, anything you want to keep running while doing other work — or when terminal_exec auto-backgrounded on you and returned a job_id. Teaches the start→poll→wait pattern with terminal_job_logs offset bookkeeping, the `wait_until_exit=True` blocking-poll idiom, the truncated_bytes_dropped resumption signal, the merge_stderr decision, the SIGINT→SIGTERM→SIGKILL escalation ladder via terminal_job_manage, and the hard rule that jobs die when the terminal-tools server restarts. Read before calling terminal_job_start, or right after terminal_exec auto-backgrounded.

Use when you need state across calls — building env vars, navigating with cd, driving REPLs (python -i, mysql, psql, node), or responding to interactive prompts (sudo password, ssh host-key confirmation, mysql connection). Teaches the prompt-sentinel exec pattern (default mode), raw I/O for REPLs (raw_send=True then read_only=True), the one-in-flight-per-session rule, and the close-or-leak-against-the-cap discipline. Bash on macOS — never zsh; explicit shell=/bin/zsh is rejected. Read before calling terminal_pty_open.

Read when a terminal-tools call returned something surprising — empty stdout despite no error, exit_code is null, output_handle came back expired, "too many jobs" / "session busy" / "too many PTYs", warning was set unexpectedly, semantic_status disagrees with exit_code. Diagnostic recipes only — load on demand. Don't preload; the foundational skill covers the happy path.

Read before automating X / Twitter with browser_* tools. Verified flows for post, reply, delete, search-and-engage, plus the Draft.js compose quirks that silently disable the send button. Includes the daily-reply and job-market-reply playbooks. Requires hive.browser-automation for the underlying screenshot + coordinate workflow. Verified 2026-04-11.

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

Build and maintain documentation sites with Mintlify. Use when

Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.

Design system conventions for the Phoenix frontend — layout, dialogs, error display, BEM CSS class naming, and CSS design tokens. Use when building UI, naming CSS classes, creating or consuming tokens, handling errors, or designing dialog interactions in app/src/.

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Build and run evaluators for AI/LLM applications using Phoenix.

Frontend development guidelines for the Phoenix AI observability platform. Use when writing, reviewing, or modifying React components, TypeScript code, styles, or UI features in the app/ directory. Triggers on any frontend task — new components, UI changes, styling, accessibility fixes, form handling, or component refactoring. Also use when the user asks about frontend conventions or component patterns for this project. For design system rules (error display, layout, dialogs, tokens), use the phoenix-design skill.

Manage GitHub issues, labels, and project boards for the Arize-ai/phoenix repository. Use when filing roadmap issues, triaging bugs, applying labels, managing the Phoenix roadmap project board, or querying issue/project state via the GitHub CLI.

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Write Playwright E2E tests for the Phoenix AI observability platform. Use when creating, updating, or debugging Playwright tests, or when the user asks about testing UI features, writing E2E tests, or automating browser interactions for Phoenix.

Screenshot a running Phoenix feature and attach images to a GitHub PR. Builds the frontend, starts Phoenix with env vars, uses agent-browser to capture screenshots, uploads to GCS, and updates the PR body.

Write, extend, and debug PXI Playwright E2E tests for Phoenix. Use when adding PXI agent frontend specs, authoring LLM-as-judge rubrics, asserting PXI tool use, persisting PXI test runs as Phoenix experiments, or debugging PXI E2E failures.

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OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.

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TypeScript conventions and patterns for any TypeScript code in the Phoenix monorepo — including js/packages/, app/, and any other TS directories. Use this skill whenever writing, reviewing, or modifying TypeScript code — new functions, types, exports, tests, or refactors. Also trigger when the user asks about TS patterns, naming conventions, or best practices for this project.

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Migrate or upgrade TypeScript tooling in the Phoenix monorepo. Use when upgrading TypeScript versions, switching tools (ESLint to oxlint, Prettier to oxfmt), upgrading bundlers (Vite, esbuild), or making major dependency upgrades. Triggers on requests to migrate, upgrade, or replace TypeScript/JavaScript tooling.

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

Open-source AI observability platform for tracing, evaluating, and improving LLM applications with OpenTelemetry integration

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Author or refine a Phoenix LLM-as-a-judge evaluator — design the judge prompt, classification labels, input mapping, and test payload. Load before proposing edits to an LLM-evaluator draft, including single-shot judge rewrites.

Author, edit, or iterate on prompts in the Phoenix prompt playground, including running experiments over a dataset. Load before any playground tool call, including single-shot prompt rewrites.

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First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the reviewer has accumulated finding outcomes.

Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt, and save the refined version. Use this once outcomes exist; use bootstrap-repo-analysis for a cold-start repo.

Comprehensive backend development guide for Node.js/Express/TypeScript microservices. Use when creating routes, controllers, services, repositories, middleware, or working with Express APIs, Prisma database access, Sentry error tracking, Zod validation, unifiedConfig, dependency injection, or async patterns. Covers layered architecture (routes → controllers → services → repositories), BaseController pattern, error handling, performance monitoring, testing strategies, and migration from legacy patterns.

Add Sentry v8 error tracking and performance monitoring to your project services. Use this skill when adding error handling, creating new controllers, instrumenting cron jobs, or tracking database performance. ALL ERRORS MUST BE CAPTURED TO SENTRY - no exceptions.

Frontend development guidelines for React/TypeScript applications. Modern patterns including Suspense, lazy loading, useSuspenseQuery, file organization with features directory, MUI v7 styling, TanStack Router, performance optimization, and TypeScript best practices. Use when creating components, pages, features, fetching data, styling, routing, or working with frontend code.

Test authenticated routes in the your project using cookie-based authentication. Use this skill when testing API endpoints, validating route functionality, or debugging authentication issues. Includes patterns for using test-auth-route.js and mock authentication.

Create and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation, or implementing progressive disclosure. Covers skill structure, YAML frontmatter, trigger types (keywords, intent patterns, file paths, content patterns), enforcement levels (block, suggest, warn), hook mechanisms (UserPromptSubmit, PreToolUse), session tracking, and the 500-line rule.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.