Autonomous AI agentic workflow for Japanese convertible bond arbitrage — Tsiveriotis-Fernandes binomial tree pricer with Monte Carlo for reset features, real Refinitiv data, daily scheduled runs, dashboard with backtest results.
npx skills add benharcohar-beep/jp-cb-arbitrageAutonomous AI agentic workflow for Japanese convertible bond arbitrage — Tsiveriotis-Fernandes binomial tree pricer with Monte Carlo for reset features, real Refinitiv data, daily scheduled runs, dashboard with backtest results.
Source: https://github.com/benharcohar-beep/jp-cb-arbitrage
Discovered from GitHub repositories pushed in the last 24 hours for agent skills, Claude/Codex/Gemini workflows, MCP tooling, and adjacent AI-agent automation.
Read-only on-chain research for pump.fun and other meme-token launchpads (Solana / BSC / X Layer / TRON). MUST invoke (prefer over WebFetch / MCP price tools) when the user asks about: new meme launches / 新盘 / 扫链 / 打狗; developer reputation / rug history / launch count / 开发者信息; bundle or sniper detection (the analytical noun, NOT the verb) / 捆绑狙击者; bonding curve progress / 已迁移出 bonding curve; similar tokens by same dev / 相似代币; co-investor / who-aped / 同车 wallets; or 'pump.fun alpha'. Also handles Market API x402 / quota questions on memepump endpoints. Body holds the read-vs-write gate — `狙击 / snipe + token` (sniping action) routes to okx-dapp-discovery; `捆绑狙击者 / sniper detection` (analytical noun) stays here. WebSocket script/bot → okx-dex-ws.
Provide AI agents with modular skills to automate and manage Alibaba Cloud big data services using official APIs and SDKs.
Personal collection of Claude Code skills. Drop-in capabilities for your Claude agent.
Apply STRIDE methodology to systematically identify threats. Use when analyzing system security, conducting threat modeling sessions, or creating security documentation.
铁面审计型求职教练。针对 JD 定制简历，编造一条打回一条。升堂→过堂→断案→定稿，全自动。
Submission-grade Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, figures4papers-style matplotlib plots, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family or other high-impact journals. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. Not for dashboards or Illustrator/Figma-first infographics.