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Found 1,637 Skills
PE deal sourcing workflow — discover target companies, check CRM for existing relationships, and draft personalized founder outreach emails. Use when sourcing new deals, prospecting companies in a sector, or reaching out to founders. Triggers on "find companies", "source deals", "draft founder email", "check if we've seen this company", or "outreach to founder".
Use this skill when the user says phrases like "get transcript", "transcribe video", "extract script", "help me extract it", "what does this video say", "what did this blogger say", or directly provides a video link requesting content extraction. Even if the user only sends a video link without stating their request, proactively trigger this skill if the context involves benchmark analysis or content extraction. Call video2text.py to obtain the raw transcript, use AI to correct common speech recognition errors, identify the author, and archive it to the benchmark blogger directory. Do NOT trigger for: analyzing viral content patterns (use li-analyzer), recording own topic ideas (use li-recorder), writing own scripts (use li-writer). Use when the user wants to "get transcript", "transcribe video", "extract script", or gives a video link for content extraction. Runs speech-to-text, AI proofreads, and archives to benchmark blogger directory.
Core Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters, leaderboards, membership sets, or session stores, or when reviewing/cleaning up Redis key naming.
Populate `<docs-dir>/features/<slug>.md` for one, several, or every undocumented feature area by dispatching up to 10 parallel subagents — one per feature. The agent docs directory is discovered from `AGENTS.md` — typically `agents-docs/` (the `setup-agentic-repository` default) but may be elsewhere if `--docs-dir` was used. Use whenever the user wants to document features, fill out feature docs, write up specific features (e.g. "document auth and billing"), document all undocumented features, or follow up on `find-features` discovery. This is the natural sequel to `find-features` — that skill identifies what is missing, this skill writes the docs in parallel.
Quick global settings — currency, language, region, units — belong in a persistent, low-profile location such as a header toolbar or footer. These controls are frequent but not primary, so they use small typography and stay out of the main content hierarchy. Use when designing global selectors, locale switchers, or user preference controls that apply across the whole product.
Query financial market data using the finflow CLI tool. Supports A-shares, HK stocks, US stocks, futures, and macro data. Use this skill whenever the user asks about stock quotes (SH600519, 00700, AAPL), market indices, financial news, capital flow, sector rankings, macro calendar, futures, or any financial data. Also trigger when the user mentions stock codes, ticker symbols (e.g. 茅台, NVDA, 腾讯), asks about market sentiment, wants to check portfolio, or needs real-time financial information for investment decisions.
Build, scaffold, extend, deploy, and troubleshoot event-driven AI agents and scheduled serverless agent apps on Azure Functions using azurefunctions-agents-runtime. Use when the user wants a scheduled agent, morning briefing, daily digest, timer agent, inbox summary, email or Teams briefing, background AI workflow, connector-triggered agent, event-driven AI automation, HTTP/chat agent, webhook-style agent, or Azure Functions hosted agent. Covers .agent.md, agents.config.yaml, Foundry gpt-4.1/gpt-5.x model choice, dynamic sessions for code execution and web browsing, built-in chat/API/MCP endpoints, remote MCP servers, Connector Namespaces, Office 365 or Teams MCP tools/triggers, custom Python tools, Agent Skills, azd deployment, local.settings.json, Application Insights, local development, and troubleshooting.
Quickly creates new Claude Code skills or translates ChatGPT projects into Claude Code skills. Handles skill scaffolding, frontmatter, directory structure, and ChatGPT-to-Claude migration. Use when the user wants to 'create a skill,' 'make a new slash command,' 'convert a ChatGPT project,' 'translate a GPT to Claude,' or 'migrate prompts to Claude Code.' For full eval/testing/benchmarking workflows, use skill-creator instead.
Build structured hierarchical memory systems for LLM agents using GAM (General Agentic Memory) with support for text, video, and agent trajectories
Build hierarchical memory systems for AI agents using GAM (General Agentic Memory) with text, video, and long-horizon trajectory support
This skill should be used to search the local Obsidian vault / markdown knowledge base by meaning, not just keywords, using the on-device qmd engine (BM25 + vector + LLM rerank). Trigger when the user asks to "search my vault/notes", "find notes about X", "what do my notes say about Y", "do I have anything on Z", "semantic search my knowledge base", or wants concept/cross-lingual retrieval over markdown. Fully local — nothing leaves the machine.
Multi-agent deep research for comprehensive market analysis using the aipa CLI. Use this skill when the user asks for deep research, thorough market analysis, sector-wide investigation, comprehensive stock comparison, or detailed financial report. This runs a supervisor → parallel workers → aggregator → reviewer pipeline that takes longer but produces more thorough results than a simple analyze. Trigger for requests like "research banking sector", "deep dive into real estate stocks", or "comprehensive market overview". Can also incorporate fundamental analysis (PE, ROE, NPL, CAR, financial ratios) via `aipa fundamentals` when the user asks for fundamental context alongside technical research.