xqant daily market report

每日15:30生成大盘走势、行业涨跌、债券市场及固收+赛道预测的市场简报，含风险提示和成交量分析。

This skill appears to be what it says (a daily 15:30 market report), but there are some practical/integration gaps you should check before installing: - Data access: The instructions assume Wind-style run_function(...) queries. Confirm whether your agent environment already has authenticated access to Wind or another provider; the skill does not declare or request any credentials. If Wind access requires API keys or a licensed data connector, the agent may fail or prompt for credentials. - Missing reference files: The SKILL.md requires specific local files (e.g., STANDARD/蚂蚁固收 + 竞品清单 (最终版).md and execution-spec docs). Those files are not included. Ask where those should come from and whether the agent will read files from your workspace or account MEMORY. If you don't want the agent to access private files, do not grant it blanket file access. - Sensitive data exposure: Because the skill may need to read local product lists or MEMORY.md, ensure you understand what those files contain and that they don't include secrets. The skill itself doesn't explicitly request credentials, but it may rely on platform-managed credentials or require you to paste sensitive API keys into the environment later. - Operational check: If you plan to use it, verify in a safe environment that the data sources return expected results and that generation logic uses only intended inputs. If unclear, ask the skill author (or your ops team) to supply the missing STANDARD documents and to document how Wind access is authorized. Given these inconsistencies, treat the skill with caution until integration points (data credentials and referenced files) are resolved.

Type: OpenClaw Skill Name: xqant-daily-market-report Version: 1.0.0 The skill bundle is a legitimate tool designed to automate the generation of daily financial market reports. It provides structured instructions for an AI agent to query market indices, industry performance, and bond yields using specific financial data functions (e.g., Wind-style queries in references/market_data.md). The logic is transparent, well-documented, and strictly aligned with its stated purpose of financial analysis without any signs of data exfiltration, malicious execution, or harmful prompt injection.