ReMe is a local-first memory management system that turns conversations and other resources into searchable, editable Markdown knowledge shared across AI agents. It is for users and agent runtimes that need durable, interconnected knowledge they can inspect and maintain. The catalogue add-ons connect coding agents and other agents to ReMe through skills, hooks, plugins, MCP, and related interfaces.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/agentscope-ai/reme/tushare-datanpx skills add agentscope-ai/ReMe --skill tushare-datagit clone --depth 1 https://github.com/agentscope-ai/ReMeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentscope-ai/reme/tushare-data)<a href="https://agentmods.dev/skills/agentscope-ai/reme/tushare-data"><img src="https://agentmods.dev/badge/skills/agentscope-ai/reme/tushare-data.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00122 | $0.05265 |
| Opus 5 | $0.00061 | $0.02632 |
| Sonnet 5 | $0.00024 | $0.01053 |
| Haiku 4.5 | $0.00012 | $0.00526 |
Grade A, and why
tushare-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 823 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tushare-data
把自然语言财经数据请求，转成可执行的 Tushare 数据工作流。
这是一个面向自然语言的金融数据研究 skill。
What this skill is for
使用这个 skill 的典型场景：
- 看某只股票、指数、ETF 最近走势
- 查公司基本资料、估值、财务趋势
- 做多标的横向对比
- 看资金流、北向资金、龙虎榜、板块强弱
- 梳理公告、新闻、研报、政策线索
- 查看 CPI / PPI / PMI / 社融 / 利率等宏观数据
- 导出 CSV / parquet 供后续分析或回测使用
- 生成简洁研究摘要，而不是只吐原始字段表
先理解用户要解决什么问题，再去选接口、取数、整理、解释、交付。
When to use
当用户表达以下意图时，优先使用本 skill：
行情 / 趋势
- 看下 XX 最近怎么样
- XX 这段时间涨得怎么样
- 今年以来表现如何
- 最近有没有放量
- 这票最近强不强
财务 / 估值 / 公司质量
- 看下 XX 财报
- 最近几个季度利润趋势
- 财务质量怎么样
- 现金流好不好
- 现在估值算高吗
- 帮我看 PE / PB / ROE / 毛利率
对比 / 排行 / 筛选
- XX 和 YY 谁更强
- 帮我横向比较一下
- 哪些公司利润增长更快
- 帮我筛一下高 ROE 低负债
- 给我排个前十
板块 / 指数 / 主题
- 最近哪个板块最强
- 半导体最近怎么样
- 机器人为什么涨
- 指数成分股有哪些
- 哪些主题最热
资金流 / 情绪
- 最近资金在买什么
- 北向资金最近流向哪里
- 哪个板块最吸金
- 主力资金流入最多的是谁
- 龙虎榜上有什么看点
公告 / 新闻 / 研报 / 政策
- 最近有什么公告
- 帮我梳理下 XX 公告
- 最近有没有什么催化
- 最近新闻面怎么样
- 最近有什么重要政策
宏观 / 跨市场
- 最近宏观环境怎么样
- CPI / PMI 最近怎么看
- 当前市场风格偏什么
- 大盘环境偏多还是偏空
- 港股 / 美股 / 美债最近怎么样
数据导出 / 研究准备
- 给我导出一份行情数据
- 把近两年日线拉成 CSV
- 生成可回测的数据表
- 拉一个研究表供后续分析
What this skill is NOT for
这个 skill 不适合：
- 直接给买卖建议或替代投资顾问
- 自动下单或执行交易
- 需要毫秒级实时交易决策的场景
- 复杂回测引擎、组合优化系统本身的实现（那是另一个工程）
- 在没有 Tushare 权限/积分支持的情况下强行伪造数据
如果数据权限不够、接口不可用或时间范围不合理，要明确说出限制，不要硬编。
Natural-language trigger guide
即使用户完全不说 tushare、financials、macro 这些术语，只要意图符合以下含义，也应该触发本 skill。
常见口语触发
- 看看这个股票最近怎么样
- 给我快速研究一下 XX
- 上次说的那只票现在什么情况
- 帮我看下财报
- 最近哪个板块最强
- 北向最近在买什么
- 有什么催化消息
- 这个公司值不值得重点看
- 给我拉份数据
- 导出成 CSV
- 帮我筛一批票
- 把这几个公司对比一下
中文自然语言优先原则
用户说人话时，先理解任务，不要先回到接口名和字段名。 优先把：
- “最近” 解释成合理时间窗
- “财报” 解释成最近 8 个季度 / 最近年度
- “强不强” 解释成走势 + 相对强弱 + 活跃度
- “资金关注” 解释成净流入、活跃成交、龙虎榜/北向等可用口径
如果任务有多个合理解释，再做最小澄清。
Environment check
在真正请求数据之前，先做前置校验：
- 检查 Python 是否可用， 版本要求 3.7+
- 检查
tushare包是否已安装· - 检查
TUSHARE_TOKEN是否存在. - 必要时做一次轻量接口冒烟测试（如交易日历 / 基础接口）
- 如用户请求高权限接口，提前提示可能存在积分/权限限制
若缺失 token，直接提示最短修复路径，例如：
export TUSHARE_TOKEN=your_token
不要等到主查询跑失败了才暴露环境问题。
Intent taxonomy
先识别任务类型，再决定接口组合。
1. 行情 / 趋势
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 823 lines · 122 tokens per session scan A cff55673023e
tushare-data is a skill published in the GitHub repository agentscope-ai/ReMe (3,416 stars, last pushed 2d ago), licensed Apache-2.0. It adds 122 tokens to every session and 5,265 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
browserwing-admin
Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.
wegent-knowledge
Knowledge base management and search tools for Wegent. Provides capabilities to list, create, update, and search knowledge bases and documents using RAG retrieval. Use this skill when the user wants to manage knowledge bases, documents, or search for information programmatically.
finance-tracker
Track expenses, analyze spending patterns, categorize transactions. Works with bank CSV exports or manual input.
memori-mcp-usage
Use when an MCP-connected agent should use Memori tools for targeted recall, summaries, post-compaction briefs, durable memory augmentation, quota checks, signup, feedback, preferences, prior context, or cross-session continuity.
memori
You have access to Memori, agent-native memory infrastructure: an LLM-agnostic layer that structures memory from not just natural language, but also from agent trace that comes from execution.
memori
Ambient Memori long-term memory for Claude Code via local Bash. MUST TRIGGER on essentially every non-trivial user turn: run recall before drafting any substantive response, whether or not the user mentioned memory, a prior session, or past work. The default is to check memory first; do not wait to be asked. Always…