04-22 08:03 · AI技能,内容生成,生态扩展,技术迭代,平台能力
根据文字描述或参考图片生成视频的技能
ClawHub Security flagged this skill as suspicious. Review the scan results before using.
AI视频生成与编辑，使用火山引擎 Doubao Seedance 模型。支持文生视频、图生视频、有声视频。当用户要求生成视频、制作视频、文生视频、图生视频时使用此 skill。
Like a lobster shell, security has layers — review code before you run it.
通过火山引擎 Doubao Seedance API 生成视频。
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04-22 08:03 · API服务,平台生态,功能增强,工具集成,技能更新
获取当前天气和天气预报的实用技能
Get current weather and forecasts (no API key required).
Like a lobster shell, security has layers — review code before you run it.
Two free services, no API keys needed.
Format codes:%ccondition ·%ttemp ·%hhumidity ·%wwind ·%llocation ·%mmoon
Free, no key, good for programmatic use:
Find coordinates for a city, then query. Returns JSON with temp, windspeed, weathercode.
Docs:https://open-meteo.com/en/docs
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04-22 08:02 · AI安全,开源工具,代理监控,生产部署,大模型应用
CrabTrap: An LLM-as-a-judge HTTP proxy to secure agents in production
Brex LLC is a wholly owned subsidiary of Capital One, N.A.
Brex LLC | 650 S 500W Suite 300 | Salt Lake City, UT 84101
The Brex business account consists of Checking, a commercial checking account provided by Column N.A., Member FDIC (an unaffiliated institution), and Treasury and Vault, cash management services provided by Brex Treasury LLC, Member FINRA/SIPC and a Capital One company.
Securities are offered through Brex Treasury LLC. Funds in Treasury are not FDIC-insured. Funds in Vault at program banks are eligible for FDIC insurance. Funds are not FDIC-insured until they arrive at program banks. Conditions apply.
Investing in securities involves risk and loss of money. Yield and return are variable and fluctuate. Past performance is not a guarantee of future results. This is not an offer to, or implied offer, or a solicitation to, buy or sell any securities. Brex Treasury LLC does not provide legal, tax, or investment advice. The latest statement of financial condition for Brex Treasury LLC is available here. You could lose money by investing in the Fund. Although the Fund seeks to preserve the value of your investment at $1.00 per share, it cannot guarantee it will do so. An investment in the Fund is not insured or guaranteed by the FDIC or any other government agency.
The Brex Mastercard® Corporate Credit Card is issued by Emigrant Bank, Fifth Third Bank N.A., or Airwallex (Netherlands) B.V. (all unaffiliated institutions), pursuant to licenses by Mastercard International Inc. Mastercard is a registered trademark, and the circles design is a trademark of Mastercard International Inc. The Brex Commercial Card is issued by Sutton Bank (an unaffiliated institution), pursuant to a license from Visa® U.S.A. Inc. Can be used where Visa® cards are accepted. No ATM access. All loans are subject to approval, including underwriting, credit, and collateral approval, as well as availability restrictions. Nothing herein should be construed as a commitment to lend.
Certain payment services are provided by Brex Payments LLC, a licensed money transmitter (NMLS #2035354) and a Capital One company.
Some Brex products have associated fees. Plans start at $0 per user, per month, and more advanced features are available for $12 per user, per month.
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04-22 08:01 · 技术迭代,平台生态,AI技能,内容生成,竞争加剧
基于文字描述的图片生成技能，支持多种风格和模型
Image Gen
v2.0.1Generate images using multiple AI models — Midjourney (via Legnext.ai), Flux, Nano Banana Pro (Gemini), Ideogram, Recraft, and more via fal.ai. Intelligently...
Like a lobster shell, security has layers — review code before you run it.
Runtime requirements
Install
npm i -g @fal-ai/client
Image Generation Skill
This skill generates images using the best AI model for each use case. Model selection is the most important decision — read the dispatch logic carefully before generating.
🧠 Intelligent Dispatch Logic
Always select the model based on the user's actual need, not just the request surface.
Decision Tree
Does the request involve MULTIPLE images that share characters, scenes, or story continuity?
├─ YES → Use NANO BANANA (Gemini)
│ Reason: Gemini understands context holistically; supports reference_images
│ for character/scene consistency across a series (storyboard, comic, sequence)
│
└─ NO → Is it a SINGLE standalone image?
├─ Artistic / cinematic / painterly / highly detailed?
│ → Use MIDJOURNEY
│
├─ Photorealistic / portrait / product photo?
│ → Use FLUX PRO
│
├─ Contains TEXT (logo, poster, sign, infographic)?
│ → Use IDEOGRAM
│
├─ Vector / icon / flat design / brand asset?
│ → Use RECRAFT
│
├─ Quick draft / fast iteration (speed priority)?
│ → Use FLUX SCHNELL (<2s)
│
└─ General purpose / balanced?
→ Use FLUX DEV
Model Capability Matrix
When to Use Nano Banana (Critical)
Use Nano Banana whenever the user's request involves:
- Storyboard / 分镜图: Multiple frames that tell a story with the same characters
- Comic strip / 漫画: Sequential panels with consistent characters
- Character series: Multiple images of the same person/character in different poses or scenes
- Scene continuation: "Now show the same girl in the forest" (referencing a previous image)
- Style consistency: A set of images that must share the same visual style/world
Nano Banana uses Google's Gemini 3 Pro multimodal architecture, which understands context holistically rather than keyword-matching. It supports up to 14 reference images for maintaining character and scene consistency.
How to Use This Skill
- Analyze the request: Is it a single image or a series? Does it need context continuity?
- Select model: Use the decision tree above.
- Enhance the prompt: Add style, lighting, and quality descriptors appropriate for the model.
- Inform the user: Tell them which model you're using and why, and that generation has started.
- Run the script: Use
exec
tool with sufficient timeout. - Deliver the result: Send image URL(s) to the user.
Calling the Generation Script
node {baseDir}/generate.js \
--model <model_id> \
--prompt "<enhanced prompt>" \
[--aspect-ratio <ratio>] \
[--num-images <1-4>] \
[--negative-prompt "<negative prompt>"] \
[--reference-images "<url1,url2,...>"]
Parameters:
--model
: One ofmidjourney
,flux-pro
,flux-dev
,flux-schnell
,sdxl
,nano-banana
,ideogram
,recraft
--prompt
: The image generation prompt (required)--aspect-ratio
: e.g.16:9
,1:1
,9:16
,4:3
,3:4
(default:1:1
)--num-images
: 1-4 (default:1
; Midjourney always returns 4 regardless)--negative-prompt
: Things to avoid (not supported by Midjourney)--reference-images
: Comma-separated image URLs for context/character consistency (Nano Banana only)--mode
: Midjourney speed:turbo
(default, ~20-40s),fast
(~30-60s),relax
(free but slow)
exec timeout: Set at least 120 seconds for Midjourney and Nano Banana; 30 seconds is sufficient for Flux Schnell.
⚡ Midjourney Workflow (Sync Mode — No --async)
Always use sync mode (no --async
). The script waits internally until complete.
node {baseDir}/generate.js \
--model midjourney \
--prompt "<enhanced prompt>" \
--aspect-ratio 16:9
Understanding Midjourney Output
{
"success": true,
"model": "midjourney",
"jobId": "xxxxxxxx-...",
"imageUrl": "https://cdn.legnext.ai/temp/....png",
"imageUrls": [
"https://cdn.legnext.ai/mj/xxxx_0.png",
"https://cdn.legnext.ai/mj/xxxx_1.png",
"https://cdn.legnext.ai/mj/xxxx_2.png",
"https://cdn.legnext.ai/mj/xxxx_3.png"
]
}
CRITICAL — image field meanings:
"放大第N张" / "要第N张" / "give me image N" = send imageUrls[N-1]
directly. Do NOT call generate.js again.
Midjourney Interaction Flow
After generation:
🎨 生成完成！这是 4 张图的预览： 预览图 你喜欢哪一张？回复 1、2、3 或 4，我直接发给你高清单图。
When user picks image N:
这是第 N 张的单独高清图： 图片 N
🤖 Nano Banana (Gemini) Workflow
Use for storyboards, character series, and any context-dependent multi-image generation.
Single image (no reference)
node {baseDir}/generate.js \
--model nano-banana \
--prompt "<detailed scene description>" \
--aspect-ratio 16:9
With reference images (character/scene consistency)
node {baseDir}/generate.js \
--model nano-banana \
--prompt "<scene description, referencing the character/style from the reference images>" \
--aspect-ratio 16:9 \
--reference-images "https://url-of-previous-image-1.png,https://url-of-previous-image-2.png"
How to build a storyboard series:
- Generate the first frame without reference images (establishes the character/scene)
- Use the fi
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04-22 08:01 · AI代理,技术竞争,人才招聘,初创企业,自动化
Trellis AI (YC W24) Is hiring engineers to build self-improving agents
Trellis builds and deploys computer use agents to get patients access to life-saving medicine.
Our computer-use AI agents process billions of dollars worth of therapies annually with patients in all fifty states. We do this by automating document intake, prior authorizations, and appeals at scale to streamline operations and accelerate care. We classify medical referrals, understand chart notes, and automate contract and reimbursement search to provide patients with accurate coverage determinations and cost responsibility. Think of us as the Stripe of healthcare billing and reimbursements.
Trellis is a spinout from Stanford AI Lab and is backed by leading investors including YC, General Catalyst, Telesoft Partners, and executives at Google and Salesforce.
🧍🏻♂️Why work with us
- Real impact at massive scale: We serve patients in all fifty states and are scaling to hundreds of healthcare locations. You'll directly see the number of patients who received treatment because of the agents you built.
- Work with industry experts: Apply your AI alongside healthcare operations leaders who have overseen 50+ healthcare locations, gaining deep domain expertise while building cutting-edge technology.
- Be at the forefront of AI in healthcare: Build production-grade agentic systems that make critical healthcare decisions, backed by robust evaluation frameworks.
- Direct customer engagement: Work closely with F500 customers and the founding team. You'll wear multiple hats from technical architecture to customer success.
- Extreme ownership: Own key parts of Trellis's technical infrastructure and have opportunities to launch new initiatives that process billions in healthcare transactions.
- World-class team: Join team members who have won international physics olympiads, published economics research, were founding engineers at unicorn startups, and taught AI classes to hundreds of Stanford graduate students.
- Incredible growth and traction: We've grown revenue 10x in the past few months alone and have XX% market share in the specialty healthcare markets we serve.
What you'll build
- Agentic frameworks for healthcare decision-making: Design and implement AI systems that autonomously navigate complex reimbursement logic and prior authorization workflows.
- 24/7 AI co-workers: Build and deploy long-running agent workers that triage and process healthcare data around the clock, functioning as reliable digital teammates for care teams.
- Production-grade AI systems: Develop your agents within our comprehensive evaluation suite, ensuring production-ready performance from day one.
Requirements
- Experience architecting, developing, and testing full-stack code end-to-end
- Expertise in programming languages such as Python, Go and ML/NLP libraries such as PyTorch, TensorFlow, Transformers
- Being proactive and a fast-learner with bias for action
- Experience working with relational and non-relational databases, especially Postgres
- Experience with data and ML infrastructure
- Open source contributions and projects are a big plus
- Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes) is a plus
Trellis helps healthcare providers treat more patients, faster—while eliminating pre-service paperwork.
We automate document intake, prior authorizations, and appeals at scale to streamline operations and accelerate care.
Our AI agent is trained on millions of clinical data points and converts messy, unstructured documents into clean, structured data directly in your EHR.
With Trellis, leading healthcare providers and pharmaceutical companies were able to:
-
Reduce time to treatment by over 90%
-
Improve prior authorization approval and reimbursement rates
-
Leverage structured data to enhance drug program performance and clinical decision-making
Administrative costs account for over 20% of U.S. healthcare spending—delaying care, draining revenue, and driving staff burnout while having less visibility into patient care than ever before. We built Trellis to tackle this head on.
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04-22 08:00 · 技能发布,流程自动化,社交媒体,平台更新,功能增强
OpenClaw微博发帖自动化技能，支持格式校验、定时发帖等功能
weibo-post
v1.0.0发微博（新浪微博）。当用户说"发微博"、"发条微博"、"发到微博"、"发一条微博"、"帮我发微博"、"发到微博"时触发。使用浏览器自动发微博。
Like a lobster shell, security has layers — review code before you run it.
微博发布 Skill
工作流程
-
打开微博
browser(action=navigate, profile="openclaw", url="https://weibo.com")
-
等待页面加载，获取快照
browser(action=snapshot, profile="openclaw", compact=true)
找到发微博的文本框
ref=e35
，以及发送按钮（初始 disabled，填入内容后变为可点击） -
填入微博内容
browser(action=act, profile="openclaw", targetId=<targetId>, ref="e35", kind="type", text=<微博内容>)
⚠️ 内容中换行用
\n
，不要用真正的换行符 -
重新获取快照，确认发送按钮可用
browser(action=snapshot, profile="opencloak", compact=true)
找到发送按钮
ref=e36
-
点击发送
browser(action=act, profile="openclaw", targetId=<targetId>, ref="e36", kind="click")
-
验证发送成功
browser(action=snapshot, profile="openclaw", compact=true)
看到"刚刚"发布的新微博即表示成功
⚠️ 重要：话题标签格式
必须是 #标签#
（前后各一个 #），不是 #标签
✅ 正确：#黄仁勋# #英伟达# #太空数据中心#
❌ 错误：#黄仁勋 #英伟达 #太空数据中心
（少了右边的 #）
注意事项
- 微博内容支持 emoji、话题标签
- 不支持 Markdown 格式（粗体等会被当作普通文本发出）
- 发之前确认内容无误，微博发出后无法编辑删除
- 如果发送按钮仍为
disabled
，再等待一下或重新 snapshot 确认
Comments
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04-22 08:00 · 开源,AI基础设施,技术竞争,开发者工具,模型路由
Show HN: GoModel – an open-source AI gateway in Go
A high-performance AI gateway written in Go, providing a unified OpenAI-compatible API for OpenAI, Anthropic, Gemini, xAI, Groq, OpenRouter, Z.ai, Azure OpenAI, Oracle, Ollama, and more.
Step 1: Start GoModel
docker run --rm -p 8080:8080 \
-e LOGGING_ENABLED=true \
-e LOGGING_LOG_BODIES=true \
-e LOG_FORMAT=text \
-e LOGGING_LOG_HEADERS=true \
-e OPENAI_API_KEY="your-openai-key" \
enterpilot/gomodel
Pass only the provider credentials or base URL you need (at least one required):
docker run --rm -p 8080:8080 \
-e OPENAI_API_KEY="your-openai-key" \
-e ANTHROPIC_API_KEY="your-anthropic-key" \
-e GEMINI_API_KEY="your-gemini-key" \
-e GROQ_API_KEY="your-groq-key" \
-e OPENROUTER_API_KEY="your-openrouter-key" \
-e ZAI_API_KEY="your-zai-key" \
-e XAI_API_KEY="your-xai-key" \
-e AZURE_API_KEY="your-azure-key" \
-e AZURE_BASE_URL="https://your-resource.openai.azure.com/openai/deployments/your-deployment" \
-e AZURE_API_VERSION="2024-10-21" \
-e ORACLE_API_KEY="your-oracle-key" \
-e ORACLE_BASE_URL="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/v1" \
-e ORACLE_MODELS="openai.gpt-oss-120b,xai.grok-3" \
-e OLLAMA_BASE_URL="http://host.docker.internal:11434/v1" \
enterpilot/gomodel
-e
on the command line - they can leak via shell history and process lists. For production, use docker run --env-file .env
to load API keys from a file instead.
Step 2: Make your first API call
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5-chat-latest",
"messages": [{"role": "user", "content": "Hello!"}]
}'
That's it! GoModel automatically detects which providers are available based on the credentials you supply.
Example model identifiers are illustrative and subject to change; consult provider catalogs for current models. Feature columns reflect gateway API support, not every individual model capability exposed by an upstream provider.
✅ Supported ❌ Unsupported
For Z.ai's GLM Coding Plan, set ZAI_BASE_URL=https://api.z.ai/api/coding/paas/v4
.
For Oracle, set ORACLE_MODELS=openai.gpt-oss-120b,xai.grok-3
when the
upstream /models
endpoint is unavailable.
Prerequisites: Go 1.26.2+
-
Create a
.env
file:cp .env.template .env
-
Add your API keys to
.env
(at least one required). -
Start the server:
make run
Infrastructure only (Redis, PostgreSQL, MongoDB, Adminer - no image build):
docker compose up -d
# or: make infra
Full stack (adds GoModel + Prometheus; builds the app image):
cp .env.template .env
# Add your API keys to .env
docker compose --profile app up -d
# or: make image
docker build -t gomodel .
docker run --rm -p 8080:8080 --env-file .env gomodel
GoModel is configured through environment variables and an optional config.yaml
. Environment variables override YAML values. See .env.template
and config/config.example.yaml
for the available options.
Key settings:
Quick Start - Authentication: By default GOMODEL_MASTER_KEY
is unset. Without this key, API endpoints are unprotected and anyone can call them. This is insecure for production. Strongly recommend setting a strong secret before exposing the service. Add GOMODEL_MASTER_KEY
to your .env
or environment for production deployments.
GoModel has a two-layer response cache that reduces LLM API costs and latency for repeated or semantically similar requests.
Hashes the full request body (path + Workflow
+ body) and returns a stored response on byte-identical requests. Sub-millisecond lookup. Activate by environment variables: RESPONSE_CACHE_SIMPLE_ENABLED
and REDIS_URL
.
Responses served from this layer carry X-Cache: HIT (exact)
.
Embeds the last user message via your configured provider’s OpenAI-compatible /v1/embeddings
API (cache.response.semantic.embedder.provider
must name a key in the top-level providers
map) and performs a KNN vector search. Semantically equivalent queries - e.g. "What's the capital of France?" vs "Which city is France's capital?" - can return the same cached response without an upstream LLM call.
Expected hit rates: ~60–70% in high-repetition workloads vs. ~18% for exact-match alone.
Responses served from this layer carry X-Cache: HIT (semantic)
.
Supported vector backends: qdrant
, pgvector
, pinecone
, weaviate
(set cache.response.semantic.vector_store.type
and the matching nested block).
Both cache layers run after guardrail/workflow patching so they always see the final prompt. Use Cache-Control: no-cache
or Cache-Control: no-store
to bypass caching per-request.
See DEVELOPMENT.md for testing, linting, and pre-commit setup.
- Intelligent routing
- Broader provider support: Oracle model configuration via environment variables, plus Cohere, Command A, Operational, and DeepSeek V3
- Budget management with limits per
user_path
and/or API key - Editable model pricing for accurate cost tracking and budgeting
- Full support for the OpenAI
/responses
and/conversations
lifecycle - Prompt cache visibility showing how much of each prompt was cached by the provider
- Guardrails h
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04-22 07:59 · 平台生态集成,办公自动化,SaaS竞争,企业服务,技能市场
飞书文档、云盘、知识库等集成技能，支持自动化办公
Feishu
v1.0.5飞书深度集成技能。不是简单的消息桥接，而是你的数字指挥中枢。专为中国企业高压协作环境设计，理解“分寸”与“效率”两套并行规则，把消息、审批、会议、文档、多维表格、日程与邮箱，压缩成有优先级、可执行的行动链。
Like a lobster shell, security has layers — review code before you run it.
Runtime requirements
飞书
这不是一个简单的飞书桥接工具，而是你的数字指挥中枢。
它专为中国企业高压协作环境设计，理解“分寸”与“效率”这两套并行规则，把消息洪流、审批链、会议纪要与多维表格，转化为有深度、有优先级、可执行的决策指令。
早上八点四十五分。你打开飞书，看到的是这样一幅景象：
群消息 247 条未读，散布在 14 个群里。其中有 3 条需要你今天回复，但它们被淹没在项目讨论、日常闲聊和转发的行业文章中间。你不知道哪 3 条是重要的，除非你把 247 条全部看完。
4 条审批等你处理。其中一条是三天前提交的报销，提交人已经在私信里委婉地问了两次“方便看一下吗”。
你有 6 个会议，其中两个时间冲突。上周五的产品评审会你缺席了，会议纪要还没人写，但今天下午的跟进会需要基于上次的结论继续讨论。
你的 OKR 本周需要更新，但你已经三周没更新了，因为每次打开那个文档你都需要先花二十分钟回忆过去一周到底做了什么。
多维表格里的项目看板显示 4 个任务逾期，但其中 2 个实际上已经完成了只是没人更新状态，另外 2 个你需要去找对应的同事确认进展。
这就是一个普通中国企业中层管理者的周一早晨。
不是因为工作量太大，而是因为信息散落在飞书的每一个角落。把它们捡起来、拼成全貌、做出判断、采取行动——这个过程本身就吞噬了你一天中最清醒的两个小时。
飞书技能要做的事情只有一件：
让这个周一早晨从“信息焦虑”变成“行动清单”。
初始化握手协议（Initialization Handshake）
洞察：高权限能力必须建立在明确授权之上。本技能采用“双轨运行模式”，并在首次调用时强制完成握手。
默认规则
如果用户尚未明确选择模式，本技能必须默认处于 参谋模式，不得擅自执行任何写操作。
模式 A：参谋模式（Counselor Mode）— 默认推荐
- 权限边界： 只读不写
- 行为准则： 提炼情报、预审审批、草拟文案、生成建议
- 执行限制： 发送消息、修改表格、调整日程、触发审批等动作，必须经用户明确确认后才可执行
模式 B：执行模式（Executive Mode）
- 权限边界： 允许在用户授权后执行常规写操作
- 行为准则： 可处理低风险、低歧义、流程型协作动作
- 强制红线： 即便在执行模式下，以下动作仍必须二次确认：
- 向上级或领导发送消息、汇报或催办
- 在跨部门公开群中进行提醒、催办或施压式表达
- 修改核心业务多维表格的关键字段
- 对审批流执行“批准 / 驳回 / 退回”等终审动作
- 对高敏日程做不可逆调整
首次调用提示模板
当用户首次调用本技能，或上下文中尚未确定模式时，智能体应先发出如下提示，再继续后续动作：
飞书中枢已接入。为保障协作安全与权限边界，请选择当前运行模式：
[1] 参谋模式（默认）：我负责读取、分析、草拟，所有写操作需你确认。
[2] 执行模式：我可在授权范围内执行常规写操作，但高敏动作仍需二次确认。
你可直接回复 1 或 2，也可以随时用“切换飞书模式”重新设定。
协作诊断层（Coordination Diagnosis Layer）
飞书中枢不会把所有问题都当成“执行问题”。 在进入摘要、草拟、催办、同步、协调之前，它应先判断当前协作摩擦真正发生在哪一层。
1. 识别摩擦类型
优先判断问题属于哪一种：
- 消息过载：真正的问题是噪音太多、行动项被埋没，而不是“没人回复”
- 审批瓶颈：真正的问题是缺材料、卡节点、责任不清，还是催办方式失分寸
- 会议断层：真正的问题是开完会没有形成决策、待办或责任归属
- 表格滞后：真正的问题是任务真的延期，还是状态没有及时更新
- 日程冲突：真正的问题不是时间碰撞本身，而是优先级排序错误
- 文档失忆：真正的问题不是没有内容，而是找不到、读不完、提炼不出结论
- 沟通风险：真正的问题不是信息缺失，而是对象、层级、场景不适合直接表达
2. 选择最佳层与最佳动作
在识别出摩擦类型后，再决定应该在哪一层处理、采取哪种动作。
- 最佳层（Best Layer）：消息 / 审批 / 会议 / 多维表格 / 日程 / 文档
- 最佳动作（Best Action）：摘要 / 草拟 / 提醒 / 同步 / 协调 / 暂缓 / 升级确认
飞书中枢的价值，不是“做更多动作”， 而是选择最高杠杆、最低摩擦的动作。
3. 默认风险提示
在进入执行链前，应优先压制这些错误动作：
- 上下文缺失：如果线程历史不完整，不应贸然催办或归责
- 过早提醒：在没有确认截止期、责任归属或层级关系前，不应公开施压
- 盲目更新：如果多维表格状态与聊天上下文不一致，不应直接覆盖主记录
- 错层处理：文档检索问题不应被误处理成群聊回复问题
- 升级漂移：本应私下解决的协作摩擦，不应被轻易放大为公开升级
只有先判断问题属于哪一层， 飞书中枢才决定应该摘要、草拟、提醒、协调、同步，还是暂停并请求确认。
能力矩阵
消息中枢
洞察：群消息不是信息问题，而是注意力排序问题。
飞书里的群聊是中国企业协作的主动脉，也是效率黑洞。真正消耗你的，不是消息量，而是你必须自己完成“筛选、归类、判断、响应”这四步。
这个技能将群聊流拆成三层：
- 需要你行动的：有人在等你决策、回复或确认
- 需要你知道的：与你相关但不需要立刻动作的更新
- 可以忽略的：噪音、闲聊、已被他人接住的内容
它不仅能提取消息，还会补足上下文。
你不用重新翻 80 条历史记录，才能明白这一条“你看下”到底在说什么。
核心动作：
- 自动提取需要你处理的消息并排序
- 合并跨群上下文，避免重复判断
- 识别隐性待办、催办、决策请求
- 将聊天流压缩为简报，而不是摘要垃圾堆
审批加速器
洞察：审批流不是单纯流程，它是企业内部资源流动的闸门。
很多审批不是因为“没人看到”而卡住，而是因为信息不完整、责任模糊、催办方式失分寸。
这个技能不会只提醒你“有审批待处理”，它会先做预审：
- 信息是否完整
- 附件是否齐全
- 是否存在明显风险点
- 这一单更适合通过、暂缓，还是补材料再审
核心动作：
- 审批预审：先判断，再提醒
- 风险提示：指出缺项和潜在驳回原因
- 高情商催办：根据关系和层级生成不同催办方式
- 审批数据分析：识别哪个节点最容易堵塞
它的目标不是让你“更快点通过”，
而是让审批链整体更少空转。
会议执行器
洞察：会议的成本不在会议本身，而在“会前没人准备、会后没人执行”。
大多数会议不是缺讨论，而是缺结构。
飞书会议录音和转写本身并不稀缺，稀缺的是：
- 哪些是决策
- 哪些是待办
- 谁负责
- 什么时候交付
- 下次会议应该基于什么继续推进
核心动作：
- 会前简报：自动汇总背景、上次结论、未完成事项
- 会中提取：从录音/转写中抓出决策项和动作项
- 会后执行：自动生成纪要并同步待办
- 会议质量追踪：识别“高时长低产出”的会议模式
会议的价值，不应停在“开过”。
而应停在“形成行动”。
多维决策层
洞察：多维表格不是数据仓库，而应成为轻量决策系统。
大多数团队的问题不是没有表，而是：
- 数据更新滞后
- 表与表之间不说话
- 会议里说了，表里没改
- 表里写了，人却没看
这个技能把多维表格从被动记录器，变成主动协同层。
核心动作：
- 自然语言更新：对话即录入
- 逾期任务识别：区分“真逾期”与“未更新”
- 跨表对齐：把需求、进度、反馈、资源关联起来
- 周报/OKR 起草：让真实数据自动转成汇报素材
它不只是帮你“填表”，
而是帮你让表成为组织的第二大脑。
日程调度器
洞察：日历不是记录时间，而是保护注意力。
如果一个人的日历完全由别人决定，那他的深度工作时间只会越来越碎。
这个技能会用“优先级、冲突、精力结构”来看待日程，而不是只看空档。
核心动作：
- 自动识别会议冲突并给出改期建议
- 保护深度工作时段
- 判断哪些会议你必须参加，哪些可替代
- 根据会议价值和角色权重优化排期
它不只是帮你安排时间，
而是在替你守住高价值时间。
文档中枢
洞察：知识的价值不在存储，而在被正确召回。
飞书文档最常见的问题不是“没有写”，而是“写了以后找不到、看不完、用不上”。
这个技能会把文档从静态容器变成动态知识流：
- 长文档智能摘要
- 跨文档搜索与归并
- 追踪关键文档变更
- 把结论提取成决策线索，而非阅读负担
核心动作：
- 文档摘要：快速压缩长文内容
- 跨文档检索：找结论而不是找文件名
- 更新追踪：谁改了什么，哪些变更值得你看
- 文档到行动：从内容提取结论、风险与待办
沟通协议：层级与分寸
洞察：在中国企业里，效率决定结果，分寸决定你还能不能继续高效。
这个技能不是简单“帮你发话”。
它要先判断：
- 对方是谁
- 你们是什么关系
- 这件事该公开说还是私下说
- 该直说，还是先铺垫
- 该给结论，还是先给背景
它会根据场景自动调整表达方式。
向上汇报
- 结论先行
- 数据支撑
- 给出备选方案
- 避免情绪化与冗长解释
跨部门协同
- 事实陈述
- 利益对齐
- 降低侵略性
- 保留合作余地
团队跟进
- 明确动作
- 保留体面
- 既指出问题，也提供支持路径
催办与提醒
- 默认私信优先
- 公开群里避免让对方下不来台
- 根据层级调节语气密度与催促力度
这不是礼貌问题。
这是协作成本问题。
交互范式
这个技能不是“查一下”，而是“代理执行逻辑链”。
场景 A：项目进度追踪
输入：
“帮我追一下 A 项目的进度。”
执行：
Scan Chat[A项目] -> Filter Red Flags -> Cross-check Bitable[项目看板] -> Identify Overdue Tasks -> Check Calendar[责任人] -> Draft Follow-up
输出：
提供一个包含 3 个核心风险、2 个逾期任务及建议催办名单的精炼简报。
场景 B：审批催办
输入：
“查一下谁的审批卡住了，帮我催一下，语气委婉点。”
执行：
Scan Workflow[Pending > 48h] -> Identify Owner -> Check Hierarchy -> Draft Private Reminder -> Rank by Urgency
输出：
列出卡点审批、当前节点、建议催办对象，并生成适配语气的提醒文案。
场景 C：周报生成
输入：
“帮我起草这周周报，重点写 A、B 两个项目。”
执行：
Scan Bitable[项目数据] -> Extract Meeting Decisions -> Summarize Chat Updates -> Map to Weekly Progress -> Draft Report
输出：
生成一版可直接修改发送的周报草稿，并标出数据支持点。
场景 D：文档检索
输入：
“我们上次讨论用户留存的结论在哪个文档里？”
执行：
Search Docs[关键词=用户留存] -> Rank by Relevance -> Extract Conclusions -> Return Source Links
输出：
返回最相关文档、关键结论摘要，以及原文位置。
适用边界
适合用于：
- 群聊情报整理
- 审批预审与催办
- 会议纪要与待办抽取
- 多维表格与文档联动
- 周报、OKR、项目追踪
- 高压协作环境中的信息压缩与动作排序
不适合用于：
- 替代正式法律、财务、合规判断
- 越权访问无权限数据
- 代替真实管理者做最终组织裁决
- 在缺乏
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04-20 08:03 · 套利,黄金,期货,套利
黄金现货与期货价差扩大至近期高位，提供期现套利机会
3月25日，A股公用事业板块震荡回暖，电力板块稳步走强，电力ETF博时（561700）震荡上行，其中成分股长江电力、中国核电、三峡能源、国电电力、永泰能源、华能国际等纷纷跟涨。
消息面上，国家能源局、国家统计局最新发布2026年1-2月电力供需核心数据，行业基本面稳步向好。需求端，1-2月全社会用电量累计16546亿千瓦时，同比增长6.1%；供给端，规上工业电力生产增速加快，1-2月规上工业发电量15718亿千瓦时，同比增长4.1%，增速较2025年12月加快4.0个百分点，日均发电266.4亿千瓦时。
国盛证券指出，算电协同下国家级政策严格限制算力园区绿电占比，电力建设战略从“东数西算”起步到“算电协同”升格。自2022年“东数西算”工程启动以来，政策端持续引导数据中心向绿色低碳转型，明确要求2025年底国家枢纽节点新建数据中心绿电占比超过80%。绿色能源成为破解数据中心高能耗难题的关键路径，数据中心用电需求也对电力系统升级提出挑战。
电力ETF博时（561700）及其联接基金（A类：017481，C类：017482）精准覆盖电力公用事业核心龙头，一键分享行业成长红利，在电力需求增长与算电协同升级双重驱动下，具备中长期配置价值。
该ETF紧密跟踪中证全指电力公用事业指数，该指数选取沪深市场中从事电力生产、电力供应等电力公用事业相关业务的上市公司证券作为指数样本，以自由流通市值加权，全面反映A股电力公用事业板块整体表现。
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04-20 08:03 · 套利,加密货币,交易所,套利
主要加密货币交易所在BTC/USDT交易对上出现显著价差
Binance
About Binance
What Is Binance?
Binance is the world’s largest cryptocurrency exchange by user count and trading volume, with over $217 billion in daily volume across spot and futures markets as of June 2025. Serving 280+ million of users globally, Binance offers access to 500+ cryptocurrencies and more than 1,500 trading pairs. Beyond trading, Binance has evolved into a comprehensive ecosystem that includes Binance Exchange, Binance Wallet, Binance Earn, Binance Pay, Binance Square, Binance Academy, Binance Research, and more. Whether users are looking to trade, invest, learn, or build, Binance provides a secure and feature-rich platform to do so.
Who Are the Binance Founders?
Binance was co-founded by Changpeng Zhao (CZ) and Yi He in 2017. CZ, a Chinese-Canadian software developer and former CEO of Binance, brought experience from previous roles at Bloomberg and Blockchain.com. Yi He, also a seasoned entrepreneur, was a co-founder of OKEX and an early executive in the digital-asset space. Since November 2023, the CEO of Binance has been Richard Teng. With over 25 years of experience in the financial services industry, Richard has held senior leadership roles at major global firms, bringing deep expertise in payments, banking, and technology to drive Binance’s continued growth and innovation.
When Did Binance Launch?
Binance officially launched in June 2017 and rapidly grew into the largest cryptocurrency exchange in the world within just six months.
Binance Restricted Countries
Under Binance’s Terms of Use, access to Binance services is restricted in certain jurisdictions, including the United States, Canada (Ontario), Iran, Cuba, Crimea Region, and North Korea. Access may also be limited in jurisdictions where Binance is not authorized to operate, or where regulatory restrictions apply. Always consult Binance's official terms and compliance updates to confirm eligibility in your country.
How Much Are Binance Fees?
Binance offers a competitive, tiered fee structure for both spot and futures trading. For regular users, spot trading fees start at 0.1% for both maker and taker. Users can receive additional discounts by paying fees in BNB or increasing their trading volume to qualify for VIP levels. Binance also offers zero-fee trading on select BTC and FDUSD spot pairs.
For the most accurate and up-to-date fee schedule, see the official Binance Fee Structure page.
Is It Possible To Use Leverage or Margin Trading on Binance?
Yes, Binance offers both margin trading and derivatives with flexible leverage. On Binance Margin, users can trade a wide range of cryptocurrencies with up to 10x leverage, depending on the asset and market conditions. On Binance Futures, users can trade USDT- or coin-margined perpetual contracts with leverage up to 125x on certain pairs. The platform also features cross and isolated margin modes, auto-deleveraging, and a robust risk engine to support advanced strategies.
You can review the latest margin and leverage rules on the official Futures Trading Rules page.
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