Claude Blog 采集 (2026-08-14)¶
共采集 2 篇文章
📋 文章索引¶
- Self-service data analytics in Slack: how Anthropic deploys Claude Tag for ad-hoc questions - Aug 13, 2026 (评分: 9.5)
- Claude Tag now reads even more of the room - Aug 13, 2026 (评分: 9.0)
Self-service data analytics in Slack: how Anthropic deploys Claude Tag for ad-hoc questions¶
来源: Claude Blog 发布日期: Aug 13, 2026 采集时间: 2026-08-14 价值评分: 9.5/10 正文字数: ~5267 字符
摘要¶
How Anthropic's data team uses Claude Tag for self-service data analytics with the same governed definitions analysts use.
正文内容¶
In our previous post , we described how we enabled Claude to answer data analytics questions with ~95% accuracy through three primary artifacts: A governed semantic layer; A set of skill files that encode our analytical conventions; and An evaluation suite to measure performance. That post focused on Claude Code (the primary development surface for our data scientists and data engineers), and best practices for improving agentic accuracy. This post discusses how the data team at Anthropic applies that foundation to where the rest of the company works using Claude Tag (public beta), which is the foundation for our data analytics agent in Slack. Anyone can ask it data-related questions and receive answers backed by the same governed definitions analysts use .
Refresh skills as often as you refresh your data models You can teach Claude how to do a task aligned with your style and requirements using a skill , which is a markdown file with natural language instructions and files Claude can reference when needed. The single most important architectural decision we made was to treat skill files as served content , refreshed continuously, rather than something shipped once and forgotten. Data models can change several times a day. For example, a column gets renamed, a metric definition is corrected, or a table is deprecated. Every one of those changes needs to land in a skill file in relatively short order. If Claude is reading last Tuesday's copy of the skill, it gives last Tuesday's wrong answer with full confidence. This tendency can be especially damaging since the data consumer is now completely separated from the context they need to judge the accuracy of the response. They aren’t looking at a dashboard with trend lines or associated metrics that can guide their “sniff test.” They may receive just a single data point or two in Slack, and if it's not data they look at regularly, they are likely to accept that confidently wrong answer. To control this ever-changing environment, Claude Tag's runtime mounts our data repo's skills/ directory and re-reads it on every conversation . The skill files are just markdown on disk; the agent reads them the same way it would read any project file. Give the agent skills beyond knowing what to query Our initial instinct for deploying our data analytics agent using Claude Tag was to create a “knowledge skill,” which teaches Claude which tables to use and how our semantic layer is organized, and call it a day. We quickly determined that approach would provide correct numbers , but stop short of useful insights . Most data consumers tend to ask open-ended and ambiguous questions like "what's driving this dip?" or "can you forecast where this lands at month-end?" or "show me this data as a funnel." Answering those requires the agent to know not just where the data is but how an analyst would work with it . So alongside this knowledge skill, we mounted Claude Tag with additional analytics or runbook skills, including: Forecasting : when and how to fit a simple trend, seasonality assumptions, and when to refuse because a series is too short or too noisy. Cohort and retention analysis : standard cohort definitions, the retention curve template reported to leadership, and any gotchas (left-censoring, survivorship) that trip up naive implementations. Funnel analysis : the canonical stage definitions for key product funnels, so "where are users dropping off in onboarding?" is consistent across responses. Charting : visualization conventions like which chart type to use for which question, color palettes, and when a table is clearer than a plot. Analytical writing : how to structure a finding (TL;DR first, number, mechanism, caveat), and the level of hedging that’s appropriate given the degree of confidence. Every data team likely already has these conventions; they just usually live in someone's head and are only occasionally documented. Writing them down as skills ensures Claude applies them as consistently as your data scientist would. Connect to business context, not just the warehouse Even this combination of knowledge skills and runbook skills is not always enough to answer a question. When someone asks "why did sign-ups drop on Tuesday?", the answer often isn’t in the data model, but rather is frequently spread across Slack threads, incident trackers, release notes, and docs. To account for these gaps, we wire Claude Tag into our internal knowledge index, which catalogs documents, discussions, and events across the company. When the agent sees a metric move, it can search that index for contemporaneous context : an incident opened that morning, a feature flag flipped, a competitor announcement someone shared in a channel. The answer now would look like "sign-ups dropped 12% Tuesday: there was a payment-service incident open 9-11am that morning, and the dip is concentrated in the affected region."
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采集自 Claude Blog，由 collect_claude_blog.py 自动采集
Claude Tag now reads even more of the room¶
来源: Claude Blog 发布日期: Aug 13, 2026 采集时间: 2026-08-14 价值评分: 9.0/10 正文字数: ~1614 字符
摘要¶
Claude has more context to decide when to proactively collaborate in Slack (and when not to)
正文内容¶
Claude has more context to decide when to proactively collaborate in Slack (and when not to)
Claude Tag lets you add Claude to a Slack channel, where it works alongside your team. Claude responds when you @-mention it, or proactively when it thinks it can be helpful. Before, Claude only saw one message at a time , so it made decisions to act proactively based on what was in front of it, but not the wider context of what was around it. Now, Claude uses context from across the channel , as well as its memory and the standing instructions you have given it, to determine when to contribute to the conversation. As a result, Claude is now roughly 30% better at determining when, and when not, to proactively respond. This update comes at no additional cost today. While holding more context does increase Claude Tag’s usage, the additional context Claude Tag holds does not count toward usage or spend limits on any plan. From passive responder to active participant Previously a lightweight classifier decided when Claude should act. It looked at each new message on its own and made one yes-or-no call. For example, here are two engineers chasing the same bug from opposite ends. Neither has a free hour to run it down, and neither message asks for anything.
Explore more product news and best practices for teams building with Claude.
Transform how your organization operates with Claude
Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox.
Please provide your email address if you'd like to receive our monthly developer newsletter. You can unsubscribe at any time.
采集自 Claude Blog，由 collect_claude_blog.py 自动采集