Pattern Radar logo

Pattern Radar

Organization
microsoft
pattern-radar

Scan the user's recent Microsoft 365 signals (emails, active chats/channels, calendar subjects) to surface recurring patterns worth productizing — either as a blog post (the user explains the same thing repeatedly) or as an automation (the user does the same multi-step task repeatedly). Use when the user asks "what should I write about?", "what could I automate?", "find patterns in my work", or invokes "/pattern-radar".

Overview

Publishermicrosoft
Repositorycat-agent-skills
Skill namepattern-radar
Stars
95
Forks
115
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Pattern Radar AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/microsoft/cat-agent-skills/tree/main/submissions/pattern-radar
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/microsoft/cat-agent-skills.git /tmp/cat-agent-skills
mkdir -p .claude/skills
cp -r /tmp/cat-agent-skills/submissions/pattern-radar .claude/skills/pattern-radar
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pattern Radar in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pattern Radar on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pattern Radar is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Pattern Radar

Find recurring patterns in the user's recent work signals that could be turned into leverage — a blog post (the user keeps explaining the same thing) or an automation (the user keeps doing the same manual, multi-step task). Give blog and automation candidates equal weight; bias toward automation candidates, since offloading repetitive work is usually the bigger win.

Default lookback window: the last 7 days from the current date/time. Treat the provided current datetime as authoritative — never reason from training-data dates.

Tool names. This skill refers to Microsoft 365 tools as m365_*. If your host exposes them under different names, map them to the equivalent capability.

Inputs — collect breadth, not depth

  1. Email — list Inbox and Sent from the last 7 days (subjects + first-line previews only; do not open bodies). Pay special attention to Sent items: they reveal the user's own repeated outbound actions (forwards, intros, status pings, FYIs, "looping in X" handoffs, RSVPs, reschedules).
  2. Active chats — list recent chats; for each chat the user has posted in over the last ~30 days, fetch the last 7 days of messages. Strip @-mention noise.
  3. Active channels — for channels the user has posted in over the last ~30 days, fetch the last 7 days of messages. Skip silent channels.
  4. Calendar — list events from the last 7 days (subjects only — no bodies, no attendee lists). Look for repeated reschedules, repeated tentative responses, recurring conflict patterns, and meeting-prep cadences.

Clustering — what counts as a pattern

A signal qualifies only if it appears ≥3 times across ≥2 distinct people / chats / customers / threads / events in the window. Be ruthless — one-offs are noise.

Blog cluster types

  • Repeated explanation — the user answers the same question or explains the same concept to 3+ different people. → blog candidate.
  • Recurring FAQ — the same question shows up in 3+ chats/channels from different askers. → blog / FAQ candidate.

Automation cluster types (cast a wide net)

  • Repeated multi-step manual task — the same sequence of clicks / drafts / file moves / API calls, repeatedly. → automation candidate.
  • Repeated handoff / triage — forwarding, "looping in", or intro'ing the same kinds of items to the same people 3+ times. → auto-route by rule or classifier.
  • Repeated status pings / FYIs — the same kind of update to the same audience on a cadence. → scheduled digest.
  • Repeated meeting reschedules for the same root cause. → auto-propose alternates.
  • Repeated RSVPs / tentative responses to recurring meetings with consistent reasoning. → auto-respond by rule.
  • Repeated meeting prep — gathering the same context before recurring meetings. → pre-meeting brief.
  • Repeated doc/wiki updates — manually appending the same kind of content on a cadence. → scheduled ingest.
  • Repeated approvals / sign-offs of the same class of request. → rule-based pre-screen.
  • Repeated file/data shuttling — move/rename/reformat between systems. → pipeline.
  • Repeated drafting — near-identical emails/messages (welcome notes, onboarding intros, availability, "I have a conflict" replies, follow-ups). → template + auto-fill.

Anti-patterns — do NOT surface

  • Normal recurring meeting cadence (standups, weekly syncs) UNLESS there's repetitive manual work around them that could be automated.
  • Anything needing the body of an excluded/private email to reason about.
  • Automations the user already runs.

Output

List 0–3 candidates max. If signal is thin (< 3 reinforcing data points across all clusters), output: ✅ No new patterns this run.

Prefer a mix — include at least one automation candidate when one qualifies, even if a blog candidate is stronger.

Per candidate (≤3 lines each):

💡 **<theme in 5–8 words>**
   _evidence:_ <1–2 anonymized signals, e.g. "forwarded 4 customer asks to the
   same engineer this week" or "explained X to 4 different people in chats">
   **<blog|automation>** · <1-line rationale. For automation, name the trigger
   and the action, e.g. "trigger: incoming request → action: auto-route + draft
   reply".>

Privacy & guardrails

  • Read-only. Never send any email, chat, channel reply, or Teams message from this skill. Producing the report is the only output.
  • Anonymize. Never include customer/team names, attendee names, meeting times/locations, file paths, or full subjects containing customer identifiers. Say "3 different customers", not the actual names.
  • Respect exclusions. Never open or paraphrase content the user has marked as private or excluded (e.g. performance-review or confidential emails).
  • When in doubt that a candidate is a real pattern vs. coincidence, drop it — silence is safer than noise.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pattern Radar AI skill do?

Scan the user's recent Microsoft 365 signals (emails, active chats/channels, calendar subjects) to surface recurring patterns worth productizing — either as a blog post (the user explains the same thing repeatedly) or as an automation (the user does the same multi-step task repeatedly). Use when the user asks "what should I write about?", "what could I automate?", "find patterns in my work", or invokes "/pattern-radar".

Why use Pattern Radar on TypingMind?

Because you install it once and use it with any model. Pattern Radar is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pattern Radar in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/cat-agent-skills/tree/main/submissions/pattern-radar. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pattern Radar?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pattern Radar?

As many as you like. As long as a model supports skills, you can use Pattern Radar with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pattern Radar AI skill free?

Yes. It is published on GitHub by microsoft under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇