channel-digest

Aktivitäten über mehrere Teams-Kanäle hinweg in einer einzigen konsolidierten Zusammenfassung darstellen – wichtige Diskussionen, Entscheidungen, Erwähnungen und Aktionspunkte.

npx skills add https://github.com/microsoft/work-iq --skill channel-digest

Channel Digest

Produce a consolidated summary of activity across multiple Microsoft Teams channels. Scans recent messages from each channel, identifies key discussions, decisions made, action items, and @‑mentions — then presents everything in a single, scannable digest. Ideal for managers, leads, or anyone tracking multiple project channels.

When to Use

  • "What happened in my channels today?"
  • "Summarize the last 3 days across all my project channels"
  • "Give me a digest of Engineering and Product channels"
  • "Any decisions made in the team channels this week?"
  • "Channel digest for the sprint channels"
  • "What did I miss in the channels while I was out?"

Instructions

Step 1: Identify the User

workiq-ask (
  question: "What is my profile information including display name, email, and time zone?"
)

Extract displayName, email, and timeZone.

Step 2: Discover Teams and Channels

List all teams and their channels:

workiq-ask (
  question: "List all Microsoft Teams teams I belong to and their channels."
)

If the user specified particular channels (e.g., "Engineering #general and #design-reviews"), filter to only those. Otherwise, include all non‑archived channels across relevant teams.

Build a channel inventory:

  • Team name → Channel name
  • Skip channels named "General" in low‑priority teams unless the user explicitly includes them.

Step 3: Pull Recent Messages from Each Channel

For each target channel, fetch recent messages within the lookback window:

workiq-ask (
  question: "Show me the last 30 messages in the '<channel name>' channel of the '<team name>' team including replies. For each message include the sender name, date and time, message content, reply count, any @mentions, reactions count, and any attachments or shared links."
)

For each message, capture:

  • Sender display name
  • Timestamp
  • Content (text body for analysis)
  • Reply count — indicates discussion depth
  • Mentions — anyone @‑mentioned, especially the current user
  • Reactions — high reaction count signals importance
  • Attachments — files or links shared

Step 4: Analyze and Categorize Messages

For each channel, classify the messages into categories:

🔑 Key Decisions — messages containing:

  • Decision language: "decided", "approved", "agreed", "going with", "finalized", "confirmed"
  • Follow‑up with context about what was decided

💬 Active Discussions — threads with:

  • 3+ replies indicating substantive conversation
  • Multiple participants contributing
  • Ongoing debate or open questions

📌 Action Items — messages containing:

  • Task language: "action item", "TODO", "follow up", "need to", "can you", "please"
  • Assignments: "@person please…", "owned by…"
  • Deadlines: "by Friday", "due date", "EOD"

📢 Announcements — messages that are:

  • From team owners or managers
  • Posted with high importance
  • Information‑sharing without expecting discussion

📎 Shared Resources — messages with:

  • File attachments (documents, spreadsheets)
  • Links to external resources, PRs, or documents

👤 Your Mentions — messages where the current user was @‑mentioned or referenced by name.

Step 5: Search for User Mentions Across Channels

Cast a wider net for the user's mentions:

workiq-ask (
  question: "Find all Teams channel messages that mention me or are directed at me in the last <lookback period>. Include the team name, channel name, sender, date, and message content for each."
)

Cross‑reference with messages already captured to avoid duplicates. Add any new mentions to the Your Mentions category.

Step 6: Compile the Consolidated Digest

Organize findings by channel, then surface cross‑channel themes at the top.

Output Format

📰 CHANNEL DIGEST
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📅 Period: {start date} → {end date}
🔍 Channels scanned: {N}  ·  💬 Messages analyzed: {N}

🎯 CROSS‑CHANNEL HIGHLIGHTS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🔑 {N} decisions made  ·  📌 {N} action items  ·  👤 {N} times you were mentioned

Top Decisions:
  1. {Team} › #{Channel}: "{decision summary}" — {Person}, {date}
  2. {Team} › #{Channel}: "{decision summary}" — {Person}, {date}

Action Items Needing You:
  • {Person} in #{Channel}: "{action item}" — due {date}
  • {Person} in #{Channel}: "{action item}"

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📢 {Team Name} › #{Channel Name}
   💬 {N} messages  ·  🧵 {N} threads  ·  👥 {N} participants
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

   🔑 DECISIONS
   • "{Decision summary}" — {Person}, {date}

   💬 KEY DISCUSSIONS
   • "{Topic summary}" — {N} replies, {participants}
     └─ Status: {resolved / ongoing / needs input}

   📌 ACTION ITEMS
   • {Person}: "{task description}" — {due date or "no deadline"}

   📢 ANNOUNCEMENTS
   • {Person}: "{announcement summary}" — {date}

   📎 SHARED FILES
   • {filename} — shared by {Person}, {date}

   👤 YOUR MENTIONS
   • {Person}: "@you {message preview}" — {date}

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📢 {Team Name} › #{Channel Name}
   💬 {N} messages  ·  ⏸️ Low activity
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
   • {brief summary of the few messages}

🔇 QUIET CHANNELS (no messages in period)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
   • {Team} › #{Channel}
   • {Team} › #{Channel}

Parameters

ParameterRequiredDefaultDescription
LookbackNo24 hoursTime window to scan (e.g., "3 days", "this week")
TeamsNoAll user's teamsSpecific teams to include
ChannelsNoAll channelsSpecific channels to include
FocusNoAll categoriesFilter to "decisions", "action-items", or "mentions"
Include QuietNotrueWhether to list channels with no activity
Max MessagesNo30 per channelMaximum messages to pull per channel

Required MCP Tools

MCP ServerToolPurpose
workiq (Local WorkIQ CLI)askUser identity, team/channel discovery, message retrieval, and mention search

Tips

  • Say "digest for Engineering channels only" to narrow the scope and speed up results.
  • Use "channel digest for the last week" before a Monday planning meeting to see what happened while you were out.
  • Use action-item-extractor to pull action items from meeting content after reviewing channel activity.
  • For recurring digests, establish a cadence: "give me my channel digest every morning."
  • Ask "expand on the discussion in #{Channel}" to drill into a specific conversation with full context.

Examples

Example 1 — Morning standup prep (default 24-hour window)

"Give me a channel digest for today."

Scans all of the user's Teams channels for the past 24 hours, surfaces any decisions made overnight, flags action items assigned to the user, and lists @-mentions — all in a single digest organized by team and channel.


Example 2 — Catch up after time off (multi-day, scoped channels)

"Channel digest for the Engineering and Product channels over the last 5 days."

Limits the scan to channels in the Engineering and Product teams, pulls up to 30 messages per channel across the 5-day window, and highlights key decisions (e.g., architecture choices, scope changes), active threads, and any messages that mentioned the user while they were away.


Example 3 — Decision-focused digest before a planning meeting

"Show me only decisions and action items from the sprint channels this week."

Uses the Focus parameter to filter output to Key Decisions and Action Items only, skipping announcements and low-activity channel summaries. Ideal for quickly building a pre-meeting agenda from what was agreed or assigned across sprint-related channels.

Error Handling

No teams or channels found

  • ask returns an empty result if the user is not a member of any team, or if the account lacks access to the Teams API.
  • Resolution: Confirm the user's account has Microsoft Teams access and is a member of at least one team. Ask the user to specify a team name manually if discovery fails.

Channel message fetch returns empty or partial results

  • ask may return fewer messages than expected if the channel has low activity or if the lookback window predates available message history (Teams message retention policies vary).
  • Resolution: Proceed with available messages and note in the digest that history may be limited. Surface "No messages in period" for those channels in the Quiet Channels section rather than failing silently.

Permission denied on a channel

  • Private channels require explicit membership; ask will indicate an access error if the user is not a member of a private channel even within a team they belong to.
  • Resolution: Skip the inaccessible channel, log it as "access restricted" in the digest output, and continue processing remaining channels.

Search for mentions returns no results

  • The mentions query via ask may return empty results for broad queries or large tenants.
  • Resolution: Fall back to the mentions already captured during the per-channel message retrieval. Note that the mention search could not be completed so the Your Mentions section may be incomplete.

Slow responses with many channels

  • Querying messages from a large number of channels requires multiple ask calls and may be slow.
  • Resolution: Process channels in batches. Inform the user of the delay and suggest narrowing scope with the Teams or Channels parameters.

Mehr Skills von microsoft

oss-growth
microsoft
OSS-Wachstums-Hacker-Persona
agent-framework-azure-ai-py
microsoft
Erstellen Sie Azure AI Foundry-Agents mit dem Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Verwenden Sie dies beim Erstellen persistenter Agents mit AzureAIAgentsProvider, bei der Nutzung gehosteter Tools (Code-Interpreter, Dateisuche, Websuche), bei der Integration von MCP-Servern, bei der Verwaltung von Konversationsthreads oder bei der Implementierung von Streaming-Antworten. Umfasst Funktionstools, strukturierte Ausgaben und Multi-Tool-Agents.
development
airunway-aks-setup
microsoft
Richte AI Runway auf AKS ein – vom leeren Cluster bis zum laufenden Modell. Umfasst Cluster-Überprüfung, Controller-Installation, GPU-Bewertung, Provider-Einrichtung und erste Bereitstellung. WANN: „AI Runway einrichten“, „AKS-Cluster onboarden“, „AI Runway installieren“, „airunway setup“, „Modell auf AKS bereitstellen“, „GPU-Inferenz auf AKS“, „KAITO-Setup auf AKS“, „LLM auf AKS ausführen“, „vLLM auf AKS“, „Modell-Serving auf AKS einrichten“, „AI Runway-Controller“.
devops
appinsights-instrumentation
microsoft
Leitfaden zur Instrumentierung von Webanwendungen mit Azure Application Insights. Bietet Telemetriemuster, SDK-Einrichtung und Konfigurationsreferenzen. WANN: wie man eine App instrumentiert, App Insights SDK, Telemetriemuster, was ist App Insights, Application Insights-Anleitung, Instrumentierungsbeispiele, APM-Best Practices.
devops
applicationinsights-web-ts
microsoft
Instrumentieren Sie Browser-/Web-Apps mit dem Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Verwenden Sie es für Real User Monitoring (RUM) – Seitenaufrufe, Klicks, AJAX/Fetch-Abhängigkeiten, Ausnahmen, benutzerdefinierte Ereignisse und browser-seitige GenAI-Agent-Traces, die mit Backend-OpenTelemetry-Traces korreliert werden. Umfasst SDK-Loader-Skript und npm-Setup, Framework-Erweiterungen (React, React Native, Angular), Click Analytics, Telemetrie-Initialisierer und OTel-GenAI-Semantik-Konventionen für Agent-/Tool-/Modell-Spans, die vom Browser ausgegeben werden.
devops
azure-ai-anomalydetector-java
microsoft
Erstellen Sie Anomalieerkennungsanwendungen mit dem Azure AI Anomaly Detector SDK für Java. Verwenden Sie dies bei der Implementierung von univariater/multivariater Anomalieerkennung, Zeitreihenanalyse oder KI-gestützter Überwachung.
development
azure-ai-language-conversations-py
microsoft
Implementieren Sie Conversational Language Understanding (CLU) mit dem azure-ai-language-conversations Python SDK. Verwenden Sie dies, wenn Sie mit ConversationAnalysisClient arbeiten, um Gesprächsabsichten und Entitäten zu analysieren, NLP-Funktionen zu erstellen oder Sprachverständnis in Anwendungen zu integrieren.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 für Python. Verwenden für ML-Workspaces, Jobs, Modelle, Datensätze, Compute und Pipelines. Auslöser: „azure-ai-ml“, „MLClient“, „Workspace“, „Modell-Registry“, „Trainings-Jobs“, „Datensätze“.
development