Customer Status logo

Customer Status

Organization
techwolf-ai
customer-status

Synthesised view of account health and activity for managers overseeing customer-facing teams (Sales, CS, Professional Services, Presales). Scans project channels, email threads, and Notion pages to surface status, risks, and upcoming milestones, without requiring the manager to trawl through individual channels. Supports proactive account management.

Overview

Publishertechwolf-ai
Repositoryai-first-toolkit
Skill namecustomer-status
Stars
138
Forks
7
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by techwolf-ai on GitHub. Read the source before you install it.

Installation

Install the Customer Status 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/techwolf-ai/ai-first-toolkit/tree/main/plugins/people-management/skills/customer-status
  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/techwolf-ai/ai-first-toolkit.git /tmp/ai-first-toolkit
mkdir -p .claude/skills
cp -r /tmp/ai-first-toolkit/plugins/people-management/skills/customer-status .claude/skills/customer-status
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Customer Status 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 Customer Status 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 Customer Status 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.

Customer Status Overview

Principle: "Narrow scope, high impact." One synthesised view across all accounts, so you can spot what needs attention without channel-hopping.

Produces a dashboard-style overview of active customer accounts and internal projects for the manager's team.

When to Use

  • Weekly check-in on account health
  • Before leadership meetings where customer status is discussed
  • When the manager says "how are our accounts?", "customer status", "any customer risks?"
  • Can be filtered: "customer status for [account]", "customer status for [team member]'s accounts"

Instructions

If any MCP connector is unavailable, follow the connector unavailability protocol in references/operating-principles.md.

1. Load Context

Read from manager-context/:

  • sources.md: customer/project channels, account mappings
  • manager-profile.md: team members and their account assignments
  • team/: individual team member profiles and project assignments

If no manager-context exists:

⚠️ No manager context found. Run /setup first so I know which accounts and channels to monitor.
I can still search broadly, but results will be less targeted.

2. Identify Active Accounts

From manager-context, get the list of active accounts/projects and their:

  • Slack channels
  • Key contacts (team member and customer-side)
  • Notion project pages
  • Current delivery phase (if documented)

If filtering by account or team member, narrow the scope.

3. Scan Per Account

For each active account, gather:

Slack (last 7 days):

  • Recent messages in the project/customer channel
  • Volume of activity (high/normal/low compared to usual)
  • Any messages with escalation signals: "blocked", "risk", "delayed", "urgent", "escalate", "concerned"
  • Any positive signals: "shipped", "live", "approved", "happy", "great feedback"
  • Most recent message timestamp (to detect silent accounts)

Gmail (last 14 days):

  • Email threads related to this customer
  • Any emails with escalation or risk language
  • Communication frequency

Notion:

  • Project/customer status page (if documented in sources.md)
  • Last updated date
  • Any documented risks or decisions

Google Drive:

  • Recent shared documents (SOWs, proposals, reports)

4. Assess Health Signal

For each account, determine a health signal based on evidence:

  • 🟢 Healthy: Regular activity, positive signals, no escalations, milestones on track
  • 🟡 Attention: Some risk signals, decreased activity, upcoming deadline, stale documentation
  • 🔴 At Risk: Escalation language, blocked progress, customer complaints, silence for >5 days on active project

Important: These are signals, not diagnoses. Always show the evidence that led to the assessment.

5. Produce the Overview

Read references/output-template.md for the full output template structure.

6. Sub-Agent Review

Spawn a sub-agent to review the customer status overview with fresh eyes. The reviewer should:

  • Check that health signal assessments are evidence-based: every red/yellow rating should cite specific signals, not just absence of activity.
  • Verify that silence is not over-interpreted: a quiet channel on a stable account is not the same as a quiet channel on an active delivery.
  • Check for team member workload signals: if one person owns many flagged accounts, note it.
  • Flag any accounts where the evidence is thin enough that the health signal might be misleading.

Incorporate the reviewer's feedback before presenting the final overview.

7. Present and Offer Follow-Up

Here's your customer status overview. Want me to:
- Dig deeper into any specific account?
- Prep for a conversation with [team member] about [account]?
- Check email threads for a specific customer?

Important Notes

Read references/operating-principles.md for shared operating principles (data scope, DM flagging, signals vs diagnoses, connector unavailability).

Additional notes specific to this skill:

  • Don't alarm unnecessarily. Silence on a channel might mean things are running smoothly. Combine multiple signals before flagging red.
  • Recency matters. Flag any account where project docs haven't been updated in >2 weeks on active projects.
  • Respect customer confidentiality. Summarise, don't reproduce customer communications verbatim. When surfacing DM content, flag it as (from DM).
  • Team member context. If a team member owns multiple accounts, note that, they might be spread thin.

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 Customer Status AI skill do?

Synthesised view of account health and activity for managers overseeing customer-facing teams (Sales, CS, Professional Services, Presales). Scans project channels, email threads, and Notion pages to surface status, risks, and upcoming milestones, without requiring the manager to trawl through individual channels. Supports proactive account management.

Why use Customer Status on TypingMind?

Because you install it once and use it with any model. Customer Status 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 Customer Status in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/techwolf-ai/ai-first-toolkit/tree/main/plugins/people-management/skills/customer-status. 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 Customer Status?

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 Customer Status?

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

Is the Customer Status AI skill free?

Yes. It is published on GitHub by techwolf-ai 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.

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