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Mk 057

Community
backtrue
mk-057

Organizing recommendation-intent feedback. Use when the task requires a summary of recommendation intent and improvement feedback. Apply this method to the specified inputs and verify the deliverable

Overview

Publisherbacktrue
Repositorythinkwithblack-skill
Skill namemk-057
Stars
249
Forks
24
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 backtrue on GitHub. Read the source before you install it.

Installation

Install the Mk 057 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/backtrue/thinkwithblack-skill/tree/main/en/skills/mk-057
  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/backtrue/thinkwithblack-skill.git /tmp/thinkwithblack-skill
mkdir -p .claude/skills
cp -r /tmp/thinkwithblack-skill/en/skills/mk-057 .claude/skills/mk-057
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mk 057 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 Mk 057 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 Mk 057 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.

MK-057 | Organizing recommendation-intent feedback

For a regular chat, upload packs/mk-057.md. It includes the shared rules, this method, and required references

Purpose

This skill delivers: A summary of recommendation intent and improvement feedback

Without raw survey responses, produce only a questionnaire or analysis plan; do not fabricate answers

Required inputs

Provide: Recommendation question, scale, actual responses and collection period; question design needs only the product/service and respondents. Invitation candidates additionally need deidentified member keys, contact eligibility and campaign conditions

Method

  1. Keep the member-care email's central question focused on NPS/recommendation intent; do not expand it into a long questionnaire
  2. Identify Promoters/high-recommendation-intent respondents as candidates for later member-event invitations
  3. Recommendation intent indicates an attitude only; do not treat it as an actual referral or sale

Calculations and interpretation

Standard NPS uses a 0–10 recommendation scale: 9–10 promoters, 7–8 passives, 0–6 detractors. NPS = (promoters − detractors) / valid responses × 100. Report missing/out-of-range exclusions; no calculation with zero valid responses. For other scales, report the original distribution. A high score is not an actual referral; verify contact eligibility for invitations

Source: Bain: Measuring Your Net Promoter Score

Required output fields

OrderFieldHow to complete
1Recommendation question and scaleUse the supplied source, calculation, or explicit unknown
2Valid response count and exclusionsUse the supplied source, calculation, or explicit unknown
3Promoter, passive and detractor countsUse the supplied source, calculation, or explicit unknown
4NPS calculation and resultUse the supplied source, calculation, or explicit unknown
5Deidentified high-intent candidate keysUse the supplied source, calculation, or explicit unknown
6Contact eligibility and invitation tasksUse the supplied source, calculation, or explicit unknown

Verification cases

MK-057-A1 | 10 valid responses: 6 promoters, 2 passives, 2 detractors

Expected: NPS = (6−2)/10×100 = 40; passives remain in the denominator

MK-057-A2 | Responses use 1–5 or are all missing

Expected: Do not convert to standard NPS; report original distribution or zero valid responses

Method sources

Source file: 營業額提升主要關鍵(二)社群/會員經營技巧

Location: NPS

Source basis: extracted from course materials

Source URL: https://docs.google.com/presentation/d/1ZYpstuVAXGk9KdZt1QZm2OQ7ozpYYtfrBf0Xsy7kRx8/edit

English translation of source excerpt:

The member-care email asks only the "NPS question" and also identifies a list of Promoters to serve as the basis for member-event invitations

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 Mk 057 AI skill do?

Organizing recommendation-intent feedback. Use when the task requires a summary of recommendation intent and improvement feedback. Apply this method to the specified inputs and verify the deliverable

Why use Mk 057 on TypingMind?

Because you install it once and use it with any model. Mk 057 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 Mk 057 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/backtrue/thinkwithblack-skill/tree/main/en/skills/mk-057. 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 Mk 057?

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 Mk 057?

As many as you like. As long as a model supports skills, you can use Mk 057 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mk 057 AI skill free?

Yes. It is published on GitHub by backtrue 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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