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Metrics Help

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nemanli
metrics-help

Router for ambiguous metrics prompts — "metrics", "help with metrics", "where do I start?", `/metrics-help`. Asks one scoping question and points to the right skill. Fallback only — the seven content/foundation skills auto-trigger themselves; skip this one when intent fits metrics-ux, metrics-spec, metrics-instrumentation, metrics-diagnose, metrics-review, metrics-present, or metrics-basics.

Overview

Publishernemanli
Repositoryux-metrics-skills
Skill namemetrics-help
Stars
3
Forks
0
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Metrics Help 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/nemanli/ux-metrics-skills/tree/main/skills/metrics-help
  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/nemanli/ux-metrics-skills.git /tmp/ux-metrics-skills
mkdir -p .claude/skills
cp -r /tmp/ux-metrics-skills/skills/metrics-help .claude/skills/metrics-help
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Metrics Help 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 Metrics Help 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 Metrics Help 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.

Overview

Routes ambiguous metrics prompts to the right skill. Does not answer metrics questions itself. Asks one short scoping question, maps the answer to a target skill, and tells the user which skill to invoke next.

The seven content/foundation skills auto-trigger from their own description fields. This router exists for the residual cases — one-word prompts, mixed intent, or users who explicitly ask "which skill?".

When to Use

Trigger this skill when:

  • User runs /metrics-help.
  • User asks directly: "which metrics skill should I use?", "where do I start?", "hansı skill?".
  • Prompt is too short or vague for the seven to match — e.g. "metrics", "help me with metrics", "I need to measure something".
  • Prompt mixes two or more jobs and it is unclear which comes first — e.g. "I need to write a measurement plan and instrument the events".

Do not trigger when the prompt clearly matches one of the seven skills. Their descriptions handle their own selection.

Routing

Step 1 — Scoping question

Ask only when the prompt is genuinely ambiguous. Skip the question when any of these signals is already present:

  • A design situation is named with no commitment to a decision rule (redesign, launch, onboarding, feature) → go to metrics-ux.
  • The user wants a written measurement plan before shipping ("measurement plan", "PRD for metrics", "pre-register", "success criteria", "decision rule") → go to metrics-spec.
  • The user mentions tracking plan, event taxonomy, naming convention, event spec, "what to log", PII → go to metrics-instrumentation.
  • A number, percentage, before/after, or "dropped/spiked" is mentioned with data the user owns → go to metrics-diagnose.
  • The user wants to stress-test a claim ("is this real?", "vet this case study", "review my draft before publish") → go to metrics-review.
  • An audience or format is named (CPO, board, slide, review, report) → go to metrics-present.
  • A framework or term is named with no task ("what is X?", "define X") → go to metrics-basics.

Otherwise, ask:

What do you need help with?

  1. Picking which metrics to measure (a redesign, a launch, a feature)
  2. Writing a measurement plan before the change ships (hypothesis, decision rule)
  3. Designing the events / tracking plan that captures a metric
  4. Understanding what a metric movement means (you have data, a number changed)
  5. Stress-testing a metric claim (incoming claim or self-review before publishing)
  6. Communicating results to stakeholders (review, slide, report)
  7. Learning a framework or term (HEART, decision rule, tracking plan, PII)

Follow the "How to ask" section in context-questions.md — closed 7-way choice, inline numbered list.

If 7 options is too many for the prompt, collapse to the lifecycle phase first:

Where are you in the project?

  1. Before launch — picking metrics, writing the plan, designing instrumentation
  2. After data exists — diagnosing a movement or reviewing a claim
  3. Communicating — building the stakeholder story
  4. Learning — defining a term

Then drill into the specific skill within that phase.

Step 2 — Map answer to skill

Answer (full 7-way)Target skill
1 — picking metricsmetrics-ux
2 — writing a measurement planmetrics-spec
3 — designing the tracking planmetrics-instrumentation
4 — interpreting data you ownmetrics-diagnose
5 — stress-testing a claimmetrics-review
6 — communicating resultsmetrics-present
7 — learning a termmetrics-basics

Out-of-scope cases:

  • Revenue, MRR, churn, LTV, conversion (free→paid) → say: "Business/product metrics are out of scope. For UX-side equivalents (adoption, retention, task success) use metrics-ux."
  • A/B test design (variant split, randomization unit, stopping rule, sequential testing) → say: "Designing experiments is PM / growth / eng work, not in this skill set. For the designer-facing pieces of an A/B test — the decision rule before the test, the analysis after, the stakeholder story — use metrics-spec, metrics-diagnose, metrics-review, metrics-present respectively."
  • Stakeholder conflict ("CPO disagrees with my metric") → say: "This is a communication and framing question. Use metrics-present for the framing; for vetting the stakeholder's counter-claim use metrics-review."
  • Tool-specific configuration (GA4, Mixpanel, Amplitude, PostHog setup) → say: "This skill set covers method, not tool configuration. Use metrics-instrumentation for the tool-agnostic plan; the tool's own docs handle config."
  • Statistical method tutorials ("teach me hypothesis testing") → say: "This skill set applies statistics to UX measurement, not statistical theory. Use metrics-basics for terms and metrics-diagnose/metrics-spec for application."
  • Data exists but no baseline / sample too small → still route to metrics-diagnose. That skill's Red Flags handle the "stop and ask" path; the router does not pre-filter on data quality.

Step 3 — Multi-skill sequences

If the user's job needs more than one skill, recommend the order. User confirms before proceeding.

SituationSequence
New design + want to present impact latermetrics-ux → (collect data) → metrics-present
New design + want decision rule before shippingmetrics-ux → metrics-spec → (collect data) → metrics-diagnose → metrics-present
Plan + instrument (full pre-launch)metrics-spec → metrics-instrumentation → (collect data) → metrics-diagnose
Metric dropped + need to brief stakeholdersmetrics-diagnose → metrics-present
Stress-test the result before publishingmetrics-diagnose → metrics-review (self-review mode) → metrics-present
Incoming vendor / "industry benchmark" claimmetrics-review only
Defining a metric + then planning measurementmetrics-basics → metrics-ux
Plan + measure + diagnose (full lifecycle)metrics-spec → metrics-instrumentation → (collect data) → metrics-diagnose → metrics-review → metrics-present
Stakeholder pushback on metric choicemetrics-basics (frame the term) → metrics-review (vet their alternative) → metrics-present (defend)
Messy live taxonomy needs cleanupmetrics-instrumentation (audit mode)

State it as: "Start with metrics-spec to write the plan. Once the change is shipped and data accrues, use metrics-diagnose to interpret. Sound right?"

General rule for prompts not in the table. When the user names ≥2 jobs in one prompt, list the sequence as numbered steps, name the bridge artifact between each step (signed spec, tracking plan version, validated numbers, draft slide), and stop. Do not chain-trigger — skills cannot invoke each other, so the user has to re-enter the next prompt when they have the bridge artifact in hand.

Step 4 — Hand off

Tell the user which skill to invoke and stop. Do not answer the underlying metrics question — that is the target skill's job. Example:

Sounds like a pre-launch measurement plan. Use metrics-spec next. It will ask about scope, stage, audience, and data, then walk you through hypothesis, primary metric, counter-metric, decision rule, and sample size.

Limits

  • Never recommend a metric, never interpret data, never draft a tracking plan, never review a claim, never write a stakeholder message. Routing only.
  • Maximum one scoping question per session. If the answer is still unclear, pick the closest skill and let the user redirect.
  • If the prompt clearly matches one of the seven skills, do not route — let that skill auto-trigger. Routing a clear prompt adds friction.

Frequently asked questions

What does the Metrics Help AI skill do?

Router for ambiguous metrics prompts — "metrics", "help with metrics", "where do I start?", `/metrics-help`. Asks one scoping question and points to the right skill. Fallback only — the seven content/foundation skills auto-trigger themselves; skip this one when intent fits metrics-ux, metrics-spec, metrics-instrumentation, metrics-diagnose, metrics-review, metrics-present, or metrics-basics.

Why use Metrics Help on TypingMind?

Because you install it once and use it with any model. Metrics Help 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 Metrics Help in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nemanli/ux-metrics-skills/tree/main/skills/metrics-help. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Metrics Help?

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 Metrics Help?

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

Is the Metrics Help AI skill free?

Yes. It is published on GitHub by nemanli 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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