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Metrics Review

Community
davekilleen
metrics-review

Use when a business or operational metric needs definition and source provenance checked, freshness and comparability validated, or an anomaly reviewed against a baseline and target.

Overview

Publisherdavekilleen
RepositoryDex
Skill namemetrics-review
Stars
481
Forks
130
Bundled files
Instructions only
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 davekilleen on GitHub. Read the source before you install it.

Installation

Install the Metrics Review 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.
  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/davekilleen/Dex.git /tmp/Dex
mkdir -p .claude/skills
cp -r /tmp/Dex/packages/dex-agent-plugin/skills/_available/operations/metrics-review .claude/skills/metrics-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Metrics Review 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 Review 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 Review 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.

Metrics review

When to use

Use this skill for a dated review of one or more business or operational metrics when the definition, unit, time window, baseline, target, freshness, comparability, or anomaly status needs to be made explicit.

Do not use it to change a metric definition, target, dashboard, alert, or data source, or to explain a result causally from correlation alone. Not for publishing a number whose source, denominator, window, or as-of point is unknown.

Inputs and source discipline

For each metric, identify the canonical metric definition, formula or counting rule, unit, denominator or population, time window and timezone, source, source date, and as-of date/time. Record freshness expectations, extraction time, revision policy, and known exclusions. If a definition or unit is missing, report unknown rather than substituting a familiar one.

Use a source ledger for the current value, comparison values, baseline, and target. Link the query, report, dashboard, or export and record its source date and as-of provenance. Do not mix values from different definitions, units, populations, windows, or timezone cutoffs without marking them non-comparable.

Method

  1. Inventory the metric definition and scope in read-only mode. Confirm that the requested period is closed or label it provisional; record missing data and source freshness.
  2. Validate freshness, completeness, and comparability before describing a trend. Check data cutoffs, late arrivals, revisions, missing denominators, definition or pipeline changes, unit conversions, population changes, and timezone boundaries.
  3. Establish a baseline and target only from cited values with matching definitions, units, populations, and time windows. State whether each is observed, supplied, or unknown; never manufacture a baseline, target, or threshold.
  4. Validate an anomaly before interpreting it. Re-read or rerun the source, compare with an independent source when available, inspect missingness and pipeline health, and record whether the deviation persists. A flagged anomaly is not a confirmed business event.
  5. Interpret the validated observation with confidence and explicit alternatives. Make no causal claim without evidence that supports the causal link; correlation is not causation. List unknowns and contradictions instead of choosing a convenient explanation.
  6. Offer recommendations for follow-up measurement or human review. A recommendation is not a human decision, and no target or action changes as a side effect of this review.

Truth and uncertainty rules

Label metric values, comparisons, and explanations as observed, inferred, unknown, stale, or contradictory. Mark a value stale when its freshness limit has passed or the source is known to lag; mark comparability unknown when definitions or windows cannot be reconciled. Keep an anomaly separate from its hypothesis about cause.

Never invent dates, metrics, owners, intent, money, percentages, causes, status, or evidence. Do not round away a material unit or denominator difference, and do not make causal claims without evidence merely because two series move together.

Output contract

Return a review with, for every metric:

  • metric definition, formula/counting rule, unit, population, time window/timezone, source, source date, and as-of date/time;
  • freshness, completeness, and comparability checks with their evidence;
  • current value, baseline, and target with matching provenance or explicit unknown;
  • anomaly description, validation steps, persistence result, and confidence;
  • observed facts, inferred explanations, unknowns, stale inputs, and contradictions;
  • any correlation or causal interpretation clearly labelled and evidence-backed; and
  • recommendations and follow-up checks clearly labelled as not human decisions.

Safety and write boundaries

The default is read-only. Do not edit source data, definitions, targets, dashboards, alerts, or reporting periods. For a requested write or notification, preview the exact change, destination, and audience; obtain explicit confirmation from the human authority; then perform only that confirmed action. Preserve the original value and source when correcting a report.

Verification and recovery

Read back the report and reconcile every displayed value, unit, denominator, time window, timezone, baseline, target, and anomaly result against the source ledger. Re- check freshness and comparability after any source refresh; verify that an explanation did not become a causal claim without evidence.

If a source read, refresh, write, or reconciliation check fails, stop and report the failed check, affected metric, and as-of point. Do not retry blindly or fill the gap with a prior number. Re-read the source, mark the result stale or unknown as applicable, and recover only through a human-confirmed correction with the failed check recorded.

Frequently asked questions

What does the Metrics Review AI skill do?

Use when a business or operational metric needs definition and source provenance checked, freshness and comparability validated, or an anomaly reviewed against a baseline and target.

Why use Metrics Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davekilleen/Dex/tree/main/packages/dex-agent-plugin/skills/_available/operations/metrics-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Metrics Review?

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 Review?

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

Is the Metrics Review AI skill free?

It is published on GitHub by davekilleen. Check the repository for licensing terms. 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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