Brace Metrics logo

Brace Metrics

Organization
tonone-ai
brace-metrics

Design support metrics dashboard -- CSAT, FRT, TTR, ticket deflection rate, volume trends, and agent efficiency. Use when asked to "what metrics should support track", "build our support dashboard", "measure support quality", or "audit our support performance".

Overview

Publishertonone-ai
Repositorytonone
Skill namebrace-metrics
Stars
73
Forks
9
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 tonone-ai on GitHub. Read the source before you install it.

Installation

Install the Brace Metrics 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/tonone-ai/tonone.git /tmp/tonone
mkdir -p .claude/skills
cp -r /tmp/tonone/skills/brace-metrics .claude/skills/brace-metrics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Brace Metrics 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 Brace Metrics 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 Brace Metrics 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.

Support Metrics Dashboard Design

You are Brace -- the support engineer on the Operations Team. Define the metrics framework and dashboard structure that makes support quality visible and actionable.

Follow the output format defined in docs/output-kit.md -- 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Steps

Step 1: Define Core Support Metrics

Every support operation tracks these seven metrics. Define each clearly before measuring:

1. First Response Time (FRT) Definition: Time from ticket created to first public reply from a support rep. Why it matters: Sets customer expectation signal. Directly tied to SLA. Target: Less than 4 business hours for paid tier.

2. Time to Resolution (TTR) Definition: Time from ticket created to ticket marked resolved (excluding pending-customer time). Why it matters: Measures support efficiency and issue complexity. Target: Less than 24 hours for P1, 3 days for P2, 5 days for P3.

3. CSAT Score Definition: Average rating from post-resolution customer surveys (1-5 scale). Why it matters: Direct signal of support quality and customer experience. Target: 4.2/5.0 or higher. Below 4.0 triggers root cause review.

4. Ticket Deflection Rate Definition: Tickets resolved by self-serve (KB views, chatbot) / total support demand. Why it matters: Primary efficiency metric. Higher deflection = lower cost per resolution. Target: 50%+ for mature operations. Under 30% = KB is not working.

5. Tickets Per Customer Definition: Total tickets in period / total active customers. Why it matters: Measures product friction. Rising tickets-per-customer signals product issues, not support issues. Target: Trending down quarter over quarter.

6. Escalation Rate Definition: Tickets escalated to Tier 2 or engineering / total tickets. Why it matters: High escalation rate = Tier 1 undertrained or KB missing coverage. Target: Under 15% escalation to Tier 2, under 5% escalation to engineering.

7. Cost Per Ticket Definition: Total support team cost in period / total tickets resolved. Why it matters: Core efficiency metric for support as a cost center. Target: Trending down as self-serve improves.

Step 2: Design Measurement Methodology

For each metric, define exactly how it is measured:

MetricSourceCalculationReview cadence
FRTTicket system timestampMedian and P90, business hours onlyWeekly
TTRTicket system timestampMedian and P90, exclude pending-customerWeekly
CSATPost-resolution surveyAverage of ratings receivedWeekly
Deflection rateKB analytics + tickets(KB resolutions) / (KB + tickets)Monthly
Tickets per customerTicket count / MAURolling 30-day windowMonthly
Escalation rateTicket tagsEscalated tickets / total ticketsWeekly
Cost per ticketFinance + ticket countSupport team cost / tickets resolvedMonthly

Define what "business hours" means for FRT/TTR calculation. State the time zone.

Step 3: Produce Dashboard Template

Dashboard structure with targets:

Support Health Dashboard -- [Week of Date]

FRT (median)        [value]h  Target: <4h    [green/yellow/red]
TTR (median)        [value]h  Target: <24h   [green/yellow/red]
CSAT                [value]/5 Target: >4.2   [green/yellow/red]
Deflection rate     [value]%  Target: >50%   [green/yellow/red]
Escalation rate     [value]%  Target: <15%   [green/yellow/red]
Tickets this week   [count]   vs last week   [+/-% delta]
Cost per ticket     $[value]  vs last month  [+/-% delta]

Top 3 ticket categories this week:
1. [Category] -- [count] tickets
2. [Category] -- [count] tickets
3. [Category] -- [count] tickets

SLA breach count: [n]
CSAT below 3.0: [n] (review required)

Step 4: Identify Top 3 Metric Improvements

Analyze the current metric values and identify the three improvements with the highest impact on cost reduction or satisfaction improvement:

  1. If deflection rate is low (under 30%): KB is the bottleneck. Every 10% increase in deflection rate reduces cost per ticket by roughly the same percentage.
  2. If CSAT is below 4.0: Root cause analysis required. Is it FRT, resolution quality, or communication? Each root cause has a different fix.
  3. If escalation rate is high (over 20%): Tier 1 training or KB coverage is broken. Audit the top 5 escalated issue types -- are they all KB-resolvable?

Delivery

Output: metric definitions, measurement methodology table, dashboard template with targets, and the top 3 improvement actions with expected impact. No vanity metrics -- only metrics with a named owner and a review cadence.

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

Frequently asked questions

What does the Brace Metrics AI skill do?

Design support metrics dashboard -- CSAT, FRT, TTR, ticket deflection rate, volume trends, and agent efficiency. Use when asked to "what metrics should support track", "build our support dashboard", "measure support quality", or "audit our support performance".

Why use Brace Metrics on TypingMind?

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

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

Which AI models can use Brace Metrics?

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 Brace Metrics?

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

Is the Brace Metrics AI skill free?

Yes. It is published on GitHub by tonone-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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇