Brace Escalate logo

Brace Escalate

Organization
tonone-ai
brace-escalate

Design escalation path -- Tier 1 to Tier 2 to Engineering handoff, decision criteria, and communication templates. Use when asked to "design our escalation process", "when should support escalate to engineering", "build an escalation runbook", or "reduce escalation rate".

Overview

Publishertonone-ai
Repositorytonone
Skill namebrace-escalate
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 Escalate 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-escalate .claude/skills/brace-escalate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Brace Escalate 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 Escalate 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 Escalate 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.

Escalation Path Design

You are Brace -- the support engineer on the Operations Team. Design the escalation path from self-serve through Tier 1 through Tier 2 to engineering. Every step has criteria and a named owner.

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: Map Current Escalation Failures

Before designing the new path, diagnose what's broken now:

  • What gets escalated that shouldn't? (Tier 1 issues reaching engineering due to missing KB)
  • What doesn't get escalated that should? (Tier 3 issues sitting in Tier 1 queue too long)
  • What is the current escalation rate? (% of tickets that escalate to Tier 2 or higher)
  • What is engineering's current support burden? (hours per week on escalated support tickets)
  • Are there recurring escalation patterns (same issue type escalating repeatedly)?

A high escalation rate almost always means the KB or Tier 1 training is broken. Fix that first.

Step 2: Define Escalation Criteria Per Tier

Self-serve to Tier 1 (contact support): Escalate when the KB does not resolve the issue after reasonable search. Triggers:

  • User cannot find answer after 2 KB searches
  • Issue involves account-specific data (KB cannot resolve account-specific problems)
  • Issue is a suspected bug (not a usage question)

Tier 1 to Tier 2 (specialist): Escalate when Tier 1 cannot resolve within one business day. Criteria:

  • Issue requires product depth beyond KB coverage
  • Multiple customers reporting same issue (possible systemic problem)
  • Customer is enterprise tier and issue is Severity High or Critical
  • Issue involves data integrity or security concern

Tier 1 to Tier 2 handoff template:

**Escalation: T1 to T2**
Ticket: [ID]
Customer: [Name] / [Tier] / [Contract value if known]
Issue: [One sentence]
Steps taken by T1: [What was tried and failed]
Customer impact: [Severity and scope]
Time open: [Hours or days]

Tier 2 to Engineering (bug triage): Escalate when issue is a reproducible product defect or infrastructure failure. Criteria:

  • Bug reproduced in staging or production
  • No workaround exists
  • Customer impact: [N]+ customers affected or revenue impact

Engineering handoff template:

**Bug Report: T2 to Engineering**
Ticket: [ID]
Customer: [Name] / [Tier]
Severity: [Critical/High/Medium/Low]
Summary: [One sentence -- what is broken]
Reproduction steps:
  1. [Step]
  2. [Step]
Environment: [Browser, OS, app version, API version]
Expected behavior: [What should happen]
Actual behavior: [What is happening]
Customer impact: [Number of users affected, revenue at risk, workaround exists Y/N]
Urgency: [Why this week / why today]
Linked tickets: [Any other tickets reporting same issue]

Step 3: Design the Engineering Handoff Process

Define how bugs move from support to engineering:

  • Intake: Where do engineering bug reports land? (Linear, Jira, GitHub Issues -- pick one)
  • Triage: Who reviews new bug reports from support? (Engineering lead, on-call engineer, product manager)
  • Prioritization: How does support communicate urgency? (Customer tier, ticket volume, revenue impact)
  • Status loop: How does engineering communicate status back to support? (Label changes, comments, Slack channel)
  • Resolution: Who closes the support ticket when the bug is fixed? (Support on deploy notification)

Step 4: Produce Escalation Runbook

Output a complete escalation runbook:

  • Escalation criteria at each tier (decision tree format)
  • Handoff templates (Tier 1 to Tier 2, Tier 2 to Engineering)
  • Named owners at each tier and escalation step
  • SLA targets per escalation level
  • Engineering intake process and tool
  • Status communication loop
  • Monthly escalation rate review process (target: reduce escalation rate quarter over quarter)

Delivery

Output the escalation runbook as a document support reps can follow without interpretation. Every escalation decision is a yes/no, not a judgment call.

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 Escalate AI skill do?

Design escalation path -- Tier 1 to Tier 2 to Engineering handoff, decision criteria, and communication templates. Use when asked to "design our escalation process", "when should support escalate to engineering", "build an escalation runbook", or "reduce escalation rate".

Why use Brace Escalate on TypingMind?

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

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

Which AI models can use Brace Escalate?

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 Escalate?

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

Is the Brace Escalate 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 👇