--- name: brace-metrics description: 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". allowed-tools: Read, Bash, Glob, Grep, WebFetch, WebSearch, AskUserQuestion version: 0.1.0 author: tonone-ai license: MIT compatibility: Designed for Claude Code tags: [operations, support, metrics] --- # 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: | Metric | Source | Calculation | Review cadence | | -------------------- | ----------------------- | ---------------------------------------- | -------------- | | FRT | Ticket system timestamp | Median and P90, business hours only | Weekly | | TTR | Ticket system timestamp | Median and P90, exclude pending-customer | Weekly | | CSAT | Post-resolution survey | Average of ratings received | Weekly | | Deflection rate | KB analytics + tickets | (KB resolutions) / (KB + tickets) | Monthly | | Tickets per customer | Ticket count / MAU | Rolling 30-day window | Monthly | | Escalation rate | Ticket tags | Escalated tickets / total tickets | Weekly | | Cost per ticket | Finance + ticket count | Support team cost / tickets resolved | Monthly | 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. --- Source attribution: tonone-ai Original project: https://github.com/tonone-ai/tonone/blob/1ceec80367a4e846e2f209cc87eea9d7544bd978/team/brace/skills/brace-metrics/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 tonone-ai Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: tonone-ai Original project: https://github.com/tonone-ai/tonone/blob/1ceec80367a4e846e2f209cc87eea9d7544bd978/team/brace/skills/brace-metrics/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Operations, Sales, Customer Support Roles: Business Process Analyst, Customer Success Manager, Data Analyst, Operations Manager, Support Agent Specialties: Specialty not specified Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 7a77bf588a964aceb26e2fd0c105253a347f3a620a9828e6bac5eca2b1ea8b8a