Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add charlieviettq/awesome-agent-skill --skill cs-analyticsgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/cs-analytics)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/cs-analytics"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/cs-analytics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/cs-analytics"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/cs-analytics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00093 | $0.01359 |
| Opus 5 | $0.00046 | $0.00679 |
| Sonnet 5 | $0.00019 | $0.00272 |
| Haiku 4.5 | $0.00009 | $0.00136 |
Grade A, and why
"cs-analytics" scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to cs-analytics — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Service Analytics
Framework
IRON LAW: Measure Satisfaction AND Efficiency — Never Just One
High CSAT with terrible resolution time = unsustainable (agents spend
too long per ticket). Fast resolution with low CSAT = cutting corners.
Both dimensions must be tracked and balanced.
Key Metrics
Satisfaction Metrics
| Metric | What It Measures | How to Collect | Benchmark |
|---|---|---|---|
| CSAT | Satisfaction with specific interaction | Post-interaction survey (1-5 scale) | > 4.0/5 |
| NPS | Likelihood to recommend | "How likely to recommend?" (0-10) | > 30 |
| CES | Effort required to resolve | "How easy was it to resolve?" (1-7) | > 5.0/7 |
Efficiency Metrics
| Metric | Formula | Benchmark |
|---|---|---|
| First Contact Resolution (FCR) | Resolved on first contact / Total contacts | > 70% |
| Average Handle Time (AHT) | Total handle time / Total contacts | 5-8 min (varies by industry) |
| Average Response Time | Time from ticket creation to first response | < SLA target |
| Backlog | Open tickets / Daily throughput | < 1 day |
| Escalation Rate | Escalated tickets / Total tickets | < 20% |
| Reopen Rate | Reopened tickets / Resolved tickets | < 5% |
Operational Metrics
| Metric | Formula | Use |
|---|---|---|
| Ticket Volume | Tickets per day/week/month | Staffing planning |
| Channel Mix | % by channel (email, chat, phone, LINE) | Resource allocation |
| Peak Hours | Volume by hour-of-day | Shift scheduling |
| Category Distribution | % by issue type | Process improvement priority |
Analysis Workflows
1. Top Contact Reason Analysis
- Categorize all tickets by reason (auto-tag or manual)
- Pareto chart: top 5 reasons usually account for 60-80% of volume
- For each top reason: can it be self-served? Automated? Eliminated at source?
2. Text Mining on Tickets
- Extract frequent keywords/phrases from ticket descriptions
- Cluster into topics (LDA, BERTopic, or simple TF-IDF)
- Identify emerging issues (new topics appearing in recent weeks)
- Sentiment analysis on customer messages
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 126 lines · 93 tokens per session scan A 27000812fbe1
"cs-analytics" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (26 stars, last pushed 1mo ago), licensed MIT. It adds 93 tokens to every session and 1,359 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to cs-analytics, differing in 8 lines, and is treated as a copy.
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