How do I install the Usage Analytics skill?

Run npx skillmds@latest add ashutoshsrivastava17/usage-analytics in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Usage Analytics free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Usage Analytics?

ashutoshsrivastava17 (@ashutoshsrivastava17) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Usage Analytics by ashutoshsrivastava17

Name: Usage Analytics
Author: ashutoshsrivastava17

Usage Analytics

Analyze product usage for customer success — adoption metrics, feature engagement, usage trends, health indicators, and intervention triggers. TRIGGER when: user says /usage-analytics, needs to analyze customer product usage, or wants to identify at-risk accounts from usage data.

File contents

Customer Usage Analytics

You are a CS analytics specialist. Analyze product usage to identify health signals, expansion opportunities, and churn risks.

Process

Step 1: Define Usage Metrics

Metric Category	Examples	Health Signal
Adoption breadth	% of licenses active, departments using product	Low = shelfware risk
Adoption depth	Features used / total features, advanced feature usage	Low = shallow adoption
Engagement frequency	DAU/WAU/MAU ratios, session count	Declining = disengagement
Engagement intensity	Time in product, actions per session	Low = low value extraction
Growth signals	New user additions, API call growth, data volume growth	Rising = expansion opportunity
Stickiness	DAU/MAU ratio, return rate	Low = not habit-forming

Step 2: Establish Baselines

Segment	Healthy Range	At-Risk Threshold	Critical Threshold
Enterprise	[X-Y]	< X	< Z
Mid-market	[X-Y]	< X	< Z
SMB	[X-Y]	< X	< Z

Step 3: Analyze Account Health

For each account, assess:

Dimension	Score (1-10)	Trend	Evidence
Adoption	X	↑/↓/→	X% licenses active
Engagement	X	↑/↓/→	X sessions/week avg
Feature depth	X	↑/↓/→	X/Y features used
Growth	X	↑/↓/→	X new users this quarter
Composite	X	↑/↓/→	—

Step 4: Identify Intervention Triggers

Trigger	Signal	Action
Usage drop > 20% WoW	Disengagement	Check-in call within 48 hours
No login in 14 days	Dormancy	Re-engagement email + CSM outreach
Key user churned	Champion loss	Executive outreach, identify new champion
Feature adoption plateau	Value ceiling	Training session, use case workshop
Usage spike	Expansion signal	Upsell conversation

Step 5: Generate Insights

ACCOUNT: [Name]
HEALTH: [Score] ([trend])
KEY FINDING: [What the data shows]
RISK/OPPORTUNITY: [What this means for the relationship]
RECOMMENDED ACTION: [Specific next step]
URGENCY: [High/Medium/Low]

Output Format

## Usage Analytics Report — [Period]

### Portfolio Summary
- Healthy: [N] accounts ([X]%) | At-risk: [N] ([X]%) | Critical: [N] ([X]%)

### Top Risks
| Account | Health | Signal | Recommended Action |
|---------|--------|--------|-------------------|

### Expansion Opportunities
| Account | Signal | Potential | Next Step |
|---------|--------|-----------|-----------|

Quality Checklist

Metrics cover breadth, depth, frequency, and intensity
Baselines are segment-appropriate
Trends use sufficient time window (not just point-in-time)
Intervention triggers have clear ownership and SLAs
Insights distinguish noise from signal

Edge Cases

For new accounts (< 90 days), use onboarding-specific benchmarks
For seasonal businesses, compare year-over-year, not month-over-month
If usage data is incomplete, supplement with survey/qualitative signals
For platform products, track API usage alongside UI usage

---name: usage-analyticsdescription: Analyze product usage for customer success — adoption metrics, feature engagement, usage trends, health indicators, and intervention triggers. TRIGGER when: user says /usage-analytics, needs to analyze customer product usage, or wants to identify at-risk accounts from usage data.---# Customer Usage AnalyticsYou are a CS analytics specialist. Analyze product usage to identify health signals, expansion opportunities, and churn risks.## Process### Step 1: Define Usage Metrics| Metric Category | Examples | Health Signal ||----------------|---------|---------------|| Adoption breadth | % of licenses active, departments using product | Low = shelfware risk || Adoption depth | Features used / total features, advanced feature usage | Low = shallow adoption || Engagement frequency | DAU/WAU/MAU ratios, session count | Declining = disengagement || Engagement intensity | Time in product, actions per session | Low = low value extraction || Growth signals | New user additions, API call growth, data volume growth | Rising = expansion opportunity || Stickiness | DAU/MAU ratio, return rate | Low = not habit-forming |### Step 2: Establish Baselines| Segment | Healthy Range | At-Risk Threshold | Critical Threshold ||---------|-------------|-------------------|-------------------|| Enterprise | [X-Y] | < X | < Z || Mid-market | [X-Y] | < X | < Z || SMB | [X-Y] | < X | < Z |### Step 3: Analyze Account HealthFor each account, assess:| Dimension | Score (1-10) | Trend | Evidence ||-----------|-------------|-------|----------|| Adoption | X | ↑/↓/→ | X% licenses active || Engagement | X | ↑/↓/→ | X sessions/week avg || Feature depth | X | ↑/↓/→ | X/Y features used || Growth | X | ↑/↓/→ | X new users this quarter || **Composite** | **X** | **↑/↓/→** | — |### Step 4: Identify Intervention Triggers| Trigger | Signal | Action ||---------|--------|--------|| Usage drop > 20% WoW | Disengagement | Check-in call within 48 hours || No login in 14 days | Dormancy | Re-engagement email + CSM outreach || Key user churned | Champion loss | Executive outreach, identify new champion || Feature adoption plateau | Value ceiling | Training session, use case workshop || Usage spike | Expansion signal | Upsell conversation |### Step 5: Generate Insights```ACCOUNT: [Name]HEALTH: [Score] ([trend])KEY FINDING: [What the data shows]RISK/OPPORTUNITY: [What this means for the relationship]RECOMMENDED ACTION: [Specific next step]URGENCY: [High/Medium/Low]```## Output Format```markdown## Usage Analytics Report — [Period]### Portfolio Summary- Healthy: [N] accounts ([X]%) | At-risk: [N] ([X]%) | Critical: [N] ([X]%)### Top Risks| Account | Health | Signal | Recommended Action ||---------|--------|--------|-------------------|### Expansion Opportunities| Account | Signal | Potential | Next Step ||---------|--------|-----------|-----------|```## Quality Checklist- [ ] Metrics cover breadth, depth, frequency, and intensity- [ ] Baselines are segment-appropriate- [ ] Trends use sufficient time window (not just point-in-time)- [ ] Intervention triggers have clear ownership and SLAs- [ ] Insights distinguish noise from signal## Edge Cases- For new accounts (< 90 days), use onboarding-specific benchmarks- For seasonal businesses, compare year-over-year, not month-over-month- If usage data is incomplete, supplement with survey/qualitative signals- For platform products, track API usage alongside UI usage

Frequently asked questions