dora-metrics

Original：🇺🇸 English

Translated

Generate DORA metrics and engineering performance reports using Harness SEI via MCP. Track deployment frequency, lead time, change failure rate, and MTTR. Use when user says "DORA metrics", "deployment frequency", "lead time", "engineering metrics", or asks about team performance.

5installs

Sourceharness/harness-skills

Added on2026-05-25

NPX Install

npx skill4agent add harness/harness-skills dora-metrics

SKILL.md Content

View Translation Comparison →

DORA Metrics

Generate DORA metrics reports using Harness Software Engineering Insights (SEI) via MCP.

Instructions

All DORA metrics are served by a single resource type:

sei_dora_metric

. Pass the

metric

parameter to select the variant:

```
deployment_frequency
```
```
deployment_frequency_drilldown
```
```
lead_time
```
```
change_failure_rate
```
```
change_failure_rate_drilldown
```
```
mttr
```

Required inputs on every DORA call:

team_ref_id

,

date_start

,

date_end

,

granularity

(DAILY | WEEKLY | MONTHLY).

Step 1: Get a DORA Metric

Deployment Frequency:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "deployment_frequency"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "WEEKLY"

Lead Time for Changes:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "lead_time"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "WEEKLY"

Change Failure Rate:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "change_failure_rate"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "WEEKLY"

Mean Time to Recovery:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "mttr"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "WEEKLY"

Step 2: Get Drilldown Data

Per-deployment detail for frequency:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "deployment_frequency_drilldown"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "DAILY"

Per-failure detail for CFR:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "change_failure_rate_drilldown"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "DAILY"

Step 3: Get Team Data

List teams:

Call MCP tool: harness_list
Parameters:
  resource_type: "sei_team"

Get team details (integrations, developers, integration filters):

Call MCP tool: harness_list
Parameters:
  resource_type: "sei_team_detail"
  team_ref_id: "<team_id>"
  aspect: "developers"   # or "integrations" | "integration_filters"

Step 4: AI Metrics (Optional)

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_ai_adoption"

sei_ai_impact

,

sei_ai_usage

,

sei_ai_raw_metric

.

DORA Benchmarks

Metric	Elite	High	Medium	Low
Deployment Frequency	Multiple/day	Weekly-Monthly	Monthly-6mo	6mo+
Lead Time	< 1 hour	1 day-1 week	1-6 months	6mo+
Change Failure Rate	< 5%	5-10%	10-15%	> 15%
MTTR	< 1 hour	< 1 day	1 day-1 week	1 week+

Report Format

## DORA Metrics Report

**Period:** <date range>
**Team:** <team or org>

### Performance Summary

| Metric | Value | Rating | Trend |
|--------|-------|--------|-------|
| Deployment Frequency | X/week | High | Improving |
| Lead Time | X hours | Elite | Stable |
| Change Failure Rate | X% | Medium | Needs attention |
| MTTR | X hours | High | Improving |

### Overall Rating: <Elite/High/Medium/Low>

### Recommendations
1. CFR at X% - invest in test automation and code review
2. Lead time trending up - look at PR review bottlenecks
3. Consider feature flags to decouple deploy from release

SEI Resource Types

Resource Type	Operations	Description
`sei_dora_metric`	get (+ `metric` param)	All 6 DORA variants: deployment_frequency, deployment_frequency_drilldown, lead_time, change_failure_rate, change_failure_rate_drilldown, mttr
`sei_team`	list, get	Team definitions
`sei_team_detail`	list (+ `aspect` param: developers / integrations / integration_filters)	Per-team sub-resources
`sei_metric`	list, get	Generic metrics
`sei_productivity_metric`	get	Productivity metrics
`sei_org_tree`	list, get	Organization structure
`sei_org_tree_detail`	list, get	Org tree detail
`sei_business_alignment`	get	Business alignment
`sei_ai_adoption`	get	AI adoption metrics
`sei_ai_impact`	get	AI impact metrics
`sei_ai_usage`	get	AI usage metrics
`sei_ai_raw_metric`	get	Raw AI metrics

Examples

"How are we doing on DORA metrics?" - Call
```
sei_dora_metric
```
four times with each primary
```
metric
```
"Compare DORA across teams" - List
```
sei_team
```
, then call
```
sei_dora_metric
```
per
```
team_ref_id
```
"What's our deployment frequency trend?" - Get
```
sei_dora_metric
```
with
```
metric: deployment_frequency
```
, then drilldown
"Show AI adoption metrics" - Get
```
sei_ai_adoption
```
and related AI resources

Performance Notes

Always pass

team_ref_id

,

date_start

,

date_end

,

granularity

— these are required.

Gather metrics across the full requested time range before generating the report. Partial data skews results.
Compare metrics across multiple time periods to identify trends, not just snapshots.

Troubleshooting

No Metric Data

Verify SEI integrations are configured (Git, CI/CD, issue tracking)

Confirm

team_ref_id

belongs to an active SEI team (

harness_list resource_type: sei_team

)

Check the date range covers data the integrations have ingested
Allow time for data collection and calculation after new integrations are added

Metrics Seem Incorrect

Verify deployment detection rules in SEI settings
Check failure classification criteria
Review team member mappings via
```
sei_team_detail aspect: developers
```