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harnessharness/harness-skills/skills/observability-governance/dora-metrics/SKILL.md

dora-metrics

Skill134 starsChanged 32 days agoApache-2.0View on GitHub

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.

3 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

6 rules. 0 are common, 4 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 3 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    All DORA metrics are served by a single resource type: sei_dora_metric.

    line 19
  2. Very specific:

    Always pass team_ref_id, date_start, date_end, granularity — these are required.

    line 194
  3. Distinctive:

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

    line 201
  4. Distinctive:

    Check the date range covers data the integrations have ingested

    line 203
  5. Distinctive:

    Verify deployment detection rules in SEI settings

    line 207
  6. Distinctive:

    Check failure classification criteria

    line 208

“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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"

Related: sei_ai_impact, sei_ai_usage, sei_ai_raw_metric.

DORA Benchmarks

MetricEliteHighMediumLow
Deployment FrequencyMultiple/dayWeekly-MonthlyMonthly-6mo6mo+
Lead Time< 1 hour1 day-1 week1-6 months6mo+
Change Failure Rate< 5%5-10%10-15%> 15%
MTTR< 1 hour< 1 day1 day-1 week1 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 TypeOperationsDescription
sei_dora_metricget (+ metric param)All 6 DORA variants: deployment_frequency, deployment_frequency_drilldown, lead_time, change_failure_rate, change_failure_rate_drilldown, mttr
sei_teamlist, getTeam definitions
sei_team_detaillist (+ aspect param: developers / integrations / integration_filters)Per-team sub-resources
sei_metriclist, getGeneric metrics
sei_productivity_metricgetProductivity metrics
sei_org_treelist, getOrganization structure
sei_org_tree_detaillist, getOrg tree detail
sei_business_alignmentgetBusiness alignment
sei_ai_adoptiongetAI adoption metrics
sei_ai_impactgetAI impact metrics
sei_ai_usagegetAI usage metrics
sei_ai_raw_metricgetRaw 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

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