Is dora-metrics safe to install?

SaferSkills independently audited dora-metrics (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

skills/dora-metrics/SKILL.md · 1 file

skills/dora-metrics/SKILL.md6.2 KB · Markdown

name: dora-metrics

description: 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.

metadata:

author: Harness

version: 2.1.0

mcp-server: harness-mcp-v2

license: Apache-2.0

compatibility: Requires Harness MCP v2 server (harness-mcp-v2). All six DORA metric variants are served by a single consolidated `sei_dora_metric` resource — pick the variant via the `metric` parameter.

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

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

dora-metrics— agent skill

Is dora-metrics safe to install?

DORA Metrics

Instructions

Step 1: Get a DORA Metric

Step 2: Get Drilldown Data

Step 3: Get Team Data

Step 4: AI Metrics (Optional)

DORA Benchmarks

Report Format

SEI Resource Types

Examples

Performance Notes

Troubleshooting

No Metric Data

Metrics Seem Incorrect

Audit the pieces. Scan the whole. Decide.