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dora-metrics

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.

Quality

84%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

71%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Highly actionable content with concrete MCP invocations, a report template, and troubleshooting, but token efficiency suffers from heavy repetition of near-identical call blocks and everything is inlined in one file rather than split across reference files. The workflow is well sequenced for a read-only reporting task.

Suggestions

Collapse the four primary-metric call blocks (and two drilldown blocks) into one exemplar harness_get invocation plus the existing metric-variant bullet list, noting that only the metric parameter changes.

Move the SEI Resource Types catalog and DORA Benchmarks table to a references/ file (e.g., references/sei-resources.md) and link them from the body to keep SKILL.md a lean overview.

Add an explicit validation checkpoint before report generation, e.g., confirm all four primary metrics returned data covering the full date range (per the Performance Notes) before composing the report.

DimensionReasoningScore

Conciseness

The six near-identical MCP call blocks (the four primary metrics differ only in the "metric" parameter value) and the resource table re-listing all six variants repeat the same "team_ref_id"/"date_start"/"date_end"/"granularity" payload seven times; content is mostly efficient but could be tightened to one exemplar call plus the variant list.

3 / 5

Actionability

Every operation is a concrete, ready-to-execute MCP call with exact resource_type, metric, parameter names, date formats, and granularity enums, plus a copy-paste report template, benchmark table, worked examples, and troubleshooting steps covering the common cases.

5 / 5

Workflow Clarity

Steps 1-4 give a clear sequence (get metric, drilldown, team data, optional AI metrics) and the Troubleshooting section supplies error-recovery guidance for missing or incorrect data, but the main flow itself has no explicit validation checkpoints before producing the report, fitting 'clear sequence with most checkpoints present'.

4 / 5

Progressive Disclosure

This is a single ~210-line SKILL.md with no bundle files; the 13-row SEI resource-type catalog and benchmark tables are inlined where moving them to a references/ file would keep the body an overview, though section headers are clear and easy to navigate.

3 / 5

Total

15

/

20

Passed

Description

88%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description that clearly states capabilities and provides explicit 'Use when' trigger guidance. The main gaps are missing natural trigger variations (change failure rate, MTTR, mean time to recovery) and slightly broad team-performance phrasing that could overlap with adjacent skills.

DimensionReasoningScore

Specificity

"Generate DORA metrics and engineering performance reports" and "Track deployment frequency, lead time, change failure rate, and MTTR" list multiple concrete actions covering all four standard DORA metrics comprehensively, matching the anchor for complete coverage rather than minor gaps.

5 / 5

Completeness

The description explicitly answers both 'what' (generate DORA metrics and performance reports via Harness SEI, tracking the four named metrics) and 'when' ("Use when user says..." with concrete trigger phrases), matching the anchor that requires both clearly and explicitly.

5 / 5

Trigger Term Quality

Triggers "DORA metrics", "deployment frequency", "lead time", "engineering metrics", and "team performance" are natural user phrases, but common variations like "change failure rate", "MTTR", and "mean time to recovery" are absent, fitting 'good coverage with a few natural terms missing' rather than comprehensive.

4 / 5

Distinctiveness Conflict Risk

DORA metrics is a clear niche with distinct triggers, but the broad phrases "engineering metrics" and "asks about team performance" create minor overlap risk with general productivity or engineering-metrics skills, fitting 'mostly distinct; minor overlap risk' rather than minimal conflict risk.

4 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

harness/harness-ai

Path

plugins/claude/skills/dora-metrics/SKILL.md

Reviewed

Table of Contents

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