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sei-analytics

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Atualizado22 de maio de 2026 às 19:02

Advanced engineering analytics via Harness Software Engineering Insights (SEI) MCP. Configure sprint velocity and estimation accuracy tracking, engineering investment allocation breakdowns, sprint planning with capacity forecasts, and release readiness assessments. Use when asked about sprint analytics, investment allocation, sprint planning, capacity forecasting, or release readiness. Do NOT use for DORA metrics (use dora-metrics instead). Trigger phrases: sprint velocity, sprint analytics, investment allocation, capacity planning, sprint forecast, release readiness, engineering productivity, estimation accuracy, sprint planning, scope creep, code quality trends.

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

SKILL.md

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name	sei-analytics
description	Advanced engineering analytics via Harness Software Engineering Insights (SEI) MCP. Configure sprint velocity and estimation accuracy tracking, engineering investment allocation breakdowns, sprint planning with capacity forecasts, and release readiness assessments. Use when asked about sprint analytics, investment allocation, sprint planning, capacity forecasting, or release readiness. Do NOT use for DORA metrics (use dora-metrics instead). Trigger phrases: sprint velocity, sprint analytics, investment allocation, capacity planning, sprint forecast, release readiness, engineering productivity, estimation accuracy, sprint planning, scope creep, code quality trends.
metadata	{"author":"Harness","version":"1.0.0","mcp-server":"harness-mcp-v2"}
license	Apache-2.0
compatibility	Requires Harness MCP v2 server (harness-mcp-v2)

SEI Analytics

Configure sprint analytics, investment allocation, capacity forecasting, and release readiness assessments in Harness Software Engineering Insights.

Instructions

Step 1: Establish Scope

Confirm the user's org, team, and tracking period.

Call MCP tool: harness_list
Parameters:
  resource_type: "project"
  org_id: "<organization>"

Step 2: Identify the SEI Task

Determine which analytics the user needs:

Sprint Analytics -- Velocity, estimation accuracy, scope change tracking
Investment Allocation -- Engineering time breakdown by category
Sprint Planning and Capacity Forecast -- Data-driven sprint planning
Release Readiness Assessment -- Checklist for release go/no-go

Step 3: Configure Sprint Analytics

Gather from the user:

Team name and sprint length (weeks)
Tracking period (last N sprints)
Issue tracker integration (Jira, Azure DevOps, Linear)

Analyze sprint metrics:

Velocity:

Story points committed vs. completed per sprint
Velocity trend over the tracking period
Carryover rate (% of stories that spill into next sprint)

Estimation Accuracy:

Actual vs. estimated story points across sprints
Which issue types are consistently underestimated
Estimation accuracy trend (improving or degrading)

Scope Change:

Stories added mid-sprint (scope creep %)
Stories removed mid-sprint
Net scope change and its impact on completion rate

Delivery Composition:

Breakdown by issue type: features, bugs, tech debt, maintenance
Breakdown by priority: P0/P1 vs. lower priority
Team-level vs. individual-level trends

Step 4: Analyze Investment Allocation

Gather from the user:

Teams to analyze
Time period for analysis
Investment categories (features, bugs, tech debt, maintenance, ops)

Calculate allocation:

% of engineering time per category
Compare against target allocation (e.g., 70% features, 15% tech debt, 10% bugs, 5% ops)
Trend over time: is tech debt growing or shrinking?
Per-team breakdowns to identify teams disproportionately spending on bugs or ops

Present with:

Current allocation vs. target allocation
Recommendations for rebalancing
Impact of current allocation on velocity and quality trends

Step 5: Sprint Planning and Capacity Forecast

Gather from the user:

Sprint number, duration, and team size
Planned PTO, meetings, and other commitments

Calculate capacity:

Available engineering days = team size * sprint days - PTO - meeting overhead
Historical velocity per engineer per sprint
Recommended story points for the sprint (based on rolling average)

Risk factors to flag:

Large stories (>8 points) that could block the sprint
Dependencies on other teams
Carryover from previous sprint
Tech debt items that may slow feature work

Step 6: Release Readiness Assessment

Gather from the user:

Service name, version, target environment
Change summary and target release date

Assess readiness:

Code Quality:

Code coverage delta (current vs. threshold)
Static analysis findings (new warnings or errors)
Code review completion (all PRs reviewed and approved)

Pipeline Health:

Call MCP tool: harness_list
Parameters:
  resource_type: "execution"
  org_id: "<organization>"
  project_id: "<project>"

CI pipeline pass rate for the release branch
All required stages green (build, test, security, integration)

Testing:

Unit test pass rate and coverage
Integration test results
Performance test results vs. baseline

Security:

No unresolved critical or high CVEs
Security scan completed and approved
Compliance attestations present

Operational Readiness:

Monitoring and alerting configured for the release
Runbooks updated for new features
Rollback plan documented and tested

Present as a structured readiness report with GO/NO-GO recommendation.

Examples

"Show sprint velocity trends for the platform team" -- Analyze committed vs. completed points over the last N sprints
"How is our engineering investment split between features and bugs?" -- Calculate investment allocation with recommendations
"Help plan Sprint 24 based on our capacity" -- Calculate available capacity and recommend story point commitment
"Is the checkout-service v2.1 ready to release?" -- Run full readiness assessment with go/no-go recommendation
"Why are our sprints always over-committed?" -- Analyze estimation accuracy and scope change patterns

Performance Notes

Sprint analytics require consistent use of story points across the team -- mixed estimation methods reduce accuracy.
Investment allocation analysis needs accurate issue labeling -- unlabeled issues default to "unclassified" and skew results.
Capacity forecasts should account for context-switching overhead (typically 10-20% of engineering time).
Release readiness assessments should run at least 24 hours before the planned release date.

Troubleshooting

Velocity Data Inconsistent

Verify story points are assigned before sprint start (not mid-sprint)
Check that completed stories are marked done within the sprint, not after
Ensure the issue tracker integration is syncing correctly

Investment Allocation Missing Categories

Check issue labeling conventions -- all issues should have a type (feature, bug, tech debt)
Review the mapping between issue tracker labels and SEI categories
Add default categorization rules for unlabeled issues

Release Readiness Check Incomplete

Verify all pipeline stages have run for the release branch
Check that security scan results are available for the release artifacts
Ensure monitoring configuration has been validated in staging

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