Dx Data Navigator

npx skills add pskoett/pskoett-ai-skills/dx-data-navigator

mcp__dx-mcp-server__queryData(sql: "SELECT ...")

SELECT column_name, data_type FROM information_schema.columns
WHERE table_name = 'table_name' ORDER BY ordinal_position;

SELECT st.name as team, i.name as metric, MAX(ts.score) as score, MAX(ts.vs_industry50) as vs_industry
FROM dx_snapshot_team_scores ts
JOIN dx_snapshot_teams st ON ts.snapshot_team_id = st.id
JOIN dx_snapshot_items i ON ts.item_id = i.id AND i.snapshot_id = ts.snapshot_id
WHERE ts.snapshot_id = (SELECT id FROM dx_snapshots ORDER BY end_date DESC LIMIT 1)
  AND st.name = 'Your Team Name'
  AND i.item_type = 'core4'
GROUP BY st.name, i.name;

SELECT t.name, COUNT(*) as prs
FROM pull_requests p
JOIN dx_users u ON p.dx_user_id = u.id
JOIN dx_teams t ON u.team_id = t.id
WHERE p.merged IS NOT NULL GROUP BY t.name;

SELECT name FROM dx_teams WHERE deleted_at IS NULL ORDER BY name;

-- Latest snapshot info
SELECT id, start_date, end_date, contributors, participation_rate
FROM dx_snapshots ORDER BY end_date DESC LIMIT 1;

-- Team scores for specific metric (use GROUP BY to dedupe)
SELECT st.name as team, i.name as metric, MAX(ts.score) as score, MAX(ts.vs_industry50) as vs_industry
FROM dx_snapshot_team_scores ts
JOIN dx_snapshot_teams st ON ts.snapshot_team_id = st.id
JOIN dx_snapshot_items i ON ts.item_id = i.id AND i.snapshot_id = ts.snapshot_id
WHERE ts.snapshot_id = (SELECT id FROM dx_snapshots ORDER BY end_date DESC LIMIT 1)
  AND st.name = 'Your Team Name'
  AND i.item_type = 'core4'
GROUP BY st.name, i.name;

-- All teams comparison on one metric
SELECT st.name as team, MAX(ts.score) as score, MAX(ts.vs_industry50) as vs_industry
FROM dx_snapshot_team_scores ts
JOIN dx_snapshot_teams st ON ts.snapshot_team_id = st.id
JOIN dx_snapshot_items i ON ts.item_id = i.id AND i.snapshot_id = ts.snapshot_id
WHERE ts.snapshot_id = (SELECT id FROM dx_snapshots ORDER BY end_date DESC LIMIT 1)
  AND i.name = 'Effectiveness' AND i.item_type = 'core4'
  AND st.parent = false
GROUP BY st.name
ORDER BY score DESC NULLS LAST;

-- Teams with contributor counts
SELECT name, contributors FROM dx_teams WHERE deleted_at IS NULL ORDER BY contributors DESC;

-- Users with AI adoption status
SELECT name, email, ai_heavy_adoption_date FROM dx_users
WHERE ai_heavy_adoption_date IS NOT NULL ORDER BY ai_heavy_adoption_date DESC;

-- Team members
SELECT u.name, u.email FROM dx_users u
JOIN dx_teams t ON u.team_id = t.id
WHERE t.name = 'Your Team Name';

-- PR metrics by team last 30 days
SELECT t.name, COUNT(*) as prs,
       AVG(p.open_to_merge)/3600 as avg_hours_to_merge,
       AVG(p.open_to_first_review)/3600 as avg_hours_to_first_review
FROM pull_requests p
JOIN dx_users u ON p.dx_user_id = u.id
JOIN dx_teams t ON u.team_id = t.id
WHERE p.merged IS NOT NULL AND p.created > NOW() - INTERVAL '30 days'
GROUP BY t.name ORDER BY prs DESC;

-- PR size distribution
SELECT
    CASE
        WHEN additions + deletions < 50 THEN 'XS (<50)'
        WHEN additions + deletions < 200 THEN 'S (50-199)'
        WHEN additions + deletions < 500 THEN 'M (200-499)'
        ELSE 'L (500+)'
    END as size_bucket,
    COUNT(*) as count,
    AVG(open_to_merge)/3600 as avg_hours
FROM pull_requests
WHERE merged IS NOT NULL AND created > NOW() - INTERVAL '90 days'
GROUP BY size_bucket ORDER BY avg_hours;

-- Deploy frequency by environment
SELECT environment, COUNT(*) FROM deployments
WHERE deployed_at > NOW() - INTERVAL '30 days' GROUP BY environment;

-- Deployment success rate
SELECT
    COUNT(*) as total,
    COUNT(*) FILTER (WHERE success) as successful,
    COUNT(*) FILTER (WHERE success)::float / COUNT(*) * 100 as success_rate
FROM deployments WHERE deployed_at > NOW() - INTERVAL '30 days';

-- Mean Time to Recovery (MTTR)
SELECT AVG(started_to_resolved)/3600 as avg_hours_to_resolve
FROM incidents
WHERE resolved_at IS NOT NULL AND priority IN ('1 - Critical', '2 - High');

-- Incidents by priority
SELECT priority, COUNT(*) FROM incidents
WHERE started_at > NOW() - INTERVAL '90 days' AND deleted = false
GROUP BY priority ORDER BY priority;

-- Copilot usage by team (via github_users email linking)
SELECT t.name as team, COUNT(DISTINCT c.login) as active_copilot_users
FROM github_copilot_daily_usages c
JOIN github_users gu ON c.login = gu.login
JOIN dx_users u ON gu.verified_emails = u.email
JOIN dx_teams t ON u.team_id = t.id
WHERE c.date > NOW() - INTERVAL '30 days' AND c.active = true
GROUP BY t.name ORDER BY active_copilot_users DESC;

-- Daily Copilot active users (overall)
SELECT date, COUNT(*) FILTER (WHERE active) as active_users
FROM github_copilot_daily_usages
WHERE date > NOW() - INTERVAL '30 days'
GROUP BY date ORDER BY date;

-- Copilot adoption rate (latest day)
SELECT
    COUNT(DISTINCT login) FILTER (WHERE active) as active_users,
    COUNT(DISTINCT login) as total_users,
    COUNT(DISTINCT login) FILTER (WHERE active)::float / COUNT(DISTINCT login) * 100 as adoption_pct
FROM github_copilot_daily_usages
WHERE date = (SELECT MAX(date) FROM github_copilot_daily_usages);

-- Weekly trend
SELECT DATE_TRUNC('week', date) as week,
       COUNT(DISTINCT login) FILTER (WHERE active) as active_users
FROM github_copilot_daily_usages
WHERE date > NOW() - INTERVAL '90 days'
GROUP BY week ORDER BY week;