DX Data Navigator

Install

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

Query the DX Data Cloud PostgreSQL database using the mcp__dx-mcp-server__queryData tool.

Tool Usage

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

Always query information_schema.columns first if uncertain about table/column names:

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

Critical: Team Tables

Three team table types exist - use the right one:

For DX survey scores: Join through dx_snapshot_teams. Use GROUP BY to avoid duplicates (team names can appear multiple times across snapshot history):

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;

For PR/deployment metrics by team: Join through dx_users to dx_teams:

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;

Discovering Team Names

Query the database to find available teams:

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

Data Domains

Core DX Metrics

Survey snapshots with team scores, benchmarks, and sentiment data.

Key tables: dx_snapshots, dx_snapshot_teams, dx_snapshot_items, dx_snapshot_team_scores

dx_snapshots columns: id, account_id, contributors, participation_rate, start_date (date), end_date (date)

dx_snapshot_teams columns: id, snapshot_id, team_id, name, parent (boolean), flattened_parent, contributors, participation_rate

dx_snapshot_items columns: id, snapshot_id, name, item_type, prompt, target_label

dx_snapshot_team_scores columns: id, snapshot_id, snapshot_team_id (FK to dx_snapshot_teams.id), team_id (FK to dx_teams.id), item_id (FK to dx_snapshot_items.id), score, vs_org, vs_prev, vs_industry50, vs_industry75, vs_industry90, unit

Item types in dx_snapshot_items:

• core4: Effectiveness, Impact, Quality, Speed
• kpi: Ease of delivery, Engagement, Weekly time loss, Quality, Speed
• sentiment: Deep work, Change Confidence, Documentation, Cross-team collaboration, Customer focus, Decision-making, etc.
• workflow: Review wait time, CI wait time, Deploy frequency, PR merge frequency, AI time savings, Red tape, etc.
• workflow_averages: Raw average values for workflow metrics (actual numbers, not percentiles)
• csat: Tool satisfaction scores (e.g., code editors, issue trackers, CI/CD tools)

-- 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 and Users

Organization structure, team hierarchies, user profiles.

Key tables: dx_teams, dx_users, dx_team_hierarchies, dx_groups

dx_teams columns: id, name, contributors, deleted_at

dx_users key columns: id, name, email, team_id, ai_light_adoption_date, ai_moderate_adoption_date, ai_heavy_adoption_date

-- 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';

Pull Requests

PR metrics including cycle times, review wait times, and throughput.

Key tables: pull_requests, pull_request_reviews, repos

pull_requests key columns: id, dx_user_id, repo_id, title, base_ref, head_ref, additions, deletions, created, merged, closed, draft, bot_authored

Key metrics (all in seconds, divide by 3600 for hours):

• open_to_merge: Total PR cycle time
• open_to_first_review: Time to first review
• open_to_first_approval: Time to approval
• Business hour variants: add _business_hours suffix

-- 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)/
how to use dx-data-navigator
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use dx-data-navigator on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add dx-data-navigator
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/pskoett/pskoett-ai-skills --skill dx-data-navigatorcopy
The skills CLI fetches dx-data-navigator from GitHub repository pskoett/pskoett-ai-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/dx-data-navigator
Reload or restart Cursor to activate dx-data-navigator. Access the skill through slash commands (e.g., /dx-data-navigator) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?