How do I install the Managing Dbt skill?

Run npx skillmds@latest add cloudthinker-ai/managing-dbt in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Managing Dbt skill do?

Manages and monitors dbt projects, model runs, and test results via dbt CLI and dbt Cloud API, covering run status, test failures, source freshness, and manifest analysis. It is listed under DevOps & Infra, Data & Analytics, CI/CD, Data Analysis, Monitoring & Observability on SkillMD.

Is Managing Dbt safe to use?

SkillMD's automated safety review verdict for this skill is CAUTION. Independent scanners report: SkillSpector: CAUTION, Skill Scanner: PASS. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Managing Dbt?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Managing Dbt free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Managing Dbt?

cloudthinker-ai (@cloudthinker-ai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Managing Dbt by cloudthinker-ai — Agent Skill

Name: Managing Dbt
Rating: 4.2 (5 reviews)
Author: cloudthinker-ai

dbt Management Skill

Manage and monitor dbt projects, model runs, and test results via dbt CLI and dbt Cloud API.

MANDATORY: Discovery-First Pattern

Always discover project structure and recent run status before querying specific models or tests.

Phase 1: Discovery

#!/bin/bash

# dbt Cloud API helper
dbt_cloud_api() {
    local method="${1:-GET}"
    local endpoint="$2"
    local data="${3:-}"

    if [ -n "$data" ]; then
        curl -s -X "$method" \
            -H "Authorization: Token ${DBT_CLOUD_API_TOKEN}" \
            -H "Content-Type: application/json" \
            "https://cloud.getdbt.com/api/v2/${endpoint}" \
            -d "$data"
    else
        curl -s -X "$method" \
            -H "Authorization: Token ${DBT_CLOUD_API_TOKEN}" \
            "https://cloud.getdbt.com/api/v2/${endpoint}"
    fi
}

echo "=== dbt Cloud Account Info ==="
dbt_cloud_api GET "accounts/${DBT_CLOUD_ACCOUNT_ID}/" | jq '{
    name: .data.name,
    plan: .data.plan,
    run_slots: .data.run_slots
}'

echo ""
echo "=== Projects ==="
dbt_cloud_api GET "accounts/${DBT_CLOUD_ACCOUNT_ID}/projects/" | jq -r '
    .data[] | "\(.id)\t\(.name)\t\(.connection.type // "N/A")\t\(.repository.remote_url // "N/A")"
' | column -t

echo ""
echo "=== Recent Runs ==="
dbt_cloud_api GET "accounts/${DBT_CLOUD_ACCOUNT_ID}/runs/?limit=15&order_by=-finished_at" | jq -r '
    .data[] | "\(.id)\t\(.status_humanized)\t\(.job.name // .job_id)\t\(.finished_at[0:16] // "running")"
' | column -t

Core Helper Functions

#!/bin/bash

dbt_cloud_api() {
    local method="${1:-GET}"
    local endpoint="$2"
    local data="${3:-}"

    if [ -n "$data" ]; then
        curl -s -X "$method" \
            -H "Authorization: Token ${DBT_CLOUD_API_TOKEN}" \
            -H "Content-Type: application/json" \
            "https://cloud.getdbt.com/api/v2/${endpoint}" \
            -d "$data"
    else
        curl -s -X "$method" \
            -H "Authorization: Token ${DBT_CLOUD_API_TOKEN}" \
            "https://cloud.getdbt.com/api/v2/${endpoint}"
    fi
}

# dbt Cloud Administrative API (v3)
dbt_admin_api() {
    local method="${1:-GET}"
    local endpoint="$2"
    curl -s -X "$method" \
        -H "Authorization: Token ${DBT_CLOUD_API_TOKEN}" \
        "https://cloud.getdbt.com/api/v3/accounts/${DBT_CLOUD_ACCOUNT_ID}/${endpoint}"
}

Output Rules

TOKEN EFFICIENCY: Target ≤50 lines per output
Use jq to extract only needed fields from API responses
Never dump full manifest.json — extract specific nodes
Filter runs by status at the API level

Common Operations

Job Run Dashboard

#!/bin/bash
ACCOUNT_ID="${DBT_CLOUD_ACCOUNT_ID}"

echo "=== Job Run Summary ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/?limit=50&order_by=-finished_at" | jq '
    .data | group_by(.status_humanized) | map({status: .[0].status_humanized, count: length}) |
    .[] | "\(.status): \(.count)"
' -r

echo ""
echo "=== Failed Runs (recent) ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/?limit=10&order_by=-finished_at&status=20" | jq -r '
    .data[] | "\(.id)\t\(.job.name // .job_id)\t\(.finished_at[0:16])\t\(.duration_humanized)"
' | column -t

echo ""
echo "=== Currently Running ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/?limit=10&status=3" | jq -r '
    .data[] | "\(.id)\t\(.job.name // .job_id)\t\(.created_at[0:16])\t\(.duration_humanized)"
' | column -t

Run Details and Model Results

#!/bin/bash
ACCOUNT_ID="${DBT_CLOUD_ACCOUNT_ID}"
RUN_ID="${1:?Run ID required}"

echo "=== Run Details ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/${RUN_ID}/" | jq '{
    id: .data.id,
    status: .data.status_humanized,
    job_name: .data.job.name,
    duration: .data.duration_humanized,
    started: .data.started_at,
    finished: .data.finished_at,
    git_sha: .data.git_sha
}'

echo ""
echo "=== Run Artifacts (model results) ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/${RUN_ID}/artifacts/run_results.json" | jq -r '
    .results[] |
    select(.status == "error" or .status == "fail") |
    "\(.unique_id)\t\(.status)\t\(.execution_time | floor)s\t\(.message[0:80] // "")"
' | column -t | head -20

Test Results Analysis

#!/bin/bash
ACCOUNT_ID="${DBT_CLOUD_ACCOUNT_ID}"
RUN_ID="${1:?Run ID required}"

echo "=== Test Results Summary ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/${RUN_ID}/artifacts/run_results.json" | jq '
    [.results[] | select(.unique_id | startswith("test."))] |
    group_by(.status) | map({status: .[0].status, count: length}) |
    .[] | "\(.status): \(.count)"
' -r

echo ""
echo "=== Failed Tests ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/${RUN_ID}/artifacts/run_results.json" | jq -r '
    .results[] |
    select(.unique_id | startswith("test.")) |
    select(.status == "fail" or .status == "error") |
    "\(.unique_id | split(".") | .[-1])\t\(.status)\t\(.failures) failures\t\(.message[0:80] // "")"
' | column -t | head -15

echo ""
echo "=== Slowest Tests ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/${RUN_ID}/artifacts/run_results.json" | jq -r '
    [.results[] | select(.unique_id | startswith("test."))] |
    sort_by(-.execution_time) | .[:10][] |
    "\(.unique_id | split(".") | .[-1])\t\(.execution_time | floor)s\t\(.status)"
' | column -t

Source Freshness

#!/bin/bash
ACCOUNT_ID="${DBT_CLOUD_ACCOUNT_ID}"
RUN_ID="${1:?Run ID required}"

echo "=== Source Freshness Results ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/${RUN_ID}/artifacts/sources.json" | jq -r '
    .results[] |
    "\(.unique_id)\t\(.status)\t\(.max_loaded_at[0:16] // "unknown")\t\(.criteria.warn_after.count // "?") \(.criteria.warn_after.period // "")"
' | column -t | head -20

echo ""
echo "=== Stale Sources ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/${RUN_ID}/artifacts/sources.json" | jq -r '
    .results[] |
    select(.status == "warn" or .status == "error") |
    "\(.unique_id)\t\(.status)\t\(.max_loaded_at[0:16] // "unknown")\t\(.snapshotted_at[0:16])"
' | column -t

Manifest and Lineage Analysis

#!/bin/bash
ACCOUNT_ID="${DBT_CLOUD_ACCOUNT_ID}"
RUN_ID="${1:?Run ID required}"

echo "=== Model Count by Materialization ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/${RUN_ID}/artifacts/manifest.json" | jq -r '
    [.nodes[] | select(.resource_type == "model") | .config.materialized] |
    group_by(.) | map({type: .[0], count: length}) |
    sort_by(-.count) | .[] | "\(.type)\t\(.count)"
' | column -t

echo ""
echo "=== Models Without Tests ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/${RUN_ID}/artifacts/manifest.json" | jq -r '
    .nodes | to_entries |
    map(select(.value.resource_type == "model")) |
    map(select(
        [.key as $k | .value.depends_on.nodes[]? | select(startswith("test."))] | length == 0
    )) |
    .[:10][] | .value.unique_id
'

echo ""
echo "=== Models Without Descriptions ==="
dbt_cloud_api GET "accounts/${ACCOUNT_ID}/runs/${RUN_ID}/artifacts/manifest.json" | jq -r '
    [.nodes[] | select(.resource_type == "model") | select(.description == "" or .description == null)] |
    .[:10][] | .unique_id
'

Output Format

Present results as a structured report:

Managing Dbt Report
═══════════════════
Resources discovered: [count]

Resource       Status    Key Metric    Issues
──────────────────────────────────────────────
[name]         [ok/warn] [value]       [findings]

Summary: [total] resources | [ok] healthy | [warn] warnings | [crit] critical
Action Items: [list of prioritized findings]

Target ≤50 lines of output. Use tables for multi-resource comparisons.

Anti-Hallucination Rules

NEVER assume resource names — always discover via CLI/API in Phase 1 before referencing in Phase 2.
NEVER fabricate metric names or dimensions — verify against the service documentation or --help output.
NEVER mix CLI commands between service versions — confirm which version/API you are targeting.
ALWAYS use the discovery → verify → analyze chain — every resource referenced must have been discovered first.
ALWAYS handle empty results gracefully — an empty response is valid data, not an error to retry.

Counter-Rationalizations

Shortcut	Counter	Why
"I'll skip discovery and check known resources"	Always run Phase 1 discovery first	Resource names change, new resources appear — assumed names cause errors
"The user only asked for a quick check"	Follow the full discovery → analysis flow	Quick checks miss critical issues; structured analysis catches silent failures
"Default configuration is probably fine"	Audit configuration explicitly	Defaults often leave logging, security, and optimization features disabled
"Metrics aren't needed for this"	Always check relevant metrics when available	API/CLI responses show current state; metrics reveal trends and intermittent issues
"I don't have access to that"	Try the command and report the actual error	Assumed permission failures prevent useful investigation; actual errors are informative

Common Pitfalls

Run status codes: 10=success, 20=error, 30=cancelled, 3=running — use status_humanized for readability
Artifact availability: Artifacts (manifest, run_results, sources) only available after run completes
API rate limits: dbt Cloud API has rate limits — avoid rapid sequential calls, use specific endpoints
Manifest size: manifest.json can be very large — always filter with jq, never dump entirely
Test naming: Tests are prefixed with test. in unique_id — schema tests auto-generate names from column/model
Source freshness: Only available if the job includes dbt source freshness step — not all jobs run it
dbt Core vs Cloud: CLI commands (dbt run, dbt test) work locally; API endpoints are dbt Cloud only
Job vs Run: A Job is the definition (schedule, commands), a Run is a specific execution of that Job
Environment branches: dbt Cloud environments may target different git branches — check environment config before comparing runs

Managing Dbt

File contents

dbt Management Skill

MANDATORY: Discovery-First Pattern

Phase 1: Discovery

Core Helper Functions

Output Rules

Common Operations

Job Run Dashboard

Run Details and Model Results

Test Results Analysis

Source Freshness

Manifest and Lineage Analysis

Output Format

Anti-Hallucination Rules

Counter-Rationalizations

Common Pitfalls

Frequently asked questions

Managing Dbt

File contents

dbt Management Skill

MANDATORY: Discovery-First Pattern

Phase 1: Discovery

Core Helper Functions

Output Rules

Common Operations

Job Run Dashboard

Run Details and Model Results

Test Results Analysis

Source Freshness

Manifest and Lineage Analysis

Output Format

Anti-Hallucination Rules

Counter-Rationalizations

Common Pitfalls

Related skills

Frequently asked questions

How do I install the Managing Dbt skill?

What does the Managing Dbt skill do?

Is Managing Dbt safe to use?

Which AI agents work with Managing Dbt?

Is Managing Dbt free to use?

Who published Managing Dbt?