creating-dbt-models

Name: creating-dbt-models
Author: AltimateAI

23installs7forks

How to install creating-dbt-models?

altimateai/data-engineering-skills/creating-dbt-models

$npx -y skills add AltimateAI/data-engineering-skills --skill creating-dbt-models

Installs into the current project.

Summary

Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions "create", "build", "add", "write", "new", or "implement" with model, table, or SQL (3) Modi

Files · 1

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SKILL.md

1	# dbt Model Development
2
3	Read before you write. Build after you write. Verify your output.
4
5	## Critical Rules
6
7	1. ALWAYS run `dbt build` after creating/modifying models - compile is NOT enough
8	2. ALWAYS verify output after build using `dbt show` - don't assume success
9	3. If build fails 3+ times, stop and reassess your entire approach
10
11	## Workflow
12
13	### 1. Understand the Task Requirements
14
15	- What columns are needed? List them explicitly.
16	- What is the grain of the table (one row per what)?
17	- What calculations or aggregations are required?
18
19	### 2. Discover Project Conventions
20
21	```bash
22	cat dbt_project.yml
23	find models/ -name "*.sql" \| head -20
24	```
25
26	Read 2-3 existing models to learn naming, config, and SQL patterns.
27
28	### 3. Find Similar Models
29
30	```bash
31	# Find models with similar purpose
32	find models/ -name "agg.sql" -o -name "fct_.sql" \| head -5
33	```
34
35	Learn from existing models: join types, aggregation patterns, NULL handling.
36
37	### 4. Check Upstream Data
38
39	```bash
40	# Preview upstream data if needed
41	dbt show --select <upstream_model> --limit 10
42	```
43
44	### 5. Write the Model
45
46	Follow discovered conventions. Match the required columns exactly.
47
48	### 6. Compile (Syntax Check)
49
50	```bash
51	dbt compile --select <model_name>
52	```
53
54	### 7. BUILD - MANDATORY
55
56	This step is REQUIRED. Do NOT skip it.
57
58	```bash
59	dbt build --select <model_name>
60	```
61
62	If build fails:
63	1. Read the error carefully
64	2. Fix the specific issue
65	3. Run build again
66	4. If fails 3+ times, step back and reassess approach
67
68	### 8. Verify Output (CRITICAL)
69
70	Build success does NOT mean correct output.
71
72	```bash
73	# Check the table was created and preview data
74	dbt show --select <model_name> --limit 10
75	```
76
77	Verify:
78	- Column names match requirements exactly
79	- Row count is reasonable
80	- Data values look correct
81	- No unexpected NULLs
82
83	### 9. Verify Calculations Against Sample Data
84
85	For models with calculations, verify correctness manually:
86
87	```bash
88	# Pick a specific row and verify calculation by hand
89	dbt show --inline "
90	select *
91	from {{ ref('model_name') }}
92	where <primary_key> = '<known_value>'
93	" --limit 1
94
95	# Cross-check aggregations
96	dbt show --inline "
97	select count(*), sum(<column>)
98	from {{ ref('model_name') }}
99	"
100	```
101
102	For example, if calculating `total_revenue = quantity * price`:
103	1. Pick one row from output
104	2. Look up the source quantity and price
105	3. Manually calculate: does it match?
106
107	### 10. Re-review Against Requirements
108
109	Before declaring done, re-read the original request:
110	- Did you implement what was asked, not what you assumed?
111	- Are column names exactly as specified?
112	- Is the calculation logic correct per the requirements?
113	- Does the grain (one row per what?) match what was requested?
114
115	## Anti-Patterns
116
117	- Declaring done after compile without running build
118	- Not verifying output data after build
119	- Getting stuck in compile/build error loops
120	- Assuming table exists just because model file exists
121	- Writing SQL without checking existing model patterns first

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Type

creating-dbt-models

23installs7forks

View on GitHub

How to install creating-dbt-models?

altimateai/data-engineering-skills/creating-dbt-models

$npx -y skills add AltimateAI/data-engineering-skills --skill creating-dbt-models

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# dbt Model Development
2
3	Read before you write. Build after you write. Verify your output.
4
5	## Critical Rules
6
7	1. ALWAYS run `dbt build` after creating/modifying models - compile is NOT enough
8	2. ALWAYS verify output after build using `dbt show` - don't assume success
9	3. If build fails 3+ times, stop and reassess your entire approach
10
11	## Workflow
12
13	### 1. Understand the Task Requirements
14
15	- What columns are needed? List them explicitly.
16	- What is the grain of the table (one row per what)?
17	- What calculations or aggregations are required?
18
19	### 2. Discover Project Conventions
20
21	```bash
22	cat dbt_project.yml
23	find models/ -name "*.sql" \| head -20
24	```
25
26	Read 2-3 existing models to learn naming, config, and SQL patterns.
27
28	### 3. Find Similar Models
29
30	```bash
31	# Find models with similar purpose
32	find models/ -name "agg.sql" -o -name "fct_.sql" \| head -5
33	```
34
35	Learn from existing models: join types, aggregation patterns, NULL handling.
36
37	### 4. Check Upstream Data
38
39	```bash
40	# Preview upstream data if needed
41	dbt show --select <upstream_model> --limit 10
42	```
43
44	### 5. Write the Model
45
46	Follow discovered conventions. Match the required columns exactly.
47
48	### 6. Compile (Syntax Check)
49
50	```bash
51	dbt compile --select <model_name>
52	```
53
54	### 7. BUILD - MANDATORY
55
56	This step is REQUIRED. Do NOT skip it.
57
58	```bash
59	dbt build --select <model_name>
60	```
61
62	If build fails:
63	1. Read the error carefully
64	2. Fix the specific issue
65	3. Run build again
66	4. If fails 3+ times, step back and reassess approach
67
68	### 8. Verify Output (CRITICAL)
69
70	Build success does NOT mean correct output.
71
72	```bash
73	# Check the table was created and preview data
74	dbt show --select <model_name> --limit 10
75	```
76
77	Verify:
78	- Column names match requirements exactly
79	- Row count is reasonable
80	- Data values look correct
81	- No unexpected NULLs
82
83	### 9. Verify Calculations Against Sample Data
84
85	For models with calculations, verify correctness manually:
86
87	```bash
88	# Pick a specific row and verify calculation by hand
89	dbt show --inline "
90	select *
91	from {{ ref('model_name') }}
92	where <primary_key> = '<known_value>'
93	" --limit 1
94
95	# Cross-check aggregations
96	dbt show --inline "
97	select count(*), sum(<column>)
98	from {{ ref('model_name') }}
99	"
100	```
101
102	For example, if calculating `total_revenue = quantity * price`:
103	1. Pick one row from output
104	2. Look up the source quantity and price
105	3. Manually calculate: does it match?
106
107	### 10. Re-review Against Requirements
108
109	Before declaring done, re-read the original request:
110	- Did you implement what was asked, not what you assumed?
111	- Are column names exactly as specified?
112	- Is the calculation logic correct per the requirements?
113	- Does the grain (one row per what?) match what was requested?
114
115	## Anti-Patterns
116
117	- Declaring done after compile without running build
118	- Not verifying output data after build
119	- Getting stuck in compile/build error loops
120	- Assuming table exists just because model file exists
121	- Writing SQL without checking existing model patterns first

People who liked this also viewed

6 picks

Type