Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AltimateAI/data-engineering-skills --skill developing-incremental-modelsgit clone --depth 1 https://github.com/AltimateAI/data-engineering-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/altimateai/data-engineering-skills/developing-incremental-models)<a href="https://agentmods.dev/skills/altimateai/data-engineering-skills/developing-incremental-models"><img src="https://agentmods.dev/badge/skills/altimateai/data-engineering-skills/developing-incremental-models/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/altimateai/data-engineering-skills/developing-incremental-models"><img src="https://agentmods.dev/badge/skills/altimateai/data-engineering-skills/developing-incremental-models.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00112 | $0.02232 |
| Opus 5.5 | $0.00045 | $0.00893 |
| Sonnet 5.5 | $0.00022 | $0.00446 |
| Haiku 4.5 | $0.00011 | $0.00223 |
Grade A, and why
developing-incremental-models scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- developing-incremental-models — 94% identical, 102 lines differ
How it starts
The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dbt Incremental Model Development
Choose the right strategy. Design the unique_key carefully. Handle edge cases.
When to Use Incremental
| Scenario | Recommendation |
|---|---|
| Source data < 10M rows | Use table (simpler, full refresh is fast) |
| Source data > 10M rows | Consider incremental |
| Source data updated in place | Use incremental with merge strategy |
| Append-only source (logs, events) | Use incremental with append strategy |
| Partitioned warehouse data | Use insert_overwrite if supported |
Default to table unless you have a clear performance reason for incremental.
Critical Rules
- ALWAYS test with
--full-refreshfirst before relying on incremental logic - ALWAYS verify unique_key is truly unique in both source and target
- If merge fails 3+ times, check unique_key for duplicates
- Run full refresh periodically to prevent data drift
Workflow
1. Confirm Incremental is Needed
# Check source table size
dbt show --inline "select count(*) from {{ source('schema', 'table') }}"
If count < 10 million, consider using table instead. Incremental adds complexity.
2. Understand the Source Data Pattern
Before choosing a strategy, answer:
- Is data append-only? (new rows added, never updated)
- Are existing rows updated? (need merge/upsert)
- Is there a reliable timestamp? (for filtering new data)
- What's the unique identifier? (for merge matching)
# Check for timestamp column
dbt show --inline "
select
min(updated_at) as earliest,
max(updated_at) as latest,
count(distinct date(updated_at)) as days_of_data
from {{ source('schema', 'table') }}
"
3. Choose the Right Strategy
| Strategy | Use When | How It Works |
|---|---|---|
append |
Data is append-only, no updates | INSERT only, no deduplication |
merge |
Data can be updated | MERGE/UPSERT by unique_key |
delete+insert |
Data updated in batches | DELETE matching rows, then INSERT |
insert_overwrite |
Partitioned tables (BigQuery, Spark) | Replace entire partitions |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 325 lines · 112 tokens per session scan A 0d4c3168f224
developing-incremental-models is a skill published in the GitHub repository AltimateAI/data-engineering-skills (129 stars, last pushed 5d ago), licensed MIT. It adds 112 tokens to every session and 2,232 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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