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 hicham-bab/dbt-legacy-migration-skills --skill remediating-lift-and-shift-to-dbtgit clone --depth 1 https://github.com/hicham-bab/dbt-legacy-migration-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/hicham-bab/dbt-legacy-migration-skills/remediating-lift-and-shift-to-dbt)<a href="https://agentmods.dev/skills/hicham-bab/dbt-legacy-migration-skills/remediating-lift-and-shift-to-dbt"><img src="https://agentmods.dev/badge/skills/hicham-bab/dbt-legacy-migration-skills/remediating-lift-and-shift-to-dbt/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/hicham-bab/dbt-legacy-migration-skills/remediating-lift-and-shift-to-dbt"><img src="https://agentmods.dev/badge/skills/hicham-bab/dbt-legacy-migration-skills/remediating-lift-and-shift-to-dbt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00090 | $0.01150 |
| Opus 5 | $0.00045 | $0.00575 |
| Sonnet 5 | $0.00018 | $0.00230 |
| Haiku 4.5 | $0.00009 | $0.00115 |
Grade A, and why
remediating-lift-and-shift-to-dbt 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remediating a lift-and-shift dbt project
A migration that "gives the same results" is not finished. A lift-and-shift often produces dbt that runs but carries the legacy tool's shape as technical debt. This skill takes an existing dbt project and refactors it to idiomatic dbt without changing its output. It is the counterpart to the legacy-to-dbt migration skills: same destination (idiomatic dbt), different starting point (already-migrated-badly, not the legacy tool).
Shares the common references in legacy-to-dbt-migration-foundations. Read the anti-pattern catalog first: anti-patterns.md.
Core principle
Parity is invariant; only the shape changes. Every refactor step must leave results identical. Capture a baseline before touching anything, and re-check parity after each change. "Done" means the project is idiomatic (clears the quality bar) and returns exactly what it returned before.
Workflow
- Step 0 - Detect the project and warehouse. Locate the dbt project, profile, and target platform
(see cloud-detection-and-materializations.md).
A
migration_decisions.ymlmay record the run's decisions. - Step 1 - Inventory the anti-patterns. Run the linter to enumerate what to fix:
python3 <skills-dir>/legacy-to-dbt-migration-foundations/scripts/lint_idiomatic.py <project-dir>anddbt build --select package:dbt_project_evaluator. Map each finding to its fix using anti-patterns.md. - Step 2 - Capture a parity baseline. Materialize or record the current output of every model you will touch, so the refactor can be proven results-preserving. Use the patterns in data-validation.md (audit_helper, or a full-outer-join / row-count baseline).
- Step 3 - Refactor one model family at a time, re-checking parity after each:
- Hooks: move
pre_hook/post_hooklogic into the model (a CTE), a test, a macro, or a snapshot; keep a hook only for a true side-effect (grants, cache warmups). - Monolith: decompose into staging -> (intermediate) -> mart via
ref()(layer-classification.md, depth per-workload, not a fixed template). - Hardcoded relations: replace
db.schema.tablewithref()/source(). - Control-flow: turn cursors/loops/
EXECUTE IMMEDIATE/CALLinto set-based SQL.
- Hooks: move
- Step 4 - Add tests, docs, contracts per dbt-best-practices.md (grain unique/not_null, accepted_values/relationships, descriptions; a data-quality DELETE that was in a hook usually becomes a test).
- Step 5 - Prove parity vs the Step 2 baseline. Every model's output must be unchanged; explain any diff (accept only environment/platform differences, never a logic change).
- Step 6 - Clear the quality bar. Re-run the linter and
dbt_project_evaluator; both must pass (or exceptions justified inremediation_changes.md). See coverage-report.md. - Step 7 - Document the before/after in
remediation_changes.md(anti-pattern -> fix, and the parity evidence).
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.
- 12d ago First seen · 74 lines · 90 tokens per session scan A 5dde1e6e8cae
remediating-lift-and-shift-to-dbt is a skill published in the GitHub repository hicham-bab/dbt-legacy-migration-skills (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 90 tokens to every session and 1,150 once invoked, about $0.0005 per session on Opus 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 2026-08-31.
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