forge-data
Skill2 starsChanged 5 days agoMITView on GitHub
Playbook for data pipelines: ETL/ELT, dbt (gebruik bij een datapijplijn).
2 other projects use a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- ForgeyClap/claude-forgeedited, first seen Oct 11, 2026
- luminary19/ateliertheir own version, first seen Jun 27, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 1
- ForgeyClap/claude-forge★ 230% same
Wrote their own version · 1
- luminary19/atelier★ 2same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository99 other files it gives its agents.
- docs/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
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The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 11
Use for: data engineering, ETL and dbt pipelines that must be trustworthy and repeatable.
- Very specific: line 34
dbt projects: read references/dbt.md before touching models — read-before-write, DRY layering, dbt show validation, unit tests vs data …
- Very specific: line 36
Plain Python / SQL / scheduler pipelines (no dbt): read references/etl-patterns.md — raw-first layering, idempotent loads, watermarks and …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Forge playbook — Data engineering / ETL / dbt
When to use
- Use for: data engineering, ETL and dbt pipelines that must be trustworthy and repeatable.
Do not duplicate ECC skills — defer to: systematic-debugging (pipeline failures), /test-coverage (transform test coverage), learn-codebase / forge-deeplearn (prime an existing warehouse before touching it). This file is orchestration only.
A data pipeline's job is to be trustworthy and repeatable, not just to "run once". The failure mode here is silent: a load that half-succeeds, a duplicate on retry, or a schema change upstream that quietly corrupts a downstream mart while every job still shows green. Treat the warehouse as the product.
Hard rules
- Schema contracts at every boundary.
- Idempotent + incremental.
- Data-quality tests are part of "done".
- Lineage is documented.
- No PII leakage.
- Dead-letter for validation failures.
- Reproducible runs.
Full text of every rule: references/hard-rules.md.
Team
Lead: build-boss (or integration-boss when the pipeline wires external sources). Full roster: references/team-and-tools.md.
Skills / commands / MCP
Skills, commands, MCP and opt-in dependencies: references/team-and-tools.md.
dbt projects: read references/dbt.md before touching models — read-before-write, DRY layering, dbt show validation, unit tests vs data tests, house-style docs one folder per pass, and failed-run triage (infrastructure / code / data). Never edit a failing test green without knowing why it fails.
Plain Python / SQL / scheduler pipelines (no dbt): read references/etl-patterns.md — raw-first layering, idempotent loads, watermarks and backfills, schema drift and dead letters, per-run quality checks.
Fan-out & flow
L2 for a single source→mart pipeline; L3 for a multi-source warehouse with several transformation layers. Parallel: independent source extractors ∥ independent staging models ∥ dq-test authoring (once the target schema/contract is fixed). Serial: source contract → extract/load (idempotent + incremental) → staging (typed, deduped) → transform/marts → data-quality tests → lineage/docs → freshness/monitoring. The contract is fixed before anyone parallelizes, so slices can't disagree on shape.
Domain gates
Schema contracts declared + enforced at each boundary; a rerun proven to produce no duplicates (idempotency evidence); incremental key + watermark in place; dq tests (not-null/unique/accepted-values/relationships) pass with real output; freshness + volume/row-count check present; lineage/DAG documented with no orphan models; PII classified, masked/scoped, and absent from logs and git; run reproducible with pinned deps + versioned SQL + env-only credentials; raw / staging / mart layers separated.
Ship-readiness
Evidence checklist before handoff: references/ship-readiness.md.
Safety, approval and honesty
- Approval / safety: warehouse/profile credentials in env, never in
profiles.ymlor git. - Approval / safety: PII classified, masked/hashed/scoped and absent from logs and git;
security-bossreview recommended whenever real personal data is in scope. - Honest failure: scaffolded but not run against a real or DuckDB target: say so; never claim idempotency or green tests that were not executed.
- Untrusted content: see
references/untrusted-content.md. - Shared gates for every playbook (honesty, owner approval, secrets, reviews):
.claude/docs/forge-reference/playbook-template.md.
References
references/hard-rules.md— full text of every hard rule.references/team-and-tools.md— full team roster, skills, commands, MCP and opt-in runtimes.references/ship-readiness.md— full ship-readiness evidence checklist.references/untrusted-content.md— Untrusted-content note.references/etl-patterns.md— ETL without dbt: idempotent loads, watermarks, drift, dead letters (Forge-native).references/polars.md— Polars or a pandas migration: lazy-first, version pin, sinks, write safety (adapted from sickn33/agentic-awesome-skills, MIT).references/dbt.md— dbt practice (adapted from dbt-labs/dbt-agent-skills, Apache-2.0; notice inLICENSE.txt).
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