agentleFS
ForgeyClapForgeyClap/Forge-Core/.claude/skills/forge-data/SKILL.md

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

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
  1. Distinctive:

    Use for: data engineering, ETL and dbt pipelines that must be trustworthy and repeatable.

    line 11
  2. Very specific:

    dbt projects: read references/dbt.md before touching models — read-before-write, DRY layering, dbt show validation, unit tests vs data …

    line 34
  3. Very specific:

    Plain Python / SQL / scheduler pipelines (no dbt): read references/etl-patterns.md — raw-first layering, idempotent loads, watermarks and …

    line 36

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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.yml or git.
  • Approval / safety: PII classified, masked/hashed/scoped and absent from logs and git; security-boss review 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 in LICENSE.txt).

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.