Borrowing it
Nothing to install: this file belongs to daochild/agents-config. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/daochild/agents-config/main/.opencode/skill/data-engineer/SKILL.mdgit clone --depth 1 https://github.com/daochild/agents-configWrote 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/daochild/agents-config/data-engineer)<a href="https://agentmods.dev/skills/daochild/agents-config/data-engineer"><img src="https://agentmods.dev/badge/skills/daochild/agents-config/data-engineer/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/daochild/agents-config/data-engineer"><img src="https://agentmods.dev/badge/skills/daochild/agents-config/data-engineer.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 | $0.00028 | $0.02227 |
| Opus 5 | $0.00014 | $0.01113 |
| Sonnet 5 | $0.00006 | $0.00445 |
| Haiku 4.5 | $0.00003 | $0.00223 |
Grade A, and why
data-engineer 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 yesterday.
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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: data-engineer
Role: Data Engineering Lead — builds reliable, maintainable data pipelines, enforces data quality, and designs schemas that evolve safely.
When to Use
- Building ETL/ELT pipelines (batch or streaming)
- Designing/evolving data schemas (JSON Schema, Protobuf, Avro, SQL DDL)
- Implementing data quality checks, contracts, observability
- Migrating data between systems, backfilling, deduplication
- Building search indexes, materialized views, analytics layers
- Setting up CI/CD for data: validation, schema registry, contract testing
Core Principles
1. Contracts First, Code Second
- Define schemas before writing pipelines
- Use schema registry (or checked-in JSON Schema/Protobuf)
- Enforce compatibility rules (BACKWARD, FORWARD, FULL)
- CI fails on breaking changes without migration plan
2. Idempotency & Replayability
- Every pipeline step is idempotent (re-runnable safely)
- Partition by time + deterministic keys
- Store raw/immutable input; derive everything else
scripts/etl/scripts accept--dry-run,--since,--backfill
3. Data Quality as Code
- Schema validation on read AND write
- Great Expectations / dbt tests / custom validators in CI
- SLIs: freshness, completeness, uniqueness, validity, consistency
- Alert on SLO breach; quarantine bad data, don't silently drop
4. Layered Architecture (Medallion)
data/
├── raw/ # Immutable, partitioned by source+date
│ └── source=github/date=2024-01-15/*.jsonl
├── curated/ # Cleaned, typed, deduplicated, conformed
│ └── entities/*.parquet
└── derived/ # Aggregations, search indexes, ML features
├── search-index/
└── analytics/
5. Observability by Default
- Structured logging (JSON) with correlation IDs
- Metrics: rows processed, latency, error rate, data quality scores
- Lineage: source → transformation → destination (OpenLineage or custom)
- Dashboards per pipeline; alert on staleness > 2x schedule
Standard Project Structure
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.
- yesterday First seen · 254 lines · 28 tokens per session scan A a8f00ce7b32f
data-engineer is a skill published in the GitHub repository daochild/agents-config (10 stars, last pushed 21d ago), licensed MIT. It adds 28 tokens to every session and 2,227 once invoked, about $0.0001 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 2026-09-07.
Other skills, from other repositories
prompt-literacy-sequence-designer
Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.
convex-agents
Building AI agents with the Convex Agent component including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
guidance
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework.
llama-factory
Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support.
prompt-master
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…