Tech Data Pipeline

What it does

When your agent uses it

• The user needs to move data between systems
• Build a data warehouse
• Automate data processing
• Improve data reliability — even if they say move data from X to Y

Example prompts

• “move data from X to Y”
• “build an ETL pipeline”
• “our data is a mess”
• “/tech-data-pipeline”

Requirements

• Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

1. 1Map sources and destinations: What data, from where, to where?
2. 2Define freshness requirements: Real-time? Hourly? Daily?
3. 3Choose architecture: ETL or ELT based on tools and team
4. 4Build incrementally: Start with one source, one destination, one schedule
5. 5Add quality checks: At minimum: row count + null check + freshness
6. 6Set up monitoring: Alert on failure, quality check violations, latency
7. 7Document: Data dictionary, pipeline diagram, SLAs

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Tech Data Pipeline loads about 1.5k tokens when it runs, and up to ~8.1k if it reads all its reference files. Until then it costs about 104 tokens; SKILL.md has 438 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~104

When it runs · the whole SKILL.md, loaded when a task matches

~1.5k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~8.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 438 words, ~1,454 tokens.

name

tech-data-pipeline

description

Design data pipelines covering ETL vs ELT architectures, data source integration, scheduling, quality checks, and warehouse design. Use this skill when the user needs to move data between systems, build a data warehouse, automate data processing, or improve data reliability — even if they say 'move data from X to Y', 'build an ETL pipeline', 'our data is a mess', or 'set up a data warehouse'.

metadata.category

WP-11 通用技術

metadata.tags

technology, data-pipeline, etl, data-engineering

IRON LAW: Data Quality Checks at Every Stage

A pipeline that moves bad data fast is worse than no pipeline — it
corrupts downstream analytics and decisions. Every pipeline stage
(extract, transform, load) must have data quality checks:
row counts, null checks, schema validation, freshness checks.

"Garbage in, garbage out" is not a warning — it's a guarantee.

[Sources] → [Extract] → [Stage] → [Transform] → [Load] → [Serve]
   ↑                                                          ↓
   |              [Quality Checks at every stage]        [Dashboard]
   |              [Monitoring & Alerting]                [API]
   └──────────────── [Orchestrator (Airflow/Prefect)] ──────┘

# Data Pipeline Design: {Project}

## Sources & Destinations
| Source | Type | Destination | Freshness | Volume |
|--------|------|-----------|-----------|--------|
| {source} | DB/API/File | {dest} | {daily/hourly} | {rows/day} |

## Architecture
- Pattern: ETL / ELT
- Orchestrator: {tool}
- Transform: {tool/SQL}
- Quality: {tool/custom checks}

## Pipeline Diagram
{Source} → {Extract} → {Stage} → {Transform} → {Load} → {Serve}

## Quality Checks
| Stage | Check | Threshold | Alert |
|-------|-------|-----------|-------|
| Extract | Row count | ±10% of prior | Slack alert |
| Load | Freshness | < 6 hours old | PagerDuty |

## Schedule
| Pipeline | Frequency | Start Time | SLA |
|----------|-----------|-----------|-----|
| {name} | {daily/hourly} | {time} | Data ready by {time} |

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in tech-data-pipeline of asgard-ai-platform/skills.

• SKILL.md
• examples/sample_scenario.md
• references/dbt-guide.md
• references/dimensional-modeling.md

Open the folder on GitHubat commit 4e7f4f8