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Agent skill

Tracing Downstream Lineage

by astronomer in astronomer/agents

Trace downstream data lineage and impact analysis. An agent skill from astronomer/agents.

Apache-2.0Auto-check passedData & Analytics

Install Tracing Downstream Lineage

skills CLI
$ npx skills add astronomer/agents --skill tracing-downstream-lineage -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install astronomer/agents tracing-downstream-lineage --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/astronomer/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tracing-downstream-lineage .claude/skills/tracing-downstream-lineage && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tracing-downstream-lineage
GitHub stars
451
Token cost
~1.2k tokens
SKILL.md length
484 words
Files
1
Skills in repo
34
Repo updated
First seen
Licence
Apache-2.0

At a glance

Trace downstream data lineage and impact analysis. An agent skill from astronomer/agents.

  • Works in 5 steps: Identify Direct Consumers → Build Dependency Tree → Categorize by Criticality → …
  • The user asks what depends on this data
  • SKILL.md covers Impact Analysis and Output: Impact Report
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Tracing Downstream Lineage is an agent skill from astronomer/agents. Trace downstream data lineage and impact analysis. Use when the user asks what depends on this data, what breaks if something changes, downstream dependencies, or needs to assess change risk before modifying a table or DAG.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Data governance and Data pipelines and ETL. It works with Astro. The repository describes itself as: AI agent tooling for data engineering workflows. The licence is Apache-2.0.

When your agent uses it

  • The user asks what depends on this data
  • What breaks if something changes
  • Downstream dependencies
  • Needs to assess change risk before modifying a table

Example prompts

  • “/tracing-downstream-lineage”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Identify Direct Consumers
  2. Build Dependency Tree
  3. Categorize by Criticality
  4. Assess Change Risk
  5. Find Stakeholders

What it can do on your machine

Read from SKILL.md and the folder at commit 486ee63. 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 sql).

    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

Tracing Downstream Lineage loads about 1.2k tokens when it runs. Until then it costs about 63 tokens; SKILL.md has 484 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~63
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

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

Auto-check passed

The automated check found no risky patterns in SKILL.md.

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 astronomer/agents at commit 486ee63, republished under its Apache-2.0 licence (© astronomer). 484 words, ~1,245 tokens.

Download SKILL.mdSave it as .claude/skills/tracing-downstream-lineage/SKILL.md (or your agent's skills folder).
name
tracing-downstream-lineage
description
Trace downstream data lineage and impact analysis. Use when the user asks what depends on this data, what breaks if something changes, downstream dependencies, or needs to assess change risk before modifying a table or DAG.

Downstream Lineage: Impacts

Answer the critical question: "What breaks if I change this?"

Use this BEFORE making changes to understand the blast radius.

Impact Analysis

Step 1: Identify Direct Consumers

Find everything that reads from this target:

For Tables:

  1. Search DAG source code: Look for DAGs that SELECT from this table

    • Use af dags list to get all DAGs
    • Use af dags source <dag_id> to search for table references
    • Look for: FROM target_table, JOIN target_table

  2. Check for dependent views:

    sql
    -- Snowflake
SELECT * FROM information_schema.view_table_usage
WHERE table_name = '<target_table>'

-- Or check SHOW VIEWS and search definitions

  3. Look for BI tool connections:

    • Dashboards often query tables directly
    • Check for common BI patterns in table naming (rpt_, dashboard_)
On Astro

If you're running on Astro, the Lineage tab in the Astro UI provides visual dependency graphs across DAGs and datasets, making downstream impact analysis faster. It shows which DAGs consume a given dataset and their current status, reducing the need for manual source code searches.

For DAGs:

  1. Check what the DAG produces: Use af dags source <dag_id> to find output tables
  2. Then trace those tables' consumers (recursive)
Step 2: Build Dependency Tree

Map the full downstream impact:

SOURCE: fct.orders
    |
    +-- TABLE: agg.daily_sales --> Dashboard: Executive KPIs
    |       |
    |       +-- TABLE: rpt.monthly_summary --> Email: Monthly Report
    |
    +-- TABLE: ml.order_features --> Model: Demand Forecasting
    |
    +-- DIRECT: Looker Dashboard "Sales Overview"
Step 3: Categorize by Criticality

Critical (breaks production):

  • Production dashboards
  • Customer-facing applications
  • Automated reports to executives
  • ML models in production
  • Regulatory/compliance reports

High (causes significant issues):

  • Internal operational dashboards
  • Analyst workflows
  • Data science experiments
  • Downstream ETL jobs

Medium (inconvenient):

  • Ad-hoc analysis tables
  • Development/staging copies
  • Historical archives

Low (minimal impact):

  • Deprecated tables
  • Unused datasets
  • Test data
Step 4: Assess Change Risk

For the proposed change, evaluate:

Schema Changes (adding/removing/renaming columns):

  • Which downstream queries will break?
  • Are there SELECT * patterns that will pick up new columns?
  • Which transformations reference the changing columns?

Data Changes (values, volumes, timing):

  • Will downstream aggregations still be valid?
  • Are there NULL handling assumptions that will break?
  • Will timing changes affect SLAs?

Deletion/Deprecation:

  • Full dependency tree must be migrated first
  • Communication needed for all stakeholders
Show full SKILL.md (174 more words)Show less
Step 5: Find Stakeholders

Identify who owns downstream assets:

  1. DAG owners: Check owners field in DAG definitions
  2. Dashboard owners: Usually in BI tool metadata
  3. Team ownership: Look for team naming patterns or documentation

Output: Impact Report

Summary

"Changing fct.orders will impact X tables, Y DAGs, and Z dashboards"

Impact Diagram
                    +--> [agg.daily_sales] --> [Executive Dashboard]
                    |
[fct.orders] -------+--> [rpt.order_details] --> [Ops Team Email]
                    |
                    +--> [ml.features] --> [Demand Model]
Detailed Impacts
DownstreamTypeCriticalityOwnerNotes
agg.daily_salesTableCriticaldata-engUpdated hourly
Executive DashboardDashboardCriticalanalyticsCEO views daily
ml.order_featuresTableHighml-teamRetraining weekly
Risk Assessment
Change TypeRisk LevelMitigation
Add columnLowNo action needed
Rename columnHighUpdate 3 DAGs, 2 dashboards
Delete columnCriticalFull migration plan required
Change data typeMediumTest downstream aggregations

Before making changes:

  1. Notify owners: @data-eng, @analytics, @ml-team
  2. Update downstream DAG: transform_daily_sales
  3. Test dashboard: Executive KPIs
  4. Schedule change during low-impact window
  • Trace where data comes from: tracing-upstream-lineage skill
  • Check downstream freshness: checking-freshness skill
  • Debug any broken DAGs: debugging-dags skill
  • Add manual lineage annotations: annotating-task-lineage skill
  • Build custom lineage extractors: creating-openlineage-extractors skill

© astronomer, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/tracing-downstream-lineage of astronomer/agents.

Open the folder on GitHubat commit 486ee63

Compare with similar skills

Tracing Downstream Lineage next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Tracing Downstream Lineage compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Tracing Downstream Lineage this skillastronomer/agents451—~1.2kAutomated safety check: PassApache-2.0
Chart Testsastronomer/airflow-chart297—~2.8kAutomated safety check: PassCustom licence
Functional Testsastronomer/airflow-chart297—~2.2kAutomated safety check: PassCustom licence
Data Quality Frameworkswshobson/agents40k11 repos~1.1kAutomated safety check: PassMIT
Helm Chartastronomer/airflow-chart297—~6.4kAutomated safety check: PassCustom licence
Monte Carlo Preventsickn33/agentic-awesome-skills47k1 repos~3.3kAutomated safety check: PassMIT

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Works with

Categories

Questions about Tracing Downstream Lineage

What does Tracing Downstream Lineage do?

Trace downstream data lineage and impact analysis. An agent skill from astronomer/agents. Tracing Downstream Lineage is an agent skill from astronomer/agents. Trace downstream data lineage and impact analysis.

When should I use Tracing Downstream Lineage?

Tracing Downstream Lineage fits situations like: the user asks what depends on this data; what breaks if something changes; downstream dependencies; needs to assess change risk before modifying a table.

How do I install Tracing Downstream Lineage in Claude Code?

Run `npx skills add astronomer/agents --skill tracing-downstream-lineage -a claude-code`. Or copy the skill folder (skills/tracing-downstream-lineage in astronomer/agents) into .claude/skills/tracing-downstream-lineage in your project. Claude Code loads it when a task matches its description.

How do I install Tracing Downstream Lineage in Codex?

Run `npx skills add astronomer/agents --skill tracing-downstream-lineage -a codex`. Or copy the skill folder (skills/tracing-downstream-lineage in astronomer/agents) into .agents/skills/tracing-downstream-lineage in your project. Codex loads it when a task matches its description.

Can I use Tracing Downstream Lineage in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add astronomer/agents --skill tracing-downstream-lineage -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tracing-downstream-lineage, .gemini/skills/tracing-downstream-lineage, .github/skills/tracing-downstream-lineage and .opencode/skills/tracing-downstream-lineage in your project.

What does Tracing Downstream Lineage need to run?

SKILL.md names no scripts, command-line tools or credentials: Tracing Downstream Lineage is instructions for the agent only.

Does Tracing Downstream Lineage access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Tracing Downstream Lineage safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Tracing Downstream Lineage use?

Tracing Downstream Lineage is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tracing Downstream Lineage use?

About 1.2k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Tracing Downstream Lineage?

Skills that share tags, products or a category with Tracing Downstream Lineage: Chart Tests (astronomer/airflow-chart, 297 stars), Functional Tests (astronomer/airflow-chart, 297 stars), Data Quality Frameworks (wshobson/agents, 40k stars) and Helm Chart (astronomer/airflow-chart, 297 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tracing Downstream Lineage?

astronomer (a GitHub organization) maintains it in astronomer/agents, which has 451 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on October 7, 2026.

Source: astronomer/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.