Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add astronomer/agents --skill tracing-upstream-lineagegit clone --depth 1 https://github.com/astronomer/agentsWrote 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/astronomer/agents/tracing-upstream-lineage)<a href="https://agentmods.dev/skills/astronomer/agents/tracing-upstream-lineage"><img src="https://agentmods.dev/badge/skills/astronomer/agents/tracing-upstream-lineage/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/astronomer/agents/tracing-upstream-lineage"><img src="https://agentmods.dev/badge/skills/astronomer/agents/tracing-upstream-lineage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00040 | $0.01050 |
| Opus 5 | $0.00020 | $0.00525 |
| Sonnet 5 | $0.00008 | $0.00210 |
| Haiku 4.5 | $0.00004 | $0.00105 |
Grade A, and why
tracing-upstream-lineage 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 9d ago.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- tracing-upstream-lineage — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Upstream Lineage: Sources
Trace the origins of data - answer "Where does this data come from?"
Lineage Investigation
Step 1: Identify the Target Type
Determine what we're tracing:
- Table: Trace what populates this table
- Column: Trace where this specific column comes from
- DAG: Trace what data sources this DAG reads from
Step 2: Find the Producing DAG
Tables are typically populated by Airflow DAGs. Find the connection:
-
Search DAGs by name: Use
af dags listand look for DAG names matching the table nameload_customers->customerstableetl_daily_orders->orderstable
-
Explore DAG source code: Use
af dags source <dag_id>to read the DAG definition- Look for INSERT, MERGE, CREATE TABLE statements
- Find the target table in the code
-
Check DAG tasks: Use
af tasks list <dag_id>to see what operations the DAG performs
On Astro
If you're running on Astro, the Lineage tab in the Astro UI provides visual lineage exploration across DAGs and datasets. Use it to quickly trace upstream dependencies without manually searching DAG source code.
On OSS Airflow
Use DAG source code and task logs to trace lineage (no built-in cross-DAG UI).
Step 3: Trace Data Sources
From the DAG code, identify source tables and systems:
SQL Sources (look for FROM clauses):
# In DAG code:
SELECT * FROM source_schema.source_table # <- This is an upstream source
External Sources (look for connection references):
S3Operator-> S3 bucket sourcePostgresOperator-> Postgres database sourceSalesforceOperator-> Salesforce API sourceHttpOperator-> REST API source
File Sources:
- CSV/Parquet files in object storage
- SFTP drops
- Local file paths
Step 4: Build the Lineage Chain
Recursively trace each source:
TARGET: analytics.orders_daily
^
+-- DAG: etl_daily_orders
^
+-- SOURCE: raw.orders (table)
| ^
| +-- DAG: ingest_orders
| ^
| +-- SOURCE: Salesforce API (external)
|
+-- SOURCE: dim.customers (table)
^
+-- DAG: load_customers
^
+-- SOURCE: PostgreSQL (external DB)
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.
- 9d ago First seen · 138 lines · 40 tokens per session scan A f7e540bb391c
tracing-upstream-lineage is a skill published in the GitHub repository astronomer/agents (439 stars, last pushed 4d ago), licensed Apache-2.0. It adds 40 tokens to every session and 1,050 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
sl
Skill "sl" from Kaelio/ktx, covering semantic layer, part 1 - schema reference, overlay sources, standalone table sources and standalone sql sources.
historic_sql_patterns
Identify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection.
historic_sql_table_digest
Convert one changed historic-SQL table usage bucket into typed table usage evidence for deterministic schema projection.
live_database_ingest
Capture semantic-layer and knowledge updates from a live database schema snapshot.
coverage-tracker
Run a Google Alerts-style keyword coverage tracker. Uses news-search for recent keyword queries, lets the LLM dedupe and classify real features versus junk, stores decisions in SQLite, and alerts only on new real coverage.
ktx
Installs and configures ktx, the open-source context layer for data agents — runs ktx setup non-interactively with hidden CLI flags, configures database connections and embeddings, installs agent integration, and verifies readiness. Use when the user asks an agent to add ktx to a project, connect data sources, install…