airflow-sensors-to-orchestra

airflow-sensors-to-orchestra is a skill for Claude Code from orchestra-hq/orchestra-skills. It costs 83 tokens per session (2,285 once invoked), scanned A, original, MIT.

A migration guide for replacing Airflow sensors with Orchestra sensors. Airflow is a platform for scheduling data workflows, and sensors wait for an outside condition—such as a file arriving or a database query succeeding—before work continues.

In plain words
What is it for?
Use it to convert S3 file checks, local file checks, SQL checks, external-task checks, time checks, and other Airflow sensor patterns into Orchestra configuration.
Why use it?
It clarifies a key difference: Airflow sensors sit inside a workflow, while Orchestra sensors watch for conditions and start a pipeline run. This helps preserve the intended waiting and triggering behavior during migration.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the migrate-to-orchestra plugin — 52 skills shipped together

Good fit Use it to convert S3 file checks, local file checks, SQL checks, external-task checks, time checks, and other Airflow sensor patterns into Orchestra configuration.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/orchestra-hq/orchestra-skills/airflow-sensors-to-orchestra
Install

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.

Any agent
npx skills add orchestra-hq/orchestra-skills --skill airflow-sensors-to-orchestra
Clone the repo
git clone --depth 1 https://github.com/orchestra-hq/orchestra-skills

Made for: Claude Code.

Or install migrate-to-orchestra, the plugin that ships this one along with the rest of its 52 skills.

Wrote 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.

agentmods badge for airflow-sensors-to-orchestra

README.md
[![agentmods](https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/airflow-sensors-to-orchestra/github.svg)](https://agentmods.dev/skills/orchestra-hq/orchestra-skills/airflow-sensors-to-orchestra)
Your own site
<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/airflow-sensors-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/airflow-sensors-to-orchestra/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.

agentmods 80×15 button for airflow-sensors-to-orchestra

Your own site · 80×15
<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/airflow-sensors-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/airflow-sensors-to-orchestra.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 83 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,285 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00083 $0.02285
Opus 5 $0.00042 $0.01143
Sonnet 5 $0.00017 $0.00457
Haiku 4.5 $0.00008 $0.00229

Measured 12d ago against content hash df27512d890b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

airflow-sensors-to-orchestra 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 12d 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.

skills/migrate-to-orchestra/skills/airflow-sensors-to-orchestra/SKILL.md · 274 lines

How it starts

The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Airflow Sensors → Orchestra Sensors Block

Overview

Airflow sensors are tasks that poll an external system until a condition is met, then allow downstream tasks to proceed. In Orchestra, sensors are pipeline triggers — they live in the sensors: block at the pipeline root (not inside pipeline:). When all sensor checks pass, the pipeline run is triggered automatically.

Key difference: Airflow sensors are inline DAG steps. Orchestra sensors are external watchers that start pipeline runs. They use a cron window + polling interval model.

Sensor checks also carry a connection: field — for mapping the sensor's aws_conn_id=/conn_id= to the right Orchestra connection type, see airflow-connections-to-orchestra.


SensorModel Structure

sensors:
  <sensor-id>:
    name: My Sensor                  # required, max 100 chars
    cron: '0 8 * * ? *'               # required — when the check window opens
    timezone: UTC                    # required — IANA timezone
    timeout_mins: 60                 # required — max 7200; must be < cron interval
    frequency_secs: 60               # optional — polling interval (60–600, default 60)
    exclude: []                      # optional — YYYY-MM-DD dates to skip
    run_inputs: {}                   # optional — inputs to pass when sensor triggers

    checks:                          # required — dict of SensorCheckModel
      <check-id>:
        integration: SNOWFLAKE       # IntegrationsEnum
        sensor_type: SNOWFLAKE_QUERY # SensorChecksEnum (see table below)
        connection: my_snowflake_12345
        parameters:
          query: "SELECT COUNT(*) FROM daily_files WHERE date = CURRENT_DATE"
        map_outputs:                 # optional — pipe check results to pipeline inputs
          file_count: "result"       # pipeline input name → check output field

    alerts:                          # optional — sensor-level alerts
      - name: sensor-timed-out
        statuses: [FAILED]
        destinations:
          - integration: SLACK
            destination: '#data-alerts'

Read the full file on GitHub · 274 lines

Changes

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.

  1. 12d ago First seen · 274 lines · 83 tokens per session scan A df27512d890b

Subscribe to this mod's changes

airflow-sensors-to-orchestra is a skill published in the GitHub repository orchestra-hq/orchestra-skills (9 stars, last pushed 3d ago), licensed MIT. It adds 83 tokens to every session and 2,285 once invoked, about $0.0004 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 2026-08-31.

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