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 orchestra-hq/orchestra-skills --skill prefect-automations-to-orchestragit clone --depth 1 https://github.com/orchestra-hq/orchestra-skillsWrote 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/orchestra-hq/orchestra-skills/prefect-automations-to-orchestra)<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/prefect-automations-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/prefect-automations-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.
<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/prefect-automations-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/prefect-automations-to-orchestra.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 | $0.00093 | $0.01703 |
| Opus 5 | $0.00046 | $0.00851 |
| Sonnet 5 | $0.00019 | $0.00341 |
| Haiku 4.5 | $0.00009 | $0.00170 |
Grade A, and why
prefect-automations-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.
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Prefect Automations react to external events (file arrivals, database state, webhook calls, or schedules) and trigger flow runs. In Orchestra, the equivalent is the sensors: block — a set of named checks that poll external systems on a cron schedule and gate the pipeline run. Automations that fire on flow state changes (failed/succeeded) are not sensors; those become alerts: (see prefect-alerts-to-orchestra).
Parameter Mapping
| Prefect construct | Orchestra field | Notes |
|---|---|---|
| Automation: S3 object created event → trigger | sensors.checks with sensor_type: AWS_S3_FILE |
integration: AWS_S3 |
| Automation: scheduled (cron) | schedule: at pipeline root |
see prefect-flow-structure-to-orchestra |
| Automation: webhook trigger | webhook: {enabled: true} at pipeline root |
|
@flow polling a DB table (Snowflake) |
sensors.checks with sensor_type: SNOWFLAKE_QUERY |
|
@flow polling SQL Server |
sensors.checks with sensor_type: SQL_SERVER_QUERY |
|
| Automation: upstream flow completion → trigger | trigger_events: at pipeline root |
see prefect-cross-flow-to-orchestra |
| Automation: flow failed → notify | alerts: block |
NOT a sensor |
SensorModel.timeout_mins |
max 1440 (24 h) | Prefect has no equivalent cap |
map_outputs |
pipes sensor query result to pipeline inputs: |
declare matching key in pipeline inputs: |
Valid sensor_type values (SensorChecksEnum — exactly 9):
AWS_S3_FILE, ADLS_FILE, SFTP_FILE, SNOWFLAKE_QUERY, SQL_SERVER_QUERY, DATABRICKS_QUERY, FABRIC_SYNAPSE_QUERY, ORCHESTRA_PIPELINE_STATUS, ORCHESTRA_WEBHOOK_EVENT
POSTGRES_QUERY and GCP_BIG_QUERY_QUERY are NOT valid sensor types.
Orchestra YAML Structure
version: v1
name: pipeline-name
sensors:
<sensor-key>:
name: <human-readable name>
cron: '<cron expression>'
timezone: UTC
timeout_mins: 60 # max 1440
frequency_secs: 60 # how often checks are polled within the window
checks:
<check-key>:
integration: <INTEGRATION>
sensor_type: <SensorChecksEnum value>
connection: <connection_name>
parameters:
<integration-specific params>
map_outputs: # optional — pipe result to pipeline inputs
<input_key>: result
pipeline:
<stages and tasks>
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.
- 12d ago First seen · 175 lines · 93 tokens per session scan A 781ceb00f4f3
prefect-automations-to-orchestra is a skill published in the GitHub repository orchestra-hq/orchestra-skills (9 stars, last pushed 3d ago), licensed MIT. It adds 93 tokens to every session and 1,703 once invoked, about $0.0005 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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