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 dagster-cross-job-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/dagster-cross-job-to-orchestra)<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/dagster-cross-job-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/dagster-cross-job-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/dagster-cross-job-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/dagster-cross-job-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 | $0.00101 | $0.01422 |
| Opus 5 | $0.00051 | $0.00711 |
| Sonnet 5 | $0.00020 | $0.00284 |
| Haiku 4.5 | $0.00010 | $0.00142 |
Grade A, and why
dagster-cross-job-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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dagster Cross-Job Triggering -> Orchestra
Overview
Dagster cross-job patterns — @run_status_sensor launching a downstream job, @asset_sensor on an upstream asset, and cross-code-location asset dependencies — all have Orchestra equivalents. Orchestra provides two mechanisms:
-
TRIGGER_PIPELINEtask — an explicit pipeline step that triggers another pipeline and waits for it to complete. Use when one pipeline must trigger another mid-flow and wait. -
trigger_events:block — event-driven triggering at the pipeline root. When an upstream pipeline completes, this pipeline starts automatically. This is the cleaner equivalent of@run_status_sensor/@asset_sensor.
@run_status_sensor (SUCCESS, launching a job) -> trigger_events: (preferred)
# Dagster
@run_status_sensor(
run_status=DagsterRunStatus.SUCCESS,
monitored_jobs=[nightly_elt],
request_job=daily_report,
)
def trigger_report_on_elt_success(context):
return RunRequest(run_config={"ops": {"build": {"config": {"env": "prod"}}}})
# Orchestra — daily-report pipeline
version: v1
name: daily-report
trigger_events:
- type: pipeline
pipeline_id: "uuid-of-nightly-elt-pipeline"
statuses: [SUCCEEDED, WARNING]
run_inputs:
env: prod
pipeline:
...
TriggerEventModel fields:
| Field | Required | Notes |
|---|---|---|
type |
yes | Always pipeline |
pipeline_id |
yes | UUID of the upstream pipeline, or "*" for any |
statuses |
no | Default [SUCCEEDED, WARNING] |
run_inputs |
no | Inputs passed to the triggered run |
Multiple entries = OR logic (fires when any upstream completes).
Explicit launch-and-wait -> ORCHESTRA + TRIGGER_PIPELINE
When a pipeline must trigger another and wait before continuing:
task-001:
integration: ORCHESTRA
integration_job: TRIGGER_PIPELINE
name: trigger_reporting
parameters:
pipeline_id: "uuid-of-daily-reporting-pipeline"
run_inputs:
env: prod
branch: null
depends_on: []
condition: null
tags: []
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 · 188 lines · 101 tokens per session scan A 8874b6151ee3
dagster-cross-job-to-orchestra is a skill published in the GitHub repository orchestra-hq/orchestra-skills (9 stars, last pushed 3d ago), licensed MIT. It adds 101 tokens to every session and 1,422 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.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.