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-sensors-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-sensors-to-orchestra)<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/dagster-sensors-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/dagster-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.
<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/dagster-sensors-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/dagster-sensors-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 | $0.00124 | $0.02037 |
| Opus 5.5 | $0.00050 | $0.00815 |
| Sonnet 5.5 | $0.00025 | $0.00407 |
| Haiku 4.5 | $0.00012 | $0.00204 |
Grade A, and why
dagster-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 2mo 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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dagster Sensors -> Orchestra Sensors Block
Overview
Dagster sensors are functions evaluated on a tick interval that inspect external state and yield RunRequest(...) to launch a run when a condition is met (or SkipReason otherwise). In Orchestra, sensors are pipeline triggers — they live in the sensors: block at the pipeline root (not inside pipeline:). When all of a sensor's checks pass, the pipeline run is triggered automatically.
Key difference: Dagster sensors run arbitrary Python every tick. Orchestra sensors use a cron window + polling interval model with declarative checks drawn from SensorChecksEnum.
SensorModel Structure
sensors:
<sensor-id>:
name: My Sensor
cron: '0 8 * * ? *' # when the check window opens
timezone: UTC
timeout_mins: 60 # max 7200; must be < cron interval
frequency_secs: 60 # polling interval (60-600)
exclude: []
run_inputs: {}
checks:
<check-id>:
integration: SNOWFLAKE
sensor_type: SNOWFLAKE_QUERY
connection: my_snowflake_12345
parameters:
query: "SELECT COUNT(*) FROM daily_files WHERE date = CURRENT_DATE"
map_outputs:
file_count: "result"
alerts:
- name: sensor-timed-out
statuses: [FAILED]
destinations:
- integration: SLACK
destination: '#data-alerts'
Valid SensorChecksEnum Values
sensor_type |
Integration | What it checks |
|---|---|---|
AWS_S3_FILE |
AWS_S3 |
File exists at S3 prefix |
ADLS_FILE |
AZURE_DATA_LAKE_STORAGE |
File exists in ADLS container |
SFTP_FILE |
SFTP |
File exists on SFTP server |
SNOWFLAKE_QUERY |
SNOWFLAKE |
SQL query returns rows |
POSTGRES_QUERY |
POSTGRES |
SQL query returns rows |
GCP_BIG_QUERY_QUERY |
GCP_BIG_QUERY |
SQL query returns rows |
SQL_SERVER_QUERY |
SQL_SERVER |
SQL query returns rows |
DATABRICKS_QUERY |
DATABRICKS |
SQL query returns rows |
FABRIC_SYNAPSE_QUERY |
FABRIC_SYNAPSE |
SQL query returns rows |
ORCHESTRA_PIPELINE_STATUS |
ORCHESTRA |
Another pipeline completed with a status |
ORCHESTRA_WEBHOOK_EVENT |
ORCHESTRA |
Webhook event received |
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.
- 2mo ago First seen · 240 lines · 124 tokens per session scan A ffc3b95c429f
dagster-sensors-to-orchestra is a skill published in the GitHub repository orchestra-hq/orchestra-skills (9 stars, last pushed yesterday), licensed MIT. It adds 124 tokens to every session and 2,037 once invoked, about $0.0005 per session on Opus 5.5. 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.
dagster-sensors-to-orchestra compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dagster-sensors-to-orchestraorchestra-hq/orchestra-skills | Skill | 9 | yesterday | MIT | A | 124 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |