Dagster is a platform for developing, running, and observing data assets, such as datasets and the processes that produce them. It is used to organize and automate data workflows.
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 agentmods add commands/dagster-io/dagster/preparegit clone --depth 1 https://github.com/dagster-io/dagsterWrote 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/commands/dagster-io/dagster/prepare)<a href="https://agentmods.dev/commands/dagster-io/dagster/prepare"><img src="https://agentmods.dev/badge/commands/dagster-io/dagster/prepare.svg" alt="Measured on agentmods" 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 | $0.00009 | $0.00314 |
| Opus 5 | $0.00005 | $0.00157 |
| Sonnet 5 | $0.00002 | $0.00063 |
| Haiku 4.5 | $0.00001 | $0.00031 |
Grade A, and why
prepare 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 4d 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.
What it actually says
/erk:prepare
Goal
Find the most recent GitHub plan issue created in this conversation and prepare a worktree for implementation via erk prepare.
What This Command Does
- Search conversation for the last GitHub issue reference
- Extract the issue number
- Run
erk prepare <issue_number>to create a worktree
Finding the Issue
Search the conversation from bottom to top for these patterns (in priority order):
- plan-save/save-raw-plan output: Look for
**Issue:** https://github.com/.../issues/<number> - Issue URL:
https://github.com/<owner>/<repo>/issues/<number>
Extract the issue number from the most recent match.
Execution
Once you have the issue number, run:
erk prepare <issue_number>
Display the command output to the user. The erk prepare command handles worktree creation and slot allocation.
The output will include activation instructions like:
To activate the worktree environment:
source /path/to/worktree/.erk/bin/activate.sh
Share these activation instructions with the user so they can switch to the new worktree.
Error Cases
- No issue found in conversation: Report "No GitHub plan issue found in conversation. Run /erk:plan-save first to create an issue."
- erk prepare fails: Display the error output from the command
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.
- 4d ago First seen · 49 lines · 9 tokens per session scan A fbca0f322ef0
prepare is a command published in the GitHub repository dagster-io/dagster (16,073 stars, last pushed 6d ago), licensed Apache-2.0. It adds 9 tokens to every session and 314 once invoked, about $0.0000 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 commands, from other repositories
OPSX: Continue
Continue working on a change - create the next artifact (Experimental).
OPSX: Fast Forward
Create a change and generate all artifacts needed for implementation in one go.
OPSX: Verify
Verify implementation matches change artifacts before archiving.
OPSX: Onboard
Guided onboarding - walk through a complete OpenSpec workflow cycle with narration.
OPSX: Apply
Implement tasks from an OpenSpec change (Experimental).
OPSX: Propose
Propose a new change - create it and generate all artifacts in one step.