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/plan-submitgit 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/plan-submit)<a href="https://agentmods.dev/commands/dagster-io/dagster/plan-submit"><img src="https://agentmods.dev/badge/commands/dagster-io/dagster/plan-submit.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 | $0.00008 | $0.00276 |
| Opus 5 | $0.00004 | $0.00138 |
| Sonnet 5 | $0.00002 | $0.00055 |
| Haiku 4.5 | $0.00001 | $0.00028 |
Grade A, and why
plan-submit 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:plan-submit
Goal
Find the most recent GitHub issue created in this conversation and submit it for remote AI implementation via erk plan submit.
What This Command Does
- Search conversation for the last GitHub issue reference
- Extract the issue number
- Run
erk plan submit <issue_number>to trigger remote implementation
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 plan submit <issue_number>
Display the command output to the user. The erk plan submit command handles all validation (issue existence, labels, state).
Error Cases
- No issue found in conversation: Report "No GitHub issue found in conversation. Run /erk:plan-save first to create an issue."
- erk plan submit fails: Display the error output from the command (erk plan submit validates the issue)
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 · 40 lines · 8 tokens per session scan A 50ddbf34293c
plan-submit is a command published in the GitHub repository dagster-io/dagster (16,073 stars, last pushed 6d ago), licensed Apache-2.0. It adds 8 tokens to every session and 276 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.