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/gh_issue_solvegit 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/gh_issue_solve)<a href="https://agentmods.dev/commands/dagster-io/dagster/gh_issue_solve"><img src="https://agentmods.dev/badge/commands/dagster-io/dagster/gh_issue_solve.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 5.1 | $0.00000 | $0.00864 |
| Opus 5 | $0.00000 | $0.00432 |
| Sonnet 5 | $0.00000 | $0.00173 |
| Haiku 4.5 | $0.00000 | $0.00086 |
Grade A, and why
gh_issue_solve 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 6d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Solve GitHub Issue
Analyzes a GitHub issue, validates it's actionable, and proposes an implementation plan.
Usage
/gh_issue_solve <issue_number>
What it does
-
Fetch Issue Details: Uses
gh apito retrieve the full issue content including:- Title and description
- Labels and assignees
- Comments and discussion context
- Linked PRs or related issues
-
Validate Issue Type: Determines if the issue is:
- Valid Bug: Reproducible issue with clear symptoms
- Valid Feature: Well-defined enhancement or new functionality
- Invalid/Unclear: Needs more information, duplicate, or out of scope
- Already Solved: Has existing PR or was resolved
-
Analyze Codebase: For valid issues:
- Locate relevant code using grep/glob
- Review existing implementations
- Check related tests
- Identify affected packages (using
.claude/python_packages.md)
-
Propose Implementation: Provides:
- Root cause analysis (for bugs)
- Implementation approach
- Files that need changes
- Testing strategy
- Potential edge cases or concerns
-
Create Todo List: Breaks down implementation into actionable steps
Requirements
- Must be run from the Dagster repository root
- GitHub CLI (
gh) must be authenticated - Issue number must be valid in
dagster-io/dagsterrepository
Example Workflow
# User runs command
/gh_issue_solve 12345
# Command fetches issue details
gh api repos/dagster-io/dagster/issues/12345
# Command analyzes and outputs:
# - Issue summary
# - Validation result (valid bug/feature or needs clarification)
# - Proposed implementation plan
# - Todo list of steps to complete the fix
Output Format
For Valid Issues
## Issue Analysis: #12345 - [Issue Title]
**Type**: Bug | Feature Request
**Status**: Valid and actionable
**Affected Area**: [e.g., Asset System, Sensors, UI]
### Summary
[Concise description of the issue]
### Root Cause (for bugs)
[Technical explanation of why the issue occurs]
### Proposed Implementation
**Files to Modify**:
- `path/to/file.py:123` - [what needs to change]
- `path/to/test.py` - [test additions]
**Approach**:
[Step-by-step technical approach]
**Testing Strategy**:
[How to verify the fix works]
**Potential Concerns**:
[Any edge cases or risks]
### Next Steps
[Todo list created for implementation]
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.
- 6d ago First seen · 142 lines · 0 tokens per session scan A 827b64181092
gh_issue_solve is a command published in the GitHub repository dagster-io/dagster (16,109 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 864 tokens. 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
fixing-pr
Fix CI failures and address PR review comments.
qa-testing-feature
Run QA testing on the current branch's feature.
reviewing-local-changes
Review changes in this branch for quality and issues.
simplifying-local-changes
Simplify and refine code changes in this branch.
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.