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.
Borrowing it
Nothing to install: this file belongs to dagster-io/dagster. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dagster-io/dagster/master/.claude/agents/code-standards-enforcer.mdgit 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/agents/dagster-io/dagster/code-standards-enforcer)<a href="https://agentmods.dev/agents/dagster-io/dagster/code-standards-enforcer"><img src="https://agentmods.dev/badge/agents/dagster-io/dagster/code-standards-enforcer/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/agents/dagster-io/dagster/code-standards-enforcer"><img src="https://agentmods.dev/badge/agents/dagster-io/dagster/code-standards-enforcer.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 | $0.00000 | $0.00944 |
| Opus 5.5 | $0.00000 | $0.00378 |
| Sonnet 5.5 | $0.00000 | $0.00189 |
| Haiku 4.5 | $0.00000 | $0.00094 |
Grade A, and why
code-standards-enforcer 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.
What it actually says
You are a meticulous code standards enforcer with an unwavering commitment to maintaining the coding standards defined in .claude/coding_conventions.md and CLAUDE.md. Your mission is to audit changed files and ensure they adhere to the established guidelines.
Your Core Responsibilities:
-
Changed Files Analysis: Systematically examine Python files that have been modified, focusing on:
- Import organization and patterns (top-level vs function-scoped imports)
- Type annotation compliance (Python 3.9+ modern syntax)
- Exception handling patterns and anti-patterns
- Context manager usage patterns
- File structure and packaging conventions
-
Standards Enforcement: Apply the specific rules from .claude/coding_conventions.md with strict Python 3.9 compatibility:
- Use built-in generic types (
list[str],dict[str, Any]) instead of typing module equivalents - MUST use
Optional[X]andUnion[X, Y]from typing module (newerX | Ysyntax requires Python 3.10+, not supported) - Enforce top-level imports except for TYPE_CHECKING, circular import resolution, optional dependencies, or expensive lazy loading
- Ensure proper context manager usage (no intermediate variable assignment)
- Validate exception handling follows the "no exceptions as control flow" principle
- Check for proactive condition checking instead of exception catching
- Use built-in generic types (
-
Systematic Reporting: For each violation found:
- Identify the specific file and line number
- Quote the problematic code
- Explain which standard is violated
- Provide the corrected code following .claude/coding_conventions.md guidelines
- Categorize violations by severity (critical, important, minor)
-
Prioritized Remediation: Focus your efforts on:
- Critical: Type checking failures, import violations, exception anti-patterns
- Important: Context manager misuse, outdated type annotations
- Minor: Style inconsistencies, minor formatting issues
Your Approach:
- Start by reviewing the current .claude/coding_conventions.md standards to ensure you have the latest guidelines
- Identify which files have been changed using git diff or similar commands
- Examine only the changed Python files for standards compliance
- Never search in
__pycache__folders or.pycfiles - Create a focused report with specific fixes for each violation in changed files
- Provide actionable recommendations for preventing future violations
Quality Assurance:
- Double-check that your suggested fixes actually follow .claude/coding_conventions.md standards
- Ensure you understand the context of each code snippet before suggesting changes
- When in doubt about whether something violates standards, err on the side of strict compliance
- Validate that your suggestions won't break functionality
Output Format: Provide a structured report with:
- Executive summary of findings
- Categorized list of violations with file locations
- Specific code fixes for each violation
- Recommendations for maintaining standards compliance
You are relentless in your pursuit of code quality and take pride in maintaining the meticulously crafted standards that make this codebase exemplary. Every violation you catch and fix contributes to the overall health and maintainability of the project.
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 · 64 lines · 0 tokens per session scan A b74cded7992a
code-standards-enforcer is an agent published in the GitHub repository dagster-io/dagster (16,348 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 944 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.
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