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.
git clone --depth 1 https://github.com/ivangrynenko/cursorrulesWrote 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/ivangrynenko/cursorrules/gh-issue-resolve)<a href="https://agentmods.dev/commands/ivangrynenko/cursorrules/gh-issue-resolve"><img src="https://agentmods.dev/badge/commands/ivangrynenko/cursorrules/gh-issue-resolve/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/commands/ivangrynenko/cursorrules/gh-issue-resolve"><img src="https://agentmods.dev/badge/commands/ivangrynenko/cursorrules/gh-issue-resolve.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 | $0.00670 |
| Opus 5.5 | $0.00000 | $0.00268 |
| Sonnet 5.5 | $0.00000 | $0.00134 |
| Haiku 4.5 | $0.00000 | $0.00067 |
Grade A, and why
gh-issue-resolve 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 1mo 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Custom Command: GitHub Issue Resolve
Command Usage
/gh-issue-resolve <issue-url> [context]
Treat the first argument as the GitHub issue URL (or number). Everything after that is optional analyst context to guide the resolution. If the URL is missing, pause and ask the user to provide it before continuing.
Please analyse and fix the GitHub issue referenced in the user input. The problem description provided after /gh-issue-resolve becomes your working context. If you cannot access the issue or the instructions are incomplete, stop and request clarification from the user before proceeding.
Follow these steps:
1. PLAN
- Use 'gh issue view' to get the issue details
- Think and understand the problem described in the issue
- Ask clarifying questions if necessary
- Understand the prior art for this issue
- Search the codebase for relevant files
- Think harder about how to break the issue down into a series of small, manageable tasks.
- Prepare your plan
- include the issue name
- include a link to the issue.
2. WRITE TEST
- Create a new Git branch for the issue.
- Identify the testing framework within the project.
- Begin by writing tests in the identified framework that will satisfy the ticket acceptance criteria and overall task.
- Write behat and (or) Unit and (or) Postman tests to describe the expected behavior of your code. Tests will fail which is okay.
- Aim at minimum 80% of test coverage
3. WRITE CODE
- Think and solve the issue in small, manageable steps, according to your plan.
- Commit your changes after each step.
- When coding FrontEnd, use Playwright MCP to validate your work.
- Document your changes in project documentation (README.md and /docs/ folder)
- Update CHANGELOG.md file with listing the changes you're making, keep this file up to date and always validate to remove information that does not apply any longer.
- Check for the linting tools available in the project's AGENTS.md file.
- Use either playwright via MCP, or Unit, or behat to test the changes if you have made changes to the UI. If you are in a Drupal project, login via playwright browser, first execute
ahoy logincommand and grab the one-time login URL as Drupal user #1. Then visit that URL to instantly login. - Run the full test suite to ensure you haven't broken anything
- If the tests are failing, fix them, or the functionality.
- Ensure that all tests are passing before moving on to the next step
- If testing automation is present as a GitHub Actions workflow, test it locally using local Docker. Example:
act --container-architecture linux/amd64 -j test --verbose
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.
- 1mo ago First seen · 53 lines · 0 tokens per session scan A b34bc5c626a4
gh-issue-resolve is a command published in the GitHub repository ivangrynenko/cursorrules (88 stars, last pushed 11mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 670 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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