Borrowing it
Nothing to install: this file belongs to mohamedzhioua/agent-done-or-not. 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/mohamedzhioua/agent-done-or-not/main/.cursorrulesgit clone --depth 1 https://github.com/mohamedzhioua/agent-done-or-notWrote 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/rules/mohamedzhioua/agent-done-or-not/cursorrules)<a href="https://agentmods.dev/rules/mohamedzhioua/agent-done-or-not/cursorrules"><img src="https://agentmods.dev/badge/rules/mohamedzhioua/agent-done-or-not/cursorrules/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/rules/mohamedzhioua/agent-done-or-not/cursorrules"><img src="https://agentmods.dev/badge/rules/mohamedzhioua/agent-done-or-not/cursorrules.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 | $0.00140 | $0.00140 |
| Opus 5.5 | $0.00056 | $0.00056 |
| Sonnet 5.5 | $0.00028 | $0.00028 |
| Haiku 4.5 | $0.00014 | $0.00014 |
Grade A, and why
cursorrules 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- windsurfrules — 92% identical, 3 lines differ
What it actually says
Proof-of-Done Rule (agent-done-or-not)
Before claiming a task is complete, you MUST verify it through the proof gate. Declaring "done" without a passing receipt is not allowed.
- Run the command that proves the work through the gate:
bash done-gate.sh capture --label check -- <your verifying command>It records the command, exit code, and a SHA-256 of the output, and exits with the command's own code — so a failing check fails here. - Only report completion after a PASSING receipt.
- If the check fails, fix the code and capture again. Never report success on a red check.
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 · 13 lines · 140 tokens per session scan A 159b7110ed6f
cursorrules is a cursor rule published in the GitHub repository mohamedzhioua/agent-done-or-not (6 stars, last pushed 15d ago), licensed MIT. It adds 140 tokens to every session, about $0.0006 per session on Opus 5.5. 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 2026-08-31.
Other cursor rules, from other repositories
python-general-coding-standards
A set of general standards for Python projects, including type hints, input checking, code style, security, web requests, background work, and tests. It also covers FastAPI-specific tasks such as CORS and authentication.
test-case-to-katalon-studio
Convert Katalon True Platform/TestOps manual test cases into Katalon Studio automation inside a local Studio Test Project checkout. Use when you need to author or extend a .tc test case file and its paired Groovy script under Scripts/, keep test case variable GUIDs consistent with the .ts test suite bindings that read…
execute-test
Execute Katalon True Platform/TestOps tests when the input is an existing test case, manual test case list, test suite, suite collection, execution request, or "run with AI" instruction. Use when you need to create a manual test run, start Run with AI, poll AI session results, schedule automated suites, read…
test-maintenance
Maintain and evolve a Katalon True Platform/TestOps regression suite as the application changes. Use when you need to detect which tests broke or became flaky from stability and result history, diagnose whether a case needs repair vs regeneration, repair test assets (update, move, reorganize cases), refresh coverage…
test-plan
Plan Katalon True Platform/TestOps testing for a release, sprint, or feature. Use when you need to translate quality goals into scope, prioritize testing by requirement coverage and risk, decide what to test first, or build the executable plan structure (folders, suites, and sprint/release association) that stands in…
test-review
Review Katalon True Platform/TestOps test quality and coverage before tests enter the delivery pipeline. Use when you need to check whether a suite is ready to run, review requirement and configuration coverage, assess test-case quality and flakiness/stability, spot weak or unreliable cases, and produce a review…