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/extsoft/elegant-gitWrote 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/extsoft/elegant-git/practices)<a href="https://agentmods.dev/rules/extsoft/elegant-git/practices"><img src="https://agentmods.dev/badge/rules/extsoft/elegant-git/practices.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 | $0.00152 | $0.00152 |
| Opus 5 | $0.00076 | $0.00076 |
| Sonnet 5 | $0.00030 | $0.00030 |
| Haiku 4.5 | $0.00015 | $0.00015 |
Grade A, and why
practices 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 3d 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
Practices
- State assumptions; surface tradeoffs; ask when unclear; minimum code; surgical edits only.
- TDD when behavior changes: failing test → pass → refactor.
- YAGNI / DRY / KISS; same domain terms in code and docs; remove orphans you created only.
- No secrets/tokens/PII in source or logs; validate inputs; least privilege.
- Docs-as-code: update README, man pages, or
docs/with user-visible behavior. - Public contract changes: update the deprecation register in the same change.
- Self-review: minimal diff, error paths considered, acceptance criteria covered.
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.
- 3d ago First seen · 15 lines · 152 tokens per session scan A 3795cbaa23d2
practices is a cursor rule published in the GitHub repository extsoft/elegant-git (47 stars, last pushed 5d ago), licensed MIT. It adds 152 tokens to every session, about $0.0008 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 2026-09-03.
Other cursor rules, from other repositories
tdd-workflow
TDD mandate - Red/Green/Refactor workflow, test-first default, escape clause for trivial changes.
manual-review.backend
A set of rules for verifying backend-only changes and changes that use different verification channels. It separates evidence the agent can collect from checks that require a person, a production authorization, or a business decision.
mcpnuke-tests
Test conventions for mcpnuke — enforces TDD workflow.
debug-issue
When the user reports a bug, an error, or unexpected behavior. Enforces four structured phases — reproduction, failing test, root cause isolation, fix and verify — to stop guess-and-check loops.
tdd
Test-driven development — red-green-refactor cycle.
test-driven-development
Your Role and Mission: You are an expert TDD Software Developer AI specializing in Python. Your primary function is to write Python code by strictly adhering to the Test-Driven Development (TDD) methodology using the pytest framework. Your goal is to produce high-quality, robust, maintainable, and well-documented…