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/TechNickAI/ai-coding-configWrote 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/technickai/ai-coding-config/ruff-linting)<a href="https://agentmods.dev/rules/technickai/ai-coding-config/ruff-linting"><img src="https://agentmods.dev/badge/rules/technickai/ai-coding-config/ruff-linting/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/technickai/ai-coding-config/ruff-linting"><img src="https://agentmods.dev/badge/rules/technickai/ai-coding-config/ruff-linting.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 | $0.00004 | $0.00236 |
| Opus 5.5 | $0.00002 | $0.00094 |
| Sonnet 5.5 | $0.00001 | $0.00047 |
| Haiku 4.5 | $0.00000 | $0.00024 |
Grade A, and why
ruff-linting scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
process = subprocess.Popen(["pbcopy"], stdin=subprocess.PIPE) # ruff: ignore=S603,S607 - Needed for clipboard This is a copy
92% identical to ruff-linting — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Ruff Linting
We use ruff for linting. If there are lint errors, or if the user says "fix lint
errors":
- Run:
ruff check --fix --unsafe-fixes - Repeat until all errors are fixed
Using Targeted Ignores
For legitimate exceptions, use targeted ignores with specific rule codes and explanations:
# Include the rule code and why we're ignoring it
process = subprocess.Popen(["pbcopy"], stdin=subprocess.PIPE) # ruff: ignore=S603,S607 - Needed for clipboard
result = eval(user_formula) # ruff: ignore=S307 - Safe: formula validated by parser
api_key = os.getenv("SECRET_KEY") # ruff: ignore=S105 - Environment variable, not hardcoded
Use # ruff: ignore=RULE_CODE format. Multiple rules: # ruff: ignore=S603,S607
When we fix all errors, celebrate! Clean code feels good. 🌟
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 · 32 lines · 4 tokens per session scan A 26e9bf11869e
ruff-linting is a cursor rule published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 27d ago), licensed MIT. It adds 4 tokens to every session and 236 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). It is 92% identical to ruff-linting, differing in 1 line, and is treated as a copy.
Other cursor rules, from other repositories
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Padrões DARE para APIs REST em Python + FastAPI + Pydantic v2 + SQLAlchemy 2.0 async + alembic. Routers, dependency injection, schemas Pydantic, OAuth2+JWT, slowapi rate limit, pytest+httpx, OpenAPI auto-gerado.
cursorrules
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django
Django: models, views, ORM best practices.
python
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pydantic
Pydantic: BaseModel, validators, Field, Settings.