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.
npx agentmods add rules/technickai/ai-coding-config/typescript-coding-standardsgit clone --depth 1 https://github.com/TechNickAI/ai-coding-configWhat 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 | $0.01399 | $0.01399 |
| Opus 5 | $0.00700 | $0.00700 |
| Sonnet 5 | $0.00280 | $0.00280 |
| Haiku 4.5 | $0.00140 | $0.00140 |
Grade A, and why
typescript-coding-standards 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 yesterday.
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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TypeScript Coding Standards
Write production TypeScript code that is maintainable, observable, and follows modern patterns.
Structured Logging with Pino
Use pino logger for structured JSON logging. The logger auto-silences during tests and provides pretty formatting in development.
import { logger } from "@/lib/logger";
logger.info({ userId, email, service }, "User authenticated");
logger.error({ error, userEmail, action }, "Failed to execute action");
logger.warn({ retryCount, url }, "HTTP request retry");
logger.debug({ requestId, method }, "Processing request");
For client-side code, use the client logger which works in browsers:
import { logger } from "@/lib/client-logger";
logger.error({ error, context }, "Failed to copy text");
logger.info({ service }, "Connection successful");
The first argument is ALWAYS a context object (even if empty {}), the second is ALWAYS
the message string. Structure your context with meaningful keys that help debug issues.
Use emojis in messages when they add clarity and make logs more scannable.
Never use console.log, console.error, console.warn, or console.info directly.
Always use the appropriate logger for consistent structured logging.
Error Monitoring with Sentry
Sentry captures errors with rich context for observability. Add context at error boundaries to make debugging easier.
Capture exceptions with tags and extra data:
import * as Sentry from "@sentry/nextjs";
try {
await riskyOperation();
} catch (error) {
logger.error({ error, userId }, "Operation failed");
Sentry.captureException(error, {
tags: { component: "api", action: "send_email" },
extra: { userId, messageId, attemptCount },
});
throw error;
}
Add breadcrumbs for important state changes:
Sentry.addBreadcrumb({
category: "http.retry",
message: `Retrying ${method} ${url}`,
level: "warning",
data: { url, retryCount },
});
Create spans for performance monitoring:
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.
- yesterday First seen · 232 lines · 1,399 tokens per session scan A 5a328895d5cd
typescript-coding-standards is a cursor rule published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 2mo ago), licensed MIT. It adds 1,399 tokens to every session, about $0.0070 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 2026-08-30.
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