基于 SOC 职业分类
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Use when auditing a paid ad account for incremental contribution, wasted spend, or measurement integrity before scaling; runs a typed 20-item ROAS profile with verified vetoes and a SHIP/FIX/BLOCK/UNDECIDED gate on own exported data. Not for campaign structure design — use campaign-architect; not for creative production — use ad-creative-builder. 付费广告账户审计/ROAS评分
Use when the user asks to "write ad copy", "generate RSA headlines", or "build ad creative at volume"; produces ad units — RSA headlines/descriptions, hooks, and an angle matrix — message-matched to the destination landing page. Not for scoring an ad account — use ad-account-auditor; not for the post-click page — use landing-optimizer; not for organic articles — use content-writer. 广告创意/广告文案/RSA标题
Use when the user asks to "design an A/B test", "set up a creative/landing test", "run an incrementality test", or "is this result statistically and practically material?"; produces a hypothesis, variant matrix, sample-size/duration/power plan, and a documented effect/uncertainty read from own exported results. It applies only a precommitted owner-approved action rule; the statistical helper never chooses a business action. Not for producing variants — use ad-creative-builder; not for reading back one shipped change — use paid-measurement-loop. 广告AB测试设计/实验设计/显著性判定/增效测试
| name | logging-best-practices |
| description | Logging best practices focused on wide events (canonical log lines) for powerful debugging and analytics |
| license | MIT |
| metadata | {"author":"boristane","version":"1.0.0"} |
Version: 1.0.0
This skill provides guidelines for implementing effective logging in applications. It focuses on wide events (also called canonical log lines) - a pattern where you emit a single, context-rich event per request per service, enabling powerful debugging and analytics.
Apply these guidelines when:
Emit one context-rich event per request per service. Instead of scattering log lines throughout your handler, consolidate everything into a single structured event emitted at request completion.
const wideEvent: Record<string, unknown> = {
method: 'POST',
path: '/checkout',
requestId: c.get('requestId'),
timestamp: new Date().toISOString(),
};
try {
const user = await getUser(c.get('userId'));
wideEvent.user = { id: user.id, subscription: user.subscription };
const cart = await getCart(user.id);
wideEvent.cart = { total_cents: cart.total, item_count: cart.items.length };
wideEvent.status_code = 200;
wideEvent.outcome = 'success';
return c.json({ success: true });
} catch (error) {
wideEvent.status_code = 500;
wideEvent.outcome = 'error';
wideEvent.error = { message: error.message, type: error.name };
throw error;
} finally {
wideEvent.duration_ms = Date.now() - startTime;
logger.info(wideEvent);
}
Include fields with high cardinality (user IDs, request IDs - millions of unique values) and high dimensionality (many fields per event). This enables querying by specific users and answering questions you haven't anticipated yet.
Always include business context: user subscription tier, cart value, feature flags, account age. The goal is to know "a premium customer couldn't complete a $2,499 purchase" not just "checkout failed."
Include environment and deployment info in every event: commit hash, service version, region, instance ID. This enables correlating issues with deployments and identifying region-specific problems.
Use one logger instance configured at startup and import it everywhere. This ensures consistent formatting and automatic environment context.
Use middleware to handle wide event infrastructure (timing, status, environment, emission). Handlers should only add business context.
info and errorconsole.log('something happened') instead of structured datarules/wide-events.md)rules/context.md)rules/structure.md)rules/pitfalls.md)References: