Universal quota tracking and enforcement patterns for rate-limited services. Provides threshold monitoring, estimation, and graceful degradation strategies. Triggers: quota tracking, rate limiting, usage limits, cost tracking, thresholds, resource management, API quotas, usage monitoring Use when: integrating rate-limited services, tracking API usage costs, implementing graceful degradation, monitoring resource consumption DO NOT use when: services have no rate limits or quota concerns. Consult this skill when managing quotas for external service integration.
/plugin marketplace add athola/claude-night-market/plugin install leyline@claude-night-marketThis skill inherits all available tools. When active, it can use any tool Claude has access to.
modules/estimation-patterns.mdmodules/threshold-strategies.mdUniversal patterns for tracking and enforcing resource quotas across any rate-limited service. This skill provides the foundational infrastructure that other plugins can leverage for consistent quota handling.
Three-tier threshold system for proactive management:
| Level | Usage | Action |
|---|---|---|
| Healthy | <80% | Proceed normally |
| Warning | 80-95% | Alert, consider batching |
| Critical | >95% | Defer non-urgent, use fallbacks |
@dataclass
class QuotaConfig:
requests_per_minute: int = 60
requests_per_day: int = 1000
tokens_per_minute: int = 100000
tokens_per_day: int = 1000000
from leyline.quota_tracker import QuotaTracker
tracker = QuotaTracker(service="my-service")
status, warnings = tracker.get_quota_status()
if status == "CRITICAL":
# Defer or use fallback
pass
tracker.record_request(
tokens=estimated_tokens,
success=True,
duration=elapsed_seconds
)
can_proceed, issues = tracker.can_handle_task(estimated_tokens)
if not can_proceed:
print(f"Quota issues: {issues}")
Other plugins reference this skill:
# In your skill's frontmatter
dependencies: [leyline:quota-management]
Then use the shared patterns:
modules/threshold-strategies.md for degradation patternsmodules/estimation-patterns.md for token/cost estimation