Tracks quotas, monitors thresholds, and degrades gracefully for rate-limited APIs. Use when integrating external services that impose rate or cost limits.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Tracks cumulative token budgets and enforces limits. For per-call logging, use usage-logging.
Patterns for tracking and enforcing resource quotas across rate-limited services. This skill provides the infrastructure that other plugins use 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 secondary services |
@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 secondary service
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 estimationNo reviews yet. Tell others what you thought.
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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
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Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
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Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
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