miro-cost-tuning

v2026.09.24

Diagnose Miro API credit demand and implement repository-side traffic controls that preserve freshness, completeness, and user experience. Use when right-sizing polling or synchronization. Trigger with "reduce Miro API usage".

GitHub
Install command
npx skhub add jeremylongshore/miro-cost-tuning
Markdown
SKILL.md

Miro Credit-Efficiency Planning

Overview

Treat API credits as capacity, not currency. Attribute demand by operation and caller, then remove waste without inventing a dollar price; use the evidence produced here to make the next decision explicit and reviewable.

Prerequisites

  • Request and rate-header telemetry by operation
  • Documented endpoint credit levels
  • Freshness, completeness, and priority requirements

Tool Discipline

Use Read, Glob, and Grep to inspect the repository, configuration names, adapters, tests, and evidence. Use WebFetch only for current official Miro documentation. Use Write or Edit after confirming the requested mode, target environment, tenant, board, and approval boundary. These declared tools do not call authenticated Miro APIs or deployment CLIs; implement client, configuration, and test changes, then return exact operator commands or an approval-gated handoff for live execution.

Current Contract

  • Miro documents credits and request capacity, not a per-credit billing price.
  • REST levels currently cost 50, 100, 500, or 2,000 credits per call.
  • Bulk creation remains charged Level 2 per item, so batching reduces round trips but not necessarily credit total.
  • Retired REST webhooks are not an available zero-polling substitute.

Authentication

For REST work, use OAuth 2.0 Authorization Code with the narrowest Miro scopes. Bind each encrypted token record to its user, application, authorized team, and granted scopes. Never print access tokens, refresh tokens, client secrets, authorization codes, or board content.

Instructions

  1. Attribute calls and credits to tenants, features, endpoints, schedules, retries, and operators.
  2. Separate required freshness from inherited polling frequency and duplicate consumers.
  3. Model options: narrower scope, cursor checkpoints, deduplication, cache, coalescing, or reduced cadence.
  4. Estimate credit reduction and quantify freshness, completeness, memory, and operational tradeoffs.
  5. Test the selected policy against representative change bursts and full-reconciliation recovery.
  6. Roll out with per-tenant fairness and alerts for credits, staleness, backlog, and missed-state indicators.

Approval Boundaries

Do not represent credits as direct monetary savings or weaken contractual freshness/completeness without product and service-owner approval. Pause when the responsible owner or exact target is uncertain.

Output

Return baseline credits, waste sources, options, selected policy, expected reduction, SLO tradeoffs, test results, and rollback threshold. State what was not inspected or changed so the receipt cannot overclaim coverage.

Error Handling

ConditionResponse
No operation-level telemetryInstrument before claiming savings.
Savings depend on retired webhooksReject that option.
Cache invalidation is unprovedLimit cache scope/TTL and retain reconciliation.
One tenant monopolizes budgetEnforce per-tenant queues and priority reserves.

Examples

The example is a redacted operator receipt; identifiers are hashes or bounded labels, not board content or credentials.

baseline=74000cr/min; duplicates=18%; selected=cursor+dedupe; forecast=51000cr/min; freshness=5m; completeness=daily-reconcile

Resources

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Version
Latest version metadata

Version

v2026.09.24

Published

Sep 24, 2026

Category

Uncategorized

License

MIT

Source path

skills/.curated/miro-cost-tuning

Default branch

main

Latest commit

e5a6c3b

Tree SHA

c2dc8e8