Results for “budgets”

Diagnoses and prevents mobile performance regressions on every stack — Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers startup time, rendering and jank, memory, battery and background work, app size, the profiler and benchmark to use on each platform, and regression detection in CI. Use when an app is slow, janky, heavy or draining the battery, when setting performance budgets, or when a release must not regress.

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Define and implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service performance.

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Axis-by-axis interview methodology for establishing engineering performance budgets during architecture PRD compilation. Use when conducting the Step 10.5 performance budget interview in /create-prd-compile.

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Use when working with Cloudhealth — cloudHealth by VMware multi-cloud cost management and governance. Covers cost reporting, rightsizing recommendations, governance policies, budget tracking, reserved instance management, and compliance auditing. Use when analyzing cloud spend across providers, enforcing tagging policies, or tracking budgets.

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Civic tech — transparence, open data, participation citoyenne, budgets participatifs

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Implement, review, or improve widgets, Live Activities, and controls using WidgetKit and ActivityKit. Use when building home screen, Lock Screen, or StandBy widgets with timeline providers; when creating interactive widgets with Button/Toggle and AppIntent actions; when adding Live Activities with Dynamic Island layouts (compact, minimal, expanded); when building Control Center widgets with ControlWidgetButton/ControlWidgetToggle; when configuring widget families, refresh budgets, deep links, push-based reloads, or Liquid Glass rendering; or when setting up widget extensions, App Groups, and entitlements.

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Validate and analyze budgets for SCF Build Award submissions. Use when reviewing whether a submission's budget is proportional, justified, and well-structured based on patterns from 215 funded Build Awards.

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Claude Managed Agents (platform.claude.com) リファレンス。Agents / Sessions / Environments API, SSE event streaming, event deltas, session threads, session budgets, multiagent orchestration (subagents, dynamic workflows, workflow runs, advisor), deployments (cron), dreams, vaults, memory stores, self-hosted sandboxes, webhooks, web search / fetch ドメイン制限, secure deployment, permission policies, Agent Skills, MCP connector, user profiles。

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Plan database migrations before executing them: risk assessment, dependency mapping, multi-service sequencing, rollback budgets, and stakeholder communication plans. Use when deciding HOW to migrate, not when writing the actual DDL. NOT for: writing migration SQL (use database-migration-manager), data ETL pipelines (use data-migration-specialist), query optimization, or ORM configuration.

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Treat tokens as a coding swarm's cost-of-goods-sold and legibility engine while accounting for finite subscription allowance when per-call price is unknown. Covers provider windows, remaining-usage evidence, burn forecasts, preemptive checkpoint/model switching, per-agent budgets, compaction, shared digests, context degradation, and spend metering. Activate on: "token budget", "context budget", "compaction strategy", "briefing as compression", "context rot", "summarization collapse", "COGS for agents", "subscription usage remaining", "five-hour or weekly limit", "burn forecast", "model switch before limit", or "/context-economics-for-agent-swarms". NOT for: memory architecture (use always-on-agent-architecture), single-prompt wording (use prompt-engineer), mechanism-design proofs (use nisan-et-al-2007-algorithmic-game-theory), physical containment (use sandboxed-adversarial-test-harness), or production rebodiment and fencing (use agent-resurrection-and-body-continuity).

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Building resilient distributed systems with circuit breakers, retries with full-jitter exponential backoff, retry budgets (per-request 3-attempt + per-client 10% ratio per Google SRE), deadline propagation, and the cascading-failure math (4 layers × 3 retries = 64x amplification). Grounded in Resilience4j, Microsoft Cloud Patterns, AWS Architecture Blog (Marc Brooker), and Google SRE Book. NOT for server-side rate limiting, bulkhead isolation, backpressure protocols, or database-specific retry.

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Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget. Downgrades models, skips optional nodes, or stops early when cost exceeds thresholds. Use when managing execution budgets, analyzing cost breakdowns, or optimizing model routing for cost. Activate on "cost budget", "too expensive", "reduce cost", "cost optimization", "model downgrade", "budget exceeded". NOT for LLM model selection logic (use llm-router), pricing comparisons across providers, or billing/invoicing.

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Use when adding or updating Spark AI provider models or their compact and preflight metrics, especially baidu-oneapi, Grok, Claude, GPT, DeepSeek, OneAPI, context windows, output budgets, prices, thinking maps, or transports.

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Google SRE (Site Reliability Engineering) practices distilled from the SRE book series and real Google Brain/Meta/ByteDance production experience. Covers SLI/SLO/Error Budget design, incident response, capacity planning, toil automation, monitoring golden signals, and chaos engineering principles. USE WHEN: designing reliability targets, setting up monitoring/alerting, planning capacity, reducing operational load, building incident response runbooks, implementing chaos engineering, defining error budgets, or establishing reliability culture that balances velocity with stability.

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Blockchain USDC payments via APay — pay services, manage budgets, open streaming channels, and handle x402 protocol.

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Build and harden high-performance APIs with strong security controls, endpoint budgets, rate limiting, and resilience patterns. Use when tasks involve API latency/throughput tuning, abuse resistance, authz hardening, or API release gates.

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Use when adding or updating Spark AI provider models or their compact and preflight metrics, especially baidu-oneapi, Grok, Claude, GPT, DeepSeek, OneAPI, context windows, output budgets, prices, thinking maps, or transports.

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