Turns platform reliability and multi-vendor coding-agent guidance into a checklist before standardizing on a single AI coding stack. Teams inventory host-platform SLAs (for example GitHub availability incidents documented on githubstatus.com), compare primary and backup agents (GitHub Copilot, Cursor, Claude Code, Codex, etc.), verify observability hooks through Braintrust or similar tracing, and rehearse workflows when the code host or agent API is degraded. The skill cites public status pages and vendor billing changes—such as usage-based Copilot pricing announced on github.blog—so procurement and engineering sign off with eyes open about downtime, leadership churn, and feature parity gaps reported in trade media.
Use cases
- Enterprise platform team picking a default agent while GitHub reports multi-hour incidents
- Evaluating whether to keep Copilot after usage-based billing shifts costs
- Disaster-recovery planning when both SCM and agent APIs must fail independently
- Quarterly review after competitors gain share in AI-assisted coding surveys
- Onboarding thousands of developers who cannot afford daily host outages
Key features
- Pull 90-day incident history from the SCM status page and classify impact on CI, PR merges, and agent features.
- Document primary and backup agent stacks with feature parity notes (inline edits, agent mode, MCP support).
- Attach tracing or eval baselines (Braintrust experiments, internal benchmarks) for critical repos before migration.
- Run a joint drill: simulate SCM outage while agents still work locally, then agent outage while SCM is healthy.
- Review licensing and usage caps (Copilot individual plan pauses, usage-based pricing calculators) with finance.
- Publish vendor readiness memo: chosen stack, failover order, open gaps, and retest schedule.
When to Use This Skill
- Before mandating one AI coding vendor across a large engineering org
- After repeated host-platform outages or security incidents affecting code storage
- When finance asks how agent usage billing changes affect team budgets
Expected Output
Signed agentic-coding readiness memo with SCM/agent failover paths, incident evidence, and billing assumptions.
Frequently Asked Questions
- Is this only about GitHub Copilot?
- No—the checklist is vendor-neutral; Copilot and GitHub appear when teams anchor on that stack.
- Do we need Braintrust?
- Not required, but tracing/eval baselines help prove agent quality during vendor switches.
- Can we skip failover drills?
- Status-page reviews help, but dual-outage tabletop plus a technical drill are needed for credible readiness.
Related
Related
3 Indexed items
Multi-region LLM provider readiness review
Converts export-control and multi-vendor routing guidance into a planning checklist for teams that cannot assume a single geography or chip supplier will stay available. Practitioners document primary and contingency model routes (including gateways such as Helicone or LiteLLM Router configs), quantify revenue or latency exposure if a region is blocked, and set investor/customer messaging when leadership advises to "expect nothing" from a market—as publicly reported when semiconductor vendors discuss China licensing uncertainty. The skill cross-checks legal/compliance sign-off, drills failover to alternate regions or domestic stacks, and records evidence before production launches tied to geopolitically sensitive deployments.
LiteLLM Router fallback readiness review
Translates LiteLLM routing documentation into a pre-flight checklist before promoting multi-deployment LLM routes to production. Teams verify Router configuration covers primary and fallback model lists, retry policies, and load-balancing strategy documented at docs.litellm.ai/docs/routing, confirm proxy virtual keys and spend limits if traffic flows through LiteLLM Proxy, and rehearse provider outage drills using OpenAI-mapped exceptions (AuthenticationError, RateLimitError, APIError). The skill also points operators to enable `store_model_in_db` when MCP tools must persist alongside router definitions and to validate MCP server names comply with SEP-986 guidance referenced in LiteLLM v1.80.18 release notes.
Example SLO document authoring
Operationalizes Appendix A from Google’s SRE workbook by translating the illustrative “Example Game Service” SLO dossier into a checklist teams can mimic: articulate the user-facing workload, nominate rolling measurement windows (the appendix uses four weeks), pair each subsystem with tightly defined SLIs (availability from load balancers excluding 5xx, latency percentile gates, freshness for derived tables, correctness via probers, completeness for pipelines), cite explicit numerator/denominator language, rationalize rounding policies, quantify per-objective error budgets, and cite the sibling error budget policy for enforcement.