ai-coding-model-guidance

Calibrate the OpenAI Codex reasoning-level dial before recommending a model upgrade. Maps task type, failure mode, and budget to the right reasoning effort level — ensuring developers exhaust the reasoning dial on the current model before paying for a bigger SKU. Domain-specific to the Codex reasoning API.

Scope an autonomous AI coding agent task so it is well-bounded, recoverable, and matched to the right model tier before it runs. Reach for this skill before any long, unsupervised, or multi-step agentic run — a poorly scoped task on a frontier model is both expensive and hard to debug.

Estimate a coding task's context demand and match it to a model tier whose window is sufficient, without quoting specific token counts that churn monthly. Reach for this skill when the task involves large codebases, long conversation histories, or multi-file agentic runs where context overflow would produce silent truncation errors.

Audit a developer's GitHub Copilot configuration across all surfaces (completions, chat IDE, coding agent, cloud agent, mobile) to identify model gaps, plan mismatches, and org-policy constraints. Reach for this skill before recommending a Copilot model — surface and plan scope the answer, not just the model name.

Check whether a Grok model ID in active use has been retired or silently redirected, with special attention to billing consequences. Reach for this skill before any Grok recommendation and whenever a developer mentions a specific Grok model ID — retirement redirects can incur unexpected charges at the new model's pricing.

Check the ai-coding-model-guidance knowledge bank for stale entries and produce a prioritized refresh list. Reach for this skill when the knowledge bank's retrieval date is more than 4 weeks old, when a consumer reports a model discrepancy, or on the monthly researcher-reminder cadence.

Compare model options across GitHub Copilot, OpenAI Codex, and xAI Grok for a single task when the developer has not committed to one ecosystem. Produces a structured side-by-side that surfaces availability, tier mapping, and key tradeoffs without naming specific volatile numbers.

Audit an organization's AI coding tool model-access policies across GitHub Copilot Business/Enterprise, OpenAI org-level controls, and xAI API governance. Reach for this skill when an enterprise team reports unexpected model access, when a compliance review requires documenting which models the org has allowed or blocked, or before rolling out a new model to a large org.

Hard gate when a coding agent/surface returns quota, rate-limit, weekly/monthly cap, spend-limit, or tokens-exhausted. Never silent-fail or blind-retry — try param/effort/scope levers before vendor failover, then traverse the vendor-neutral tier tree before naming a substitute SKU. Deep layer: coding-agent-levers-playbook.