gpt-5-5
GPT-5.5
Concept of the skill
What it is: GPT-5.5 is OpenAI's current GPT frontier model generation (~1M context). This skill is the routing decision: when a task's shape favors GPT-5.5's strengths enough to send it there instead of a Claude tier.
Mental model: Treat frontier models as specialists with overlapping but non-identical edges. GPT-5.5 and Claude Opus are within a couple of points on aggregate coding benchmarks, but they diverge by task type: GPT-5.5 leads on terminal/CLI workflows and precise tool-use; Claude Opus leads on architecture and complex multi-file resolution. Routing is matching the task's dominant axis to the model that owns that axis — not crowning one model "best."
Why it exists: "Just use the strongest model" is the wrong default — it ignores that strength is task-shaped, and it ignores cost. An explicit per-task routing rule captures the real per-benchmark splits so concrete-implementation and review work lands on GPT-5.5 while architecture work stays on Opus, instead of being decided by habit.
What it is NOT: It is not the Codex CLI (that is the harness that runs GPT — a separate decision), not the OpenAI SDK/API, and not a claim that GPT-5.5 beats Claude everywhere. It is a model-lane selector, not a leaderboard.
Adjacent concepts: the Codex CLI (the harness for GPT), the Claude tier skills (claude-opus, claude-sonnet, claude-haiku), gemini-pro (the third frontier lane), and skill-router (request-time dispatch among local skills, a different layer).
One-line analogy: Routing GPT-5.5 vs Opus is choosing a surgeon by procedure, not by overall reputation — the more decorated surgeon is still the wrong pick for the operation the other one specializes in.
Common misconception: That the higher SWE-bench Verified score makes GPT-5.5 the universal coding choice. The aggregate margin is within noise and benchmark-contamination caveats; on complex multi-file resolution (SWE-bench Pro) Claude Opus leads, so the verified-bench headline does not generalize to architecture-heavy work.