Test on the real agent
The lab runs the same prompt through a synced production agent twice, once with its normal system prompt and once with the skill, on that agent’s own model. No simulations.
A skill is a block of instructions an agent loads at runtime. Most teams write one, paste it into a prompt and hope it helps. Nobody can say whether a given skill made a given agent better or worse.
With hundreds of agents in production, that guesswork compounds. A bad skill quietly degrades every agent it is assigned to, and nothing flags it.
The lab runs the same prompt through a synced production agent twice, once with its normal system prompt and once with the skill, on that agent’s own model. No simulations.
If a skill loses, a frontier model rewrites it into a new version from both outputs and the judge’s reasoning. Then it gets tested again.
Agents pull skills at prompt-fetch time. If the service is down they fall back to their base prompts instead of breaking. An outage here must never take down the fleet.
A suggestions engine proposes new skills from real usage. Any agent or skill ID it hallucinates is dropped by schema validation before it reaches the screen.
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