Golang Samber Hot

[
  {
    "id": 1,
    "name": "algorithm-selection-default",
    "description": "Tests whether the model recommends W-TinyLFU as default instead of LRU",
    "prompt": "I need an in-memory cache for my Go web API using samber/hot. It handles user profile lookups with mixed access patterns — some users are accessed frequently, others only occasionally. Set up a cache with 50k entries and 10-minute TTL.",
    "trap": "Without the skill, the model defaults to hot.LRU (the most well-known algorithm) instead of hot.WTinyLFU which handles mixed workloads better",
    "assertions": [
      {"id": "1.1", "text": "Uses hot.WTinyLFU as the eviction algorithm (not hot.LRU)"},
      {"id": "1.2", "text": "Uses hot.NewHotCache constructor with generic type parameters"},
      {"id": "1.3", "text": "Chains .WithTTL(10 * time.Minute) or equivalent"},
      {"id": "1.4", "text": "Chains .WithJanitor() in the builder (not calling cache.Janitor() separately after Build)"},
      {"id": "1.5", "text": "Calls defer cache.StopJanitor() after Build()"},
      {"id": "1.6", "text": "Calls .Build() to finalize the cache"}
    ]
  },
  {
    "id": 2,
    "name": "algorithm-selection-frequency",
    "description": "For stable frequency-dominated workloads, LFU beats W-TinyLFU despite W-TinyLFU being the general default",
    "prompt": "I'm building a DNS resolver cache in Go with samber/hot. Lookups follow a heavy power-law distribution — a small set of domains (google.com, cloudflare.com) are looked up millions of times, while most domains are rare. The popularity rankings are very stable over time.\n\nA teammate says: 'The docs say W-TinyLFU is the general-purpose default that handles mixed workloads. We should always start with hot.WTinyLFU to avoid bike-shedding — you can always tune later.' Is this good advice for this specific use case? Which algorithm should I actually use?",
    "trap": "The teammate's advice sounds pragmatic — 'start with the default, tune later' is usually good. But the skill teaches that for stable frequency-dominated workloads (where popularity rankings don't shift), hot.LFU is superior to W-TinyLFU. LFU's known weakness (stale popular items never evict) is irrelevant when rankings are stable. The model should override the default recommendation.",
    "assertions": [
      {"id": "2.1", "text": "Pushes back on the teammate — does NOT blindly apply hot.WTinyLFU when the workload is clearly stable-frequency-dominated"},
      {"id": "2.2", "text": "Recommends hot.LFU as the correct algorithm: stable power-law distribution maps exactly to LFU's strength (keeping the most frequently accessed items)"},
      {"id": "2.3", "text": "Explains why LFU's known weakness (stale popular items stuck in cache) does NOT apply here — popularity rankings are stable, so the stale-items problem doesn't manifest"},
      {"id": "2.4", "text": "Correctly positions W-TinyLFU as 'general-purpose for unknown/shifting patterns' while LFU is 'optimal for known stable frequency patterns'"}
    ]
  },
  {
    "id": 3,
    "name": "janitor-required",
    "description": "Tests whether the model correctly includes WithJanitor() — the most common mistake",
    "prompt": "Write a simple samber/hot cache in Go with a 5-minute TTL for caching API responses. Key is string, value is []byte.",
    "trap": "Without the skill, the model forgets WithJanitor() — expired entries stay in memory until algorithm eviction, silently serving stale data",
    "assertions": [
      {"id": "3.1", "text": "Includes .WithJanitor() in the builder chain"},
      {"id": "3.2", "text": "Includes defer cache.StopJanitor() for cleanup"},
      {"id": "3.3", "text": "Uses .WithTTL() for expiration"},
      {"id": "3.4", "text": "Does NOT call cache.Janitor() as a separate method after Build() — it should be chained in the builder"}
    ]
  },
  {
    "id": 4,
    "name": "missing-cache-panic-prevention",
    "description": "Tests whether the model correctly enables missing cache bef