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Create specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern.
Passed 20 of 21 test cases with the skill, and 10 of 21 without it.
and 12 more in the full report
11 fixed10 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
Provide the exact directory relative path where domain expert command files (such as plan.md, self-improve.md, question.md) belong within a Claude repository setup for an 'auth' domain.
| Checked | Without skill | With skill |
|---|---|---|
| The directory path is .claude/commands/experts/auth/ | Missed | Passed |
The instructions your model reads when the skill loads.
Create specialized agent experts that learn and maintain domain knowledge through the Act-Learn-Reuse pattern.
"The massive problem with agents is this. Your agents forget. And that means your agents don't learn."
Generic agents execute and forget. Agent experts execute and learn by maintaining expertise files (mental models) that sync with the codebase.
┌─────────────────────────────────────────────────────────────┐
│ ACT-LEARN-REUSE CYCLE │
├─────────────────────────────────────────────────────────────┤
│ │
│ ACT ──────────► LEARN ──────────► REUSE │
│ │ │ │ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ Take useful Update expertise Read expertise │
│ action file via file FIRST on │
│ (build, fix) self-improve next execution │
│ prompt │
│ │
└─────────────────────────────────────────────────────────────┘
| Step | Action | Purpose |
|---|---|---|
| ACT | Take a useful action | Generate data to learn from (build, fix, answer) |
test cases passed, vs 10 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.