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Analyze and improve the improvement process.
Passed 13 of 17 test cases with the skill, and 7 of 17 without it.
and 6 more in the full report
9 fixed8 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
When writing general insights about skill modification success patterns into ImprovementMemory, which identifier string must be assigned to skill_ref?
| Checked | Without skill | With skill |
|---|---|---|
| The response uses '_meta' as the skill_ref parameter. | Missed | Passed |
The instructions your model reads when the skill loads.
Analyze the effectiveness of past skill improvements and refine the improvement process itself. This is the core innovation from the Hyperagents paper: not just improving skills, but improving HOW skills are improved.
This skill should be invoked automatically when:
Regression detected: The homeostatic monitor finds
a skill's evaluation window ended in
pending_rollback_review status. The improvement
made things worse, and we need to understand why.
Low effectiveness rate: When
ImprovementMemory.get_effective_strategies() vs
get_failed_strategies() shows effectiveness below
50%, the improvement process itself needs refinement.
Degradation despite improvements: When
PerformanceTracker.get_improvement_trend() returns
negative for a skill that was recently improved.
Periodic check: After every 10 improvement cycles (tracked via outcome count in ImprovementMemory).
The homeostatic monitor emits
"improvement_triggered": true when a skill crosses the
flag threshold. At that point, before dispatching the
skill-improver, check if metacognitive analysis is
warranted:
from abstract.improvement_memory import ImprovementMemory
test cases passed, vs 7 / 17 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.