Stop Writing Skills from Memory
Blog post from Paper Compute Company
Skills in AI engineering are becoming an essential part of the stack, yet many are developed from memory rather than evidence, leading to "lossy exports" similar to the issues with prompt libraries. The text highlights that skills should be based on patterns extracted from successful agent sessions to avoid the pitfalls of incomplete documentation and knowledge silos. Knowledge silos, exacerbated by AI tools, occur when problem-solving is isolated between agents and engineers, leading to lost institutional knowledge. Capturing and maintaining session histories can mitigate these silos by turning ephemeral interactions into durable records that can be shared and learned from by entire teams. Initiatives like those at Code Climate, which focus on capturing key decisions and documenting work, have shown significant productivity improvements. The text argues for the importance of treating captured sessions as valuable artifacts that form the basis of reusable skills, advocating for a systematic approach that begins with capturing evidence rather than relying on memory.
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