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What this talk covers
Matt Pocock presents a 'skill checklist' framework to help developers escape 'skill hell' by systematically evaluating and improving agent skills, covering trigger, structure, steering, and pruning. He explains the trade-offs between user-invoked and model-invoked skills: model-invoked increase context load and unpredictability, user-invoked increase pilot cognitive load. Structure divides skills into steps and reference, with branching material hidden behind context pointers to minimize skill.md. Steering uses 'leading words' like 'vertical slice' that appear in reasoning traces; to boost leg work, he splits skills so agents only see one step at a time. Pruning removes duplication, sediment, and no-ops that don't affect behavior. The framework is in the 'writing-great-skills' skill in his repository.
This overview is derived from the transcript and has not been independently fact-checked by AI Engineer.
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Key moments
Skill hell is the inability to distinguish good AI agent skills from bad ones, preventing effective use and organization adoption.
Adding a model-invoked skill to an agent increases context load by permanently inserting its description into the context window.
Skills are built from two units: steps (the procedure) and reference (supporting information), simplifying skill design.
Leading words pack dense meaning into short phrases, guiding agent reasoning traces when repeated in the skill and thinking tokens.
No-ops are skill instructions that appear useful but do not change agent behavior, such as asking for a long commit message when it's already the default.
The 'writing great skills' skill encodes the entire checklist framework, available on Matt Pocock's GitHub for immediate use.
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