Unverified50% confidenceTradeoffExact time
It is not advisable to install too many skills, as having too many can lead to erroneous triggering that affects results; greater emphasis should be placed on distilling one's own usage experience into dedicated skills.
1
Sources
50%
Confidence
Long-term
Relevance
9/20/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe quality of a Skill is highly dependent on the rigor of its initial creation; if the initial settings are not clear enough, subsequent reuse may amplify errors79% similarUnverifiedSkill分为能力型Skill和偏好型Skill，能力型随模型变强会被淘汰，偏好型更持久需要用评估保护防止性能倒退78% similarUnverifiedSkill的正确使用方式是渐进式优化：使用→发现问题→加规则→再测试，而非追求一次完美77% similarUnverified通用性与专业性之间存在张力，专注单一任务的工具通常能达到更高精度和可控性，而通用系统面临每个环节够用但不够好的风险77% similarUnverifiedWhen a skill description is so detailed that it contains all the information needed to complete a task, the model will skip the skill-calling step.76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/936560API
curl https://kongchang.com/api/v1/knowledge/claims/936560MCP
get_claim(id=936560)