Unverified50% confidenceCautionExact time
Do not assume that Skills always consume fewer tokens, and do not assume that MCP always produces fresher data; you should compare correctness, context usage, latency, and recovery capability on representative tasks.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIt 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.69% 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68% similarUnverifiedIn mission-critical tasks, a model's predictability and stability are sometimes more important than raw capability.68% 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68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1024457API
curl https://kongchang.com/api/v1/knowledge/claims/1024457MCP
get_claim(id=1024457)