BC self-evolving-ai
自我进化AI是一个让智能体从每次交互中持续学习并自主改进的记忆与进化系统。针对传统自改进代理"记不住、用不上、成本高"三大痛点,构建了智能分层记忆、自动痛点检测、模式复发追踪和按需加载四大核心能力。 核心能力包括:经验/错误/特性请求三类结构化记忆;基于语义的自动分类与优先级评估;跨会话模式复发检测与晋升机制;分层文档加载策略(快速入门→标准用法→高级配置);一键晋升到项目记忆与可复用技能。 适用场景:长期运行的AI代理项目、多人协作的代码仓库、需要积累领域知识的研发团队、希望减少重复犯错的独立开发者、需要将隐性经验转化为显性规则的技术团队。 差异化亮点:相比原始版本,新增智能痛点检测触发器(自动识别纠正/知识缺口/最佳实践)、模式复发追踪(Recurrence-Count跨任务统计+30天窗口晋升规则)、按需加载机制(主文档<150行,详情分层引用)、token成本预估与优化建议、故障排查决策树、分层评审清单。 触发关键词:自我进化、经验捕获、错误学习、模式追踪、记忆晋升、持续改进、self-evolving、learnings、corrections、pattern-detection
自我进化AI是一个让智能体从每次交互中持续学习并自主改进的记忆与进化系统。针对传统自改进代理"记不住、用不上、成本高"三大痛点,构建了智能分层记忆、自动痛点检测、模式复发追踪和按需加载四大核心能力。…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "tools"
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 93 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2334 tokens
- 100Running it twice. No mutating operations
- low 18 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 506: enough signal without eating the budget
- +4Structure: 47 headings
- +3Step-by-step instructions: 93 items
- +4Has examples (14 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.