Provide implementations of advanced data structures
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a5c-ai/babysitter18612026年9月17日 更新
免费在 GitHub 公开
continuous-learning
Pattern extraction, confidence-scored evaluation, skill creation, organization, versioning, and cross-project export pipeline.
中文简介正在准备中。当前显示原文说明。
查看安装方法包含的文件（2）
- SKILL.md2.0 KB
- README.md219 B
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
- Analyze code changes and implementation approaches
- Identify recurring patterns and conventions
- Extract architectural decisions with rationale
- Capture error resolution strategies
- Record tool usage patterns
- Assign initial confidence scores (0-100)
2. Pattern Evaluation
- Score generalizability (0-100): cross-project applicability
- Score reliability (0-100): validation frequency
- Score impact (0-100): outcome improvement
- Composite: generalizability * 0.3 + reliability * 0.4 + impact * 0.3
- Filter below confidence threshold (default: 75)
- Merge similar patterns
3. Skill Creation
- Convert high-confidence patterns to SKILL.md format
- Write clear instructions with phases
- Include when-to-use and when-not-to-use sections
- Add usage examples and agent references
- Follow kebab-case naming convention
4. Organization
- Categorize: language-specific, domain, business, meta
- Resolve naming conflicts
- Update indexes and manifests
- Create dependency graphs
5. Version and Export
- Assign semantic versions by maturity
- Create portable export bundles
- Include usage examples and test cases
- Generate import instructions
Strategic Compaction
- Analyze context token usage
- Identify low-value context for compression
- Archive completed phases to memory files
- Calculate token savings per suggestion
When to Use
- End of development sessions
- After significant code reviews
- After debugging sessions
- Periodically during long sessions
Agents Used
continuous-learning(custom agent for this skill)context-engineering(compaction analysis)
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