用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/majiayu000/claude-skill-registry --skill relevant-learnings命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
LLM token logprobs and calibration. Per-decision confidence, ECE, Brier, reliability diagrams, low-confidence triage.
Analyze LLM token logprobs and calibration. Use for per-decision confidence, ECE, Brier scores, reliability diagrams, and low-confidence triage.
回顾最近 N 天的 Claude Code 使用记录——扫描原始会话数据,按主题分组汇总"我都做了什么",并从个人操作系统视角输出模式、风险与增删建议。当用户说 /recap、"看看我这几天做了什么"、"回顾一下我最近的会话"、"这两天我用 claude 干了啥"、"活动回顾" 时使用。
基于 SOC 职业分类
正在显示 SKILL.md
| name | relevant-learnings |
| description | Find past learnings applicable to current work |
| argument-hint | ["topic or situation"] |
Search the learnings index for wisdom from past experiences that applies to current work.
Phase 6: Learning & Adaptation - This skill surfaces learnings to inform new work.
Prerequisites: Learnings captured from retrospectives and outcome reviews Outputs used by: All phases (applies institutional knowledge to new work)
The organization accumulates learnings from retrospectives, outcome reviews, and decision audits. This skill surfaces relevant learnings before repeating past mistakes or missing known opportunities.
Invoke /relevant-learnings [topic] when:
Accept a topic, situation, or question with optional flags:
/relevant-learnings pricing strategy/relevant-learnings launching to enterprise/relevant-learnings team scaling challenges --include-demo/relevant-learnings customer churn --demo-onlyFlags:
--include-demo - Include demo learnings (marked with [DEMO])--demo-only - Show only demo learnings (for testing/learning)Before searching, determine if production learnings exist:
Check context/learnings/index.md for non-demo entries
Apply demo filtering rule:
| Production Data? | Flag | Behavior |
|---|---|---|
| No | (any) | Include demo with [DEMO] markers |
| Yes | (none) | Exclude demo data, show excluded count |
| Yes | --include-demo | Include demo with [DEMO] markers |
| (any) | --demo-only | Only demo data |
Demo data is identified by:
L-DEMO-001)Read context/learnings/index.md and search for:
Prioritize learnings by:
## Relevant Learnings: [Topic]
*Found [N] learnings related to "[topic]"*
### Highly Relevant
#### L-[NNN]: [Learning Title]
- **Learning**: [Clear statement of what was learned]
- **Source**: [Retrospective/Outcome Review] for [ID]
- **Date**: [When captured]
- **Confidence**: [High/Medium/Low]
- **Context**: [Brief background]
- **Application**: [How to apply this now]
[Repeat for top 3-5 highly relevant]
### Also Related
| ID | Learning | Source | Confidence |
|----|----------|--------|------------|
| L-[NNN] | [Brief learning] | [Source] | [Confidence] |
[List additional related learnings]
### Patterns Observed
Based on these learnings, common patterns emerge:
- [Pattern 1]
- [Pattern 2]
### Recommendations
For your current work on [topic]:
1. [Recommendation based on learnings]
2. [Recommendation based on learnings]
3. [Recommendation based on learnings]
### Learnings to Validate
Some learnings may need re-validation for your context:
- L-[NNN]: [Learning] — Consider validating because: [Reason]
If user wants more detail on any learning:
context/learnings/index.md## Relevant Learnings: [Topic]
No learnings found for "[topic]" in the learnings index.
This could mean:
1. This is a new area without documented learnings
2. Try different keywords: [suggest alternatives]
3. The learnings index may need to be updated
**Recommendation**: After completing work in this area, use `/retrospective` to capture learnings for future reference.
Learnings are categorized as:
Use category context to improve recommendations.