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Agent Skill · 研究检索

skill-learner(技能学习者)

skill-learner 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

skill-learner 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 56,981。

总安装 442周安装 19GitHub Stars 8下载 155核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:skill-learner(技能学习者)
来源仓库:https://github.com/vamseeachanta/workspace-hub
仓库路径:skills/skill-learner
安装命令:
npx skills add https://github.com/vamseeachanta/workspace-hub --skill skill-learner
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/vamseeachanta/workspace-hub --skill skill-learner

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

skill-learner 用于查找、检索和筛选相关信息,适合在学习或培训类任务中快速定位教程、文档或示例。支持基于关键词、技能类型或难度筛选内容,提升学习效率。通过 npx skills add 命令从指定仓库安装,需确认来源仓库的有效性与访问权限。使用前建议核实维护状态,避免使用废弃或存在兼容性问题功能。

注意是否涉及外部资源调用,确保符合本地网络与权限策略。

来源仓库内路径:skills/skill-learner

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/vamseeachanta/workspace-hub --skill skill-lear…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

skill-learner 怎么安装?

在终端执行 npx skills add https://github.com/vamseeachanta/workspace-hub --skill skill-learner。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skill-learner 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

skill-learner 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

skill-learner 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

skill-learner 用的人多吗?

公开数据是累计安装 442 次,近一周安装 19 次,下载 155 次,GitHub 8 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

skill-learner 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
skill-learner

技能学习者 · 本页

4428需要联网MIT有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Skill Learner

Automatically analyze completed work, extract reusable patterns, and create/enhance skills for continual organizational learning.

Quick Start

# Manual trigger after commit
/skill-learner

# Auto-triggers after:
# - git commit (via post-commit hook)
# - Task completion
# - Feature implementation

# Review learning
cat .claude/skill-learning-log.md

When to Use

AUTO-EXECUTES (via hook):

  • After every git commit
  • After task completion
  • After feature implementation
  • After bug fixes with significant patterns
  • After refactoring work

MANUAL TRIGGER:

  • To analyze recent work history
  • To review learning opportunities
  • To create skills from existing patterns
  • After completing a project phase
  • During knowledge capture sessions

Prerequisites

  • Git repository with commit history
  • Access to committed files and diffs
  • (Optional) Skills directory for new skill creation
  • (Optional) Internet for pattern research

Overview

The skill-learner performs post-commit analysis to identify reusable patterns, common workflows, and valuable techniques from completed work. It automatically creates new skills or enhances existing ones, ensuring continuous organizational learning.

What It Analyzes

  1. Commit Content: Files changed, code added, patterns used
  2. Commit Message: Task description, context, intent
  3. Work Patterns: Repeated workflows, common operations
  4. Problem Solutions: How bugs were fixed, how features were built
  5. Tool Usage: Libraries, frameworks, techniques employed
  6. Documentation: Comments, docs added, conventions followed

Output

Generates:

  • Pattern analysis report
  • Skill creation recommendations
  • Enhanced existing skills
  • Learning log with insights
  • Knowledge base entries

Core Operations

1. Commit Analysis

Analyzes Last Commit:

# Extract commit metadata
COMMIT_HASH=$(git rev-parse HEAD)
COMMIT_MSG=$(git log -1 --pretty=%B)
COMMIT_AUTHOR=$(git log -1 --pretty=%an)
COMMIT_DATE=$(git log -1 --pretty=%ai)

# Get changed files
git diff-tree --no-commit-id --name-only -r HEAD

# Get diff content
git diff HEAD^ HEAD

Example Analysis:

## Commit Analysis

**Commit**: a1b2c3d4
**Message**: Add interactive NPV calculator with Plotly visualization
**Author**: Developer
**Date**: 2026-01-07 14:30:00

### Files Changed (12)
- src/modules/npv/calculator.py (new)
- src/modules/npv/visualizer.py (new)
- src/modules/npv/__init__.py (new)
- tests/unit/test_npv_calculator.py (new)
- config/input/npv_analysis.yaml (new)
- scripts/run_npv_analysis.sh (new)
- docs/npv_calculator.md (new)
- ... 5 more files

### Code Additions
- Lines added: 847
- Lines removed: 23
- Net change: +824

### Technologies Used
- Plotly for interactive visualization
- Pandas for data handling
- NumPy for NPV calculations
- pytest for testing

2. Pattern Extraction

Identifies Reusable Patterns:

Pattern Types:

1. **Workflow Patterns**
   - YAML config → Script execution → HTML report
   - Data load → Process → Validate → Visualize → Save

2. **Code Patterns**
   - Interactive plotting with Plotly
   - CSV data loading with relative paths
   - Modular calculator structure
   - TDD test-first approach

3. **Tool Patterns**
   - UV environment management
   - Bash script execution
   - YAML configuration files
   - Plotly visualization

4. **Problem-Solving Patterns**
   - NPV calculation with multiple discount rates
   - Handling missing data gracefully
   - Interactive parameter adjustment
   - Real-time visualization updates

Example Extraction:

## Pattern: Interactive Financial Calculator

### Problem Solved
Need to calculate NPV with multiple scenarios and visualize results interactively.

### Solution Pattern
1. YAML configuration with parameters
2. Python calculator module
3. Plotly visualization
4. Bash execution wrapper
5. HTML report generation

### Reusability Score: 95/100
- Applicable to: IRR, ROI, payback period, sensitivity analysis
- Generic enough: Yes
- Well-documented: Yes
- Tested: Yes (85% coverage)

3. Skill Identification

Determines Skill Creation Need:

Decision Matrix:

CREATE NEW SKILL if:
✓ Pattern used 3+ times across commits
✓ Workflow is complex (5+ steps)
✓ Domain-specific knowledge required
✓ Significant time savings (>30 min per use)
✓ No existing skill covers it

ENHANCE EXISTING SKILL if:
✓ Pattern similar to existing skill
✓ New technique for known problem
✓ Updated best practices
✓ New tool/library version
✓ Improved approach discovered

SKIP if:
✗ One-time solution
✗ Trivial pattern (<3 steps)
✗ Already well-covered
✗ Repository-specific only

Example Decision:

## Skill Decision: NPV Calculator

### Analysis
- **Frequency**: Used 5 times in last month
- **Complexity**: 8-step workflow
- **Domain Knowledge**: Financial engineering
- **Time Savings**: ~2 hours per use
- **Existing Skills**: None for financial calculators

### Recommendation: CREATE NEW SKILL
**Skill Name**: financial-calculator-builder
**Category**: development/finance
**Priority**: High

**Rationale**:
- High reusability across energy economic analysis
- Complex enough to warrant skill documentation
- Significant time savings
- Establishes pattern for future financial tools

4. Skill Creation

Automatically Creates Skills:

Creation Process:

1. Generate skill name from pattern
2. Extract workflow steps
3. Create SKILL.md with template
4. Document code examples
5. Add usage instructions
6. Link related skills
7. Update skills README
8. Commit new skill

Example Skill Created:

---
name: financial-calculator-builder
description: Build interactive financial calculators (NPV, IRR, ROI) with Plotly visualization, YAML configuration, and HTML reporting.
version: 1.0.0
category: development/finance
created_from: commit a1b2c3d4
pattern_source: npv_calculator implementation
---

# Financial Calculator Builder

> Create interactive financial analysis tools with visualization and reporting.

## Quick Start

Create NPV calculator

/financial-calculator-builder npv

Create IRR calculator

/financial-calculator-builder irr

Custom calculator

/financial-calculator-builder custom --config config/calc.yaml


## Pattern

1. Define calculation in YAML config
2. Implement calculator class
3. Add Plotly visualization
4. Create bash execution wrapper
5. Generate HTML report
6. Write tests (TDD)

[... full skill documentation...]

5. Skill Enhancement

Updates Existing Skills:

Enhancement Types:

1. **Version Updates**
   - New tool versions (Plotly 5.17 → 5.18)
   - Updated APIs
   - Deprecated method replacements

2. **Best Practice Improvements**
   - Better error handling
   - Performance optimizations
   - Security enhancements

3. **New Examples**
   - Additional use cases
   - Real-world implementations
   - Edge case handling

4. **Integration Points**
   - New tool integrations
   - Cross-skill workflows
   - Automation hooks

Example Enhancement:

## Enhancement: plotly-visualization skill

### Commit: a1b2c3d4
### Pattern Found: Multi-scenario NPV visualization

### Enhancement Applied:
Added section "Financial Visualizations" to plotly-visualization skill:

**New Example: Multi-Scenario Analysis**

import plotly.graph_objects as go

Create multi-scenario NPV plot

fig = go.Figure()

for scenario in scenarios: fig.add_trace(go.Scatter( x=scenario['years'], y=scenario['npv'], name=scenario['name'], mode='lines+markers' ))

fig.update_layout( title='NPV Analysis: Multiple Scenarios', xaxis_title='Year', yaxis_title='Net Present Value ($M)' )


**Version**: 1.2.0 → 1.3.0 **Reason**: Added financial visualization patterns

6. Knowledge Synthesis

Builds Organizational Knowledge:

Knowledge Base Structure:


.claude/knowledge/ ├── patterns/ │ ├── financial-calculations.md │ ├── interactive-visualization.md │ └── data-pipelines.md ├── techniques/ │ ├── npv-calculation.md │ ├── scenario-analysis.md │ └── sensitivity-testing.md └── lessons/ ├── 2026-01-07-npv-calculator.md └──...

Example Knowledge Entry:

# Pattern: Interactive Financial Calculator

**Discovered**: 2026-01-07 (commit a1b2c3d4)
**Category**: Financial Engineering
**Reusability**: High

## Problem
Need to perform financial calculations (NPV, IRR, ROI) with:
- Multiple scenarios
- Interactive parameter adjustment
- Visual comparison
- Exportable reports

## Solution Pattern

### 1. Configuration (YAML)

calculation: type: npv discount_rates: [0.05, 0.08, 0.10, 0.12] cash_flows: data/cash_flows.csv scenarios: - base - optimistic - pessimistic


### 2. Calculator Module (Python)

class NPVCalculator: def calculate(self, cash_flows, discount_rate): return np.npv(discount_rate, cash_flows)

def multi_scenario(self, scenarios, rates): results = {} for scenario in scenarios: for rate in rates: npv = self.calculate(scenario.cash_flows, rate) results[(scenario.name, rate)] = npv return results


### 3. Visualization (Plotly)

Interactive multi-scenario comparison with hover tooltips.

### 4. Execution (Bash)

./scripts/run_npv_analysis.sh config/npv.yaml


## Lessons Learned

1. YAML configuration makes calculators flexible
2. Plotly enables interactive scenario exploration
3. Modular design allows easy extension to other metrics
4. TDD catches calculation errors early

## Applications

- ✅ NPV Calculator (implemented)
- 🔲 IRR Calculator (recommended)
- 🔲 ROI Calculator (recommended)
- 🔲 Payback Period Calculator (recommended)
- 🔲 Sensitivity Analysis Tool (high value)

## Related Skills

- financial-calculator-builder (created)
- plotly-visualization (enhanced)
- yaml-workflow-executor (existing)

Learning Log

Maintains Continuous Learning Record:

Log Format:

# Skill Learning Log

## 2026-01-07

### Commit: a1b2c3d4 - NPV Calculator Implementation

**Patterns Extracted**: 3
- Interactive financial calculator
- Multi-scenario analysis
- YAML-driven calculations

**Skills Created**: 1
- financial-calculator-builder (v1.0.0)

**Skills Enhanced**: 2
- plotly-visualization (v1.2.0 → v1.3.0)
  - Added financial visualization examples
- yaml-workflow-executor (v2.1.0 → v2.1.1)
  - Added financial config examples

**Knowledge Added**: 1
- patterns/interactive-financial-calculator.md

**Reusability Score**: 95/100
**Time Savings**: ~2 hours per future use
**Learning Value**: High - establishes financial tool pattern

---

## 2026-01-06

### Commit: e5f6g7h8 - Marine Safety Data Processor

**Patterns Extracted**: 2
- CSV data validation
- Safety incident categorization

**Skills Enhanced**: 1
- data-pipeline-processor (v3.0.0 → v3.1.0)
  - Added safety data validation patterns

**Knowledge Added**: 1
- techniques/safety-data-validation.md

**Reusability Score**: 75/100
**Time Savings**: ~1 hour per future use
**Learning Value**: Medium - domain-specific but valuable

---

Execution Checklist

Pre-Analysis:

  • Git repository is valid
  • At least one commit exists
  • Access to skills directory
  • Access to knowledge base

Analysis Phase:

  • Extract commit metadata
  • Analyze changed files
  • Review code diff
  • Identify patterns used
  • Assess reusability

Decision Phase:

  • Check for existing similar skills
  • Calculate reusability score
  • Determine create vs enhance vs skip
  • Prioritize recommendations

Action Phase:

  • Create new skills (if warranted)
  • Enhance existing skills
  • Update knowledge base
  • Append to learning log
  • Commit skill changes

Post-Analysis:

  • Report generation
  • Metrics tracking
  • User notification (optional)

Hook Integration

Post-Commit Hook

Hook Configuration:

# .claude/hooks/post-commit.sh
#!/bin/bash
# Auto-execute skill learning after commits

REPO_PATH="$(pwd)"
SKILL_PATH="${HOME}/.claude/skills/workspace-hub/skill-learner"

# Allow bypassing skill learning
if [ "${SKIP_SKILL_LEARNING:-0}" = "1" ]; then
    echo "Skill learning skipped (SKIP_SKILL_LEARNING=1)"
    exit 0
fi

# Only run on significant commits
LINES_CHANGED=$(git diff HEAD^ HEAD --shortstat | grep -oE '[0-9]+ insertion' | grep -oE '[0-9]+' || echo 0)

if [ "$LINES_CHANGED" -lt 50 ]; then
    echo "Small commit (<50 lines), skipping skill learning"
    exit 0
fi

# Run skill learning
echo "Analyzing commit for learning opportunities..."
"$SKILL_PATH/analyze_commit.sh" "$REPO_PATH"

exit 0

Trigger Conditions:

  • After git commit (>50 lines changed)
  • After task completion
  • After feature merges
  • Manual trigger via /skill-learner

Bypass Hook:

# Skip learning for trivial commits
SKIP_SKILL_LEARNING=1 git commit -m "Fix typo"

# Or disable temporarily
mv .claude/hooks/post-commit.sh .claude/hooks/post-commit.sh.disabled

Automation Scripts

1. Commit Analyzer

Location: analyze_commit.sh

#!/bin/bash
# Analyze recent commit for learning opportunities

REPO_PATH="${1:-.}"
COMMIT_HASH="${2:-HEAD}"

# Extract commit info
analyze_commit() {
    echo "Analyzing commit: $COMMIT_HASH"

    # Get commit metadata
    local msg=$(git log -1 --pretty=%B $COMMIT_HASH)
    local author=$(git log -1 --pretty=%an $COMMIT_HASH)
    local date=$(git log -1 --pretty=%ai $COMMIT_HASH)

    # Get changed files
    local files=$(git diff-tree --no-commit-id --name-only -r $COMMIT_HASH)

    # Analyze patterns
    extract_patterns "$files"

    # Make skill decisions
    decide_skill_actions

    # Generate report
    generate_learning_report
}

# Extract reusable patterns
extract_patterns() {
    local files="$1"

    # Check for workflow patterns
    if echo "$files" | grep -q "config/input/.*\.yaml"; then
        echo "Pattern: YAML-driven workflow"
    fi

    if echo "$files" | grep -q "scripts/.*\.sh"; then
        echo "Pattern: Bash execution script"
    fi

    # Check for code patterns
    if git diff $COMMIT_HASH^ $COMMIT_HASH | grep -q "import plotly"; then
        echo "Pattern: Plotly visualization"
    fi

    # More pattern detection...
}

# Decide whether to create/enhance skills
decide_skill_actions() {
    # Reusability scoring
    local score=0

    # Check commit history for similar patterns
    local pattern_count=$(git log --all --grep="similar pattern" | wc -l)
    if [ $pattern_count -ge 3 ]; then
        ((score += 30))
    fi

    # Check code complexity
    local lines_added=$(git diff $COMMIT_HASH^ $COMMIT_HASH --shortstat | grep -oE '[0-9]+ insertion' | grep -oE '[0-9]+')
    if [ $lines_added -gt 100 ]; then
        ((score += 20))
    fi

    # Decision
    if [ $score -ge 70 ]; then
        echo "Recommendation: CREATE NEW SKILL"
    elif [ $score -ge 40 ]; then
        echo "Recommendation: ENHANCE EXISTING SKILL"
    else
        echo "Recommendation: SKIP (low reusability)"
    fi
}

# Generate learning report
generate_learning_report() {
    local report_file=".claude/learning-reports/$(date +%Y-%m-%d)-$COMMIT_HASH.md"
    mkdir -p "$(dirname "$report_file")"

    {
        echo "# Learning Report"
        echo "**Commit**: $COMMIT_HASH"
        echo "**Date**: $(date)"
        echo ""
        echo "## Patterns Extracted"
        # ... pattern details ...
        echo ""
        echo "## Recommendations"
        # ... recommendations ...
    } > "$report_file"

    echo "Learning report saved: $report_file"
}

main "$@"

2. Skill Creator (from Pattern)

Location: create_skill_from_pattern.sh

#!/bin/bash
# Create new skill from extracted pattern

PATTERN_NAME="$1"
CATEGORY="${2:-development}"
SOURCE_COMMIT="$3"

# Generate skill from template
generate_skill() {
    local skill_dir="${HOME}/.claude/skills/${CATEGORY}/${PATTERN_NAME}"
    mkdir -p "$skill_dir"

    # Create SKILL.md
    cat > "${skill_dir}/SKILL.md" << EOF
---
name: ${PATTERN_NAME}
description: Auto-generated skill from commit pattern
version: 1.0.0
category: ${CATEGORY}
created_from: ${SOURCE_COMMIT}
---

# ${PATTERN_NAME^} Skill

> Auto-generated from repeated commit pattern

## Pattern Source

**Commit**: ${SOURCE_COMMIT}
**Extracted**: $(date)

[... skill template ...]
EOF

    echo "Skill created: $skill_dir"
}

generate_skill

3. Bulk Learning Analysis

Analyzes Recent Commit History:

#!/bin/bash
# Analyze last N commits for learning opportunities

REPO_PATH="${1:-.}"
COMMIT_COUNT="${2:-10}"

echo "Analyzing last $COMMIT_COUNT commits..."

for commit in $(git log -n $COMMIT_COUNT --pretty=%H); do
    echo "Commit: $commit"
    ./analyze_commit.sh "$REPO_PATH" "$commit"
    echo ""
done

# Generate aggregate report
echo "Generating aggregate learning report..."
./generate_aggregate_report.sh

Error Handling

No Patterns Found

ℹ️ Info: No reusable patterns detected in commit

Reason: Commit too small or repository-specific changes

Action: No skills created, logged for future pattern detection

Skill Already Exists

⚠️ Warning: Skill 'financial-calculator-builder' already exists

Action Options:
1. Enhance existing skill (recommended)
2. Create variant skill (e.g., 'financial-calculator-builder-v2')
3. Skip creation

Recommendation: ENHANCE

Invalid Pattern

❌ Error: Pattern extraction failed

Reason: Unable to identify coherent workflow or technique

Action: Manual review required - see .claude/learning-reports/error-*.md

Skills Directory Not Found

⚠️ Warning: Skills directory not accessible

Location Expected: ~/.claude/skills/ or .claude/skills/

Action: Create skills directory or configure SKILLS_PATH

Metrics & Success Criteria

Learning Metrics

MetricTargetMeasurement
Patterns Extracted/Week5-10Count from learning log
Skills Created/Month2-5New skills in directory
Skills Enhanced/Month5-10Version bumps recorded
Reusability Score Avg> 70Average across patterns
Time Savings> 10 hrs/monthEstimated from usage

Quality Metrics

MetricTargetMeasurement
Pattern Accuracy> 90%Correct pattern identification
Skill Usefulness> 80%Skills used after creation
Enhancement Value> 70%Enhanced skills improve workflow
False Positives< 10%Incorrect skill creation rate

Adoption Metrics

MetricTargetCurrent
Hook Installation100% reposTrack per repo
Learning Log SizeGrowingLines in log
Knowledge Base Growth+10 entries/monthCount entries
Skill Library Size+3-5 skills/monthTotal skills

Integration Points

With Session Start Routine

# Review recent learning at session start
/session-start-routine

# Includes:
# - Recent patterns extracted
# - Skills created/enhanced
# - Learning opportunities

With Skill Creator

# Manual skill creation uses patterns
/skill-creator new-feature

# Skill learner provides:
# - Similar existing patterns
# - Related skills
# - Best practices from learning log

With Repo Sync

# Learning across all repos
./bulk_learning_analysis.sh

# Aggregate patterns from entire workspace

Best Practices

1. Review Learning Log Regularly

# Weekly review
cat .claude/skill-learning-log.md | head -100

# Identify high-value patterns
grep "Reusability Score: 9" .claude/skill-learning-log.md

2. Refine Patterns

# If pattern is too specific, generalize
# If pattern is too generic, specialize
# Document edge cases

3. Link Related Knowledge

# Cross-reference skills
# Update related documentation
# Share patterns across teams

4. Validate Skill Usefulness

# Track skill usage
# Deprecate unused skills
# Enhance frequently-used skills

Related Skills

References


Version History

  • 1.0.0 (2026-01-07): Initial release - post-commit skill learning with pattern extraction, skill creation/enhancement, knowledge synthesis, auto-hook integration, learning log, metrics tracking, and continual learning capabilities

平台分布

Claude Code

26.46%
按下载量换算41

windsurf

21.83%
按下载量换算34

trae

17.48%
按下载量换算27

OpenCode

12.46%
按下载量换算19

Cursor

6.86%
按下载量换算11

Codex

3.06%
按下载量换算5

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

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