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Free建立符合官方規格的 Claude Code skill:蒐集需求、產出正確格式的 SKILL.md、驗證命名與描述規則、套用 Progressive Disclosure 結構。適合在新增 skill 或 slash command 時使用。
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分析當前專案的 Claude Code session 資料,產出 context 效率報告(模型用量、工具效率、委派比例、TodoWrite 使用率)。搭配 context-management.md 使用。
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See how to installReview the instructions the agent will receive before you install.
.claude/rules/context-management.md 做 context 壓縮決策執行以下 bash 腳本(適配 Claude Code 官方 ~/.claude/projects/ 路徑):
#!/bin/bash
CWD="${ARGUMENTS:-$(pwd)}"
PROJECT_KEY=$(echo "$CWD" | sed 's|/|-|g; s|^-||')
PROJECTS_BASE="$HOME/.claude/projects"
PROJECT_DIR="$PROJECTS_BASE/$PROJECT_KEY"
if [ ! -d "$PROJECT_DIR" ]; then
echo "No session data found for: $CWD"
echo "Looking in: $PROJECT_DIR"
echo "Available projects:"
ls "$PROJECTS_BASE" 2>/dev/null | head -10
exit 1
fi
echo "╔══════════════════════════════════════════════════════════════════╗"
printf "║ Context Efficiency Report: %-35s║\n" "$(basename "$CWD" | head -c 34)"
echo "╚══════════════════════════════════════════════════════════════════╝"
echo "Project: $CWD"
echo ""
# Session counts
session_count=$(find "$PROJECT_DIR" -maxdepth 1 -name "*.jsonl" ! -name "agent-*.jsonl" 2>/dev/null | wc -l | tr -d ' ')
agent_count=$(find "$PROJECT_DIR" -maxdepth 1 -name "agent-*.jsonl" 2>/dev/null | wc -l | tr -d ' ')
echo "📁 SESSIONS"
echo "────────────────────────────────────────────────────────────────────"
echo "Main Sessions: $session_count"
echo "Agent Sessions: $agent_count"
if [ "$session_count" -gt 0 ]; then
ratio=$(echo "scale=2; $agent_count / $session_count" | bc)
echo "Delegation Ratio: ${ratio}:1"
fi
echo ""
# Model usage
echo "🤖 MODEL USAGE"
echo "────────────────────────────────────────────────────────────────────"
find "$PROJECT_DIR" -maxdepth 1 -name "*.jsonl" -size +1k -exec cat {} + 2>/dev/null | \
jq -r 'select(.message.model != null) | .message.model' 2>/dev/null | \
sort | uniq -c | sort -rn
echo ""
# Tool usage top 15
echo "🔧 TOOL USAGE (top 15)"
echo "────────────────────────────────────────────────────────────────────"
find "$PROJECT_DIR" -maxdepth 1 -name "*.jsonl" -size +1k -exec cat {} + 2>/dev/null | \
jq -r '.message.content[]? | select(.type == "tool_use") | .name' 2>/dev/null | \
sort | uniq -c | sort -rn | head -15
echo ""
# Data size
echo "📊 DATA SIZE"
echo "────────────────────────────────────────────────────────────────────"
total_size=$(du -sh "$PROJECT_DIR" 2>/dev/null | cut -f1)
msg_count=$(find "$PROJECT_DIR" -maxdepth 1 -name "*.jsonl" -exec cat {} + 2>/dev/null | wc -l | tr -d ' ')
echo "Total Size: $total_size"
echo "Message Records: $msg_count"
echo ""
# Collect data once
all_tools=$(find "$PROJECT_DIR" -maxdepth 1 -name "*.jsonl" -size +1k -exec cat {} + 2>/dev/null | \
jq -r '.message.content[]? | select(.type == "tool_use") | .name' 2>/dev/null)
all_models=$(find "$PROJECT_DIR" -maxdepth 1 -name "*.jsonl" -size +1k -exec cat {} + 2>/dev/null | \
jq -r '.message.model // empty' 2>/dev/null)
# Model counts
opus=$(echo "$all_models" | grep -c "opus" || echo 0)
sonnet=$(echo "$all_models" | grep -c "sonnet" || echo 0)
haiku=$(echo "$all_models" | grep -c "haiku" || echo 0)
model_total=$((opus + sonnet + haiku))
# Tool counts
bash_count=$(echo "$all_tools" | grep -c "^Bash$" || echo 0)
grep_count=$(echo "$all_tools" | grep -c "^Grep$" || echo 0)
glob_count=$(echo "$all_tools" | grep -c "^Glob$" || echo 0)
todo_count=$(echo "$all_tools" | grep -c "^TodoWrite$" || echo 0)
# Scoring (100 pts)
score=0
warnings=""
# 1. Model efficiency (30 pts):Opus ≤50% = 30pts,100% Opus = 0pts
if [ "$model_total" -gt 0 ]; then
opus_pct=$((opus * 100 / model_total))
model_score=$((30 - (opus_pct > 50 ? (opus_pct - 50) * 30 / 50 : 0)))
[ "$model_score" -lt 0 ] && model_score=0
score=$((score + model_score))
[ "$opus_pct" -gt 80 ] && warnings="${warnings}⚠️ Opus ${opus_pct}% — 探索型任務改用 Haiku\n"
else
opus_pct=0; model_score=15; score=$((score + 15))
fi
# 2. Delegation ratio (25 pts):3:1 = 25pts
if [ "$session_count" -gt 0 ]; then
delegation_ratio_x100=$((agent_count * 100 / session_count))
delegation_score=$((delegation_ratio_x100 * 25 / 300))
[ "$delegation_score" -gt 25 ] && delegation_score=25
score=$((score + delegation_score))
[ "$delegation_ratio_x100" -lt 100 ] && warnings="${warnings}⚠️ 委派比例低 — 多用 subagent 隔離大型任務\n"
else
delegation_score=0
fi
# 3. Tool efficiency (25 pts):native Grep/Glob > Bash
native_search=$((grep_count + glob_count))
if [ "$bash_count" -gt 0 ]; then
tool_ratio_x100=$((native_search * 100 / bash_count))
tool_score=$((tool_ratio_x100 * 25 / 50))
[ "$tool_score" -gt 25 ] && tool_score=25
score=$((score + tool_score))
[ "$tool_ratio_x100" -lt 10 ] && warnings="${warnings}⚠️ 優先用 Grep/Glob 工具而非 bash grep/find\n"
else
tool_score=25; score=$((score + 25))
fi
# 4. TodoWrite tracking (20 pts)
if [ "$msg_count" -gt 0 ]; then
todo_ratio_x1000=$((todo_count * 1000 / msg_count))
todo_score=$((todo_ratio_x1000 * 20 / 50))
[ "$todo_score" -gt 20 ] && todo_score=20
score=$((score + todo_score))
[ "$todo_count" -eq 0 ] && warnings="${warnings}⚠️ 未使用 TodoWrite — 複雜任務應追蹤進度\n"
else
todo_score=0
fi
# Output score
echo "📈 EFFICIENCY SCORE"
echo "────────────────────────────────────────────────────────────────────"
echo ""
echo " ┌─────────────────────────────────────┐"
printf " │ SCORE: %3d / 100 │\n" "$score"
echo " └─────────────────────────────────────┘"
echo ""
echo " Breakdown:"
printf " Model efficiency: %2d/30 (Opus %d%%)\n" "$model_score" "$opus_pct"
del_ratio=$(echo "scale=1; ($agent_count + 0.0) / ($session_count + 0.001)" | bc)
printf " Task delegation: %2d/25 (ratio %s:1)\n" "$delegation_score" "$del_ratio"
printf " Tool efficiency: %2d/25 (Grep+Glob: %d, Bash: %d)\n" "$tool_score" "$native_search" "$bash_count"
printf " Task tracking: %2d/20 (TodoWrite: %d)\n" "$todo_score" "$todo_count"
echo ""
if [ -n "$warnings" ]; then
echo " Warnings:"
printf " $warnings"
fi
echo ""
echo "═══════════════════════════════════════════════════════════════════"
echo "Generated: $(date '+%Y-%m-%d %H:%M')"
| 維度 | 滿分 | 目標 | 說明 |
|---|---|---|---|
| 模型效率 | 30 | Opus ≤50% | 搜尋/探索用 Haiku,實作用 Sonnet |
| 委派比例 | 25 | ≥3:1 | agent session / main session |
| 工具效率 | 25 | Grep+Glob:Bash ≥50% | 優先用 native tools |
| 任務追蹤 | 20 | TodoWrite 有使用 | 複雜任務應有追蹤 |
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建立符合官方規格的 Claude Code skill:蒐集需求、產出正確格式的 SKILL.md、驗證命名與描述規則、套用 Progressive Disclosure 結構。適合在新增 skill 或 slash command 時使用。
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啟動 Agent Team 模式,多個 worker 平行處理任務並共享發現。適用於大型重構、codebase 遷移、全面審查。
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分析 claude.com/blog 的新文章,提取可操作洞察並更新 workspace 設定。手動調用。
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追蹤並計算當前 session 的 Token 使用量和花費(USD)。產出前後對比報告,幫助使用者了解 workspace 優化的實際效益。
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系統性 debug 工作流:捕捉錯誤、分析日誌、關聯跨系統 pattern,產出有根因與修復驗證的報告。適用於 bug、test 失敗、生產異常。
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對 staged changes 執行三維度平行審查(安全、效能、風格),合成優先排序的發現摘要。
The English summary is being prepared. Showing the original description.