Files
Claude-Code-Workflow/.claude/commands/workflow/session/complete.md
catlog22 920b179440 docs: 更新所有命令描述并重新生成索引文件
- 更新所有69个命令文件的description字段,基于实际功能重新生成详细描述
- 重新生成5个索引文件(all-commands, by-category, by-use-case, essential-commands, command-relationships)
- 移动analyze_commands.py到scripts/目录并完善功能
- 移除临时备份文件

命令描述改进示例:
- workflow:plan: 增加了工具和代理的详细说明(Gemini, action-planning-agent)
- cli:execute: 说明了YOLO权限和多种执行模式
- memory:update-related: 详细说明了批处理策略和工具回退链

索引文件改进:
- usage_scenario从2种扩展到10种(更精细分类)
- command-relationships覆盖所有69个命令
- 区分built-in(内置调用)和sequential(用户顺序执行)关系

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-06 15:11:31 +08:00

148 lines
4.7 KiB
Markdown

---
name: complete
description: Mark active workflow session as complete, archive with lessons learned, update manifest, remove active flag
examples:
- /workflow:session:complete
- /workflow:session:complete --detailed
---
# Complete Workflow Session (/workflow:session:complete)
## Overview
Mark the currently active workflow session as complete, analyze it for lessons learned, move it to the archive directory, and remove the active flag marker.
## Usage
```bash
/workflow:session:complete # Complete current active session
/workflow:session:complete --detailed # Show detailed completion summary
```
## Implementation Flow
### Phase 1: Prepare for Archival (Minimal Manual Operations)
**Purpose**: Find active session, move to archive location, pass control to agent. Minimal operations.
#### Step 1.1: Find Active Session and Get Name
```bash
# Find active marker
bash(find .workflow/ -name ".active-*" -type f | head -1)
# Extract session name from marker path
bash(basename .workflow/.active-WFS-session-name | sed 's/^\.active-//')
```
**Output**: Session name `WFS-session-name`
#### Step 1.2: Move Session to Archive
```bash
# Create archive directory if needed
bash(mkdir -p .workflow/.archives/)
# Move session to archive location
bash(mv .workflow/WFS-session-name .workflow/.archives/WFS-session-name)
```
**Result**: Session now at `.workflow/.archives/WFS-session-name/`
### Phase 2: Agent-Orchestrated Completion (All Data Processing)
**Purpose**: Agent analyzes archived session, generates metadata, updates manifest, and removes active marker.
#### Agent Invocation
Invoke `universal-executor` agent to complete the archival process.
**Agent Task**:
```
Task(
subagent_type="universal-executor",
description="Complete session archival",
prompt=`
Complete workflow session archival. Session already moved to archive location.
## Context
- Session: .workflow/.archives/WFS-session-name/
- Active marker: .workflow/.active-WFS-session-name
## Tasks
1. **Extract session data** from workflow-session.json (session_id, description/topic, started_at/timestamp, completed_at, status)
- If status != "completed", update it with timestamp
2. **Count files**: tasks (.task/*.json) and summaries (.summaries/*.md)
3. **Generate lessons**: Use gemini with ~/.claude/workflows/cli-templates/prompts/archive/analysis-simple.txt (fallback: analyze files directly)
- Return: {successes, challenges, watch_patterns}
4. **Build archive entry**:
- Calculate: duration_hours, success_rate, tags (3-5 keywords)
- Construct complete JSON with session_id, description, archived_at, archive_path, metrics, tags, lessons
5. **Update manifest**: Initialize .workflow/.archives/manifest.json if needed, append entry
6. **Remove active marker**
7. **Return result**: {"status": "success", "session_id": "...", "archived_at": "...", "metrics": {...}, "lessons_summary": {...}}
## Error Handling
- On failure: return {"status": "error", "task": "...", "message": "..."}
- Do NOT remove marker if failed
`
)
```
**Expected Output**:
- Agent returns JSON result confirming successful archival
- Display completion summary to user based on agent response
## Workflow Execution Strategy
### Two-Phase Approach (Optimized)
**Phase 1: Minimal Manual Setup** (2 simple operations)
- Find active session and extract name
- Move session to archive location
- **No data extraction** - agent handles all data processing
- **No counting** - agent does this from archive location
- **Total**: 2 bash commands (find + move)
**Phase 2: Agent-Driven Completion** (1 agent invocation)
- Extract all session data from archived location
- Count tasks and summaries
- Generate lessons learned analysis
- Build complete archive metadata
- Update manifest
- Remove active marker
- Return success/error result
## Quick Commands
```bash
# Phase 1: Find and move
bash(find .workflow/ -name ".active-*" -type f | head -1)
bash(basename .workflow/.active-WFS-session-name | sed 's/^\.active-//')
bash(mkdir -p .workflow/.archives/)
bash(mv .workflow/WFS-session-name .workflow/.archives/WFS-session-name)
# Phase 2: Agent completes archival
Task(subagent_type="universal-executor", description="Complete session archival", prompt=`...`)
```
## Archive Query Commands
After archival, you can query the manifest:
```bash
# List all archived sessions
jq '.archives[].session_id' .workflow/.archives/manifest.json
# Find sessions by keyword
jq '.archives[] | select(.description | test("auth"; "i"))' .workflow/.archives/manifest.json
# Get specific session details
jq '.archives[] | select(.session_id == "WFS-user-auth")' .workflow/.archives/manifest.json
# List all watch patterns across sessions
jq '.archives[].lessons.watch_patterns[]' .workflow/.archives/manifest.json
```