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