一键导入
during-task
Lightweight tactical guidance during implementation. Just MCP suggestions and quick lookups, no heavy Graphiti searches.
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。
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Lightweight tactical guidance during implementation. Just MCP suggestions and quick lookups, no heavy Graphiti searches.
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。
Mandatory knowledge capture after completing work. Documents solution in Graphiti and tracks effectiveness for system improvement.
Comprehensive discovery before starting any spec or major task. Searches Graphiti, recommends vibe/MCPs, surfaces patterns.
This skill should be used when conducting focused code reviews that emphasize clarity, data flow understanding, and minimal assumptions. Trigger when reviewing pull requests, code changes, or when explicitly asked to review code. Produces structured reviews with priority-based feedback.
This skill should be used when testing ScreenGraph end-to-end flows (drift detection, agent runs) while monitoring backend and frontend logs in real-time. Use when the user wants to observe system behavior across services, debug live runs, or verify event streaming.
Workflow for using Graphiti MCP tools to capture preferences, procedures, and facts consistently.
Self-improving context loader that MUST be run before every prompt. Searches Graphiti for relevant past work, loads appropriate vibe, and provides intelligent recommendations.
基于 SOC 职业分类
| name | during-task |
| description | Lightweight tactical guidance during implementation. Just MCP suggestions and quick lookups, no heavy Graphiti searches. |
Use DURING:
tasks.mdCAN be called MULTIPLE times (designed for this!)
1. Get MCP suggestions for subtask (no full Graphiti search)
2. Quick gotcha lookup (optional, only if stuck)
3. Brief workflow guidance
Token cost: ~300 tokens
Frequency: 5-10 times per spec
ROI: Stays aligned = prevents rework
suggest_mcps({
task: "[specific subtask]",
include_examples: false
});
Returns:
search_memory_facts({
query: "[specific tech/component] [specific issue]",
group_ids: ["screengraph"],
max_facts: 3 // Just top 3, not 10!
});
Returns:
**Task**: [subtask]
**MCPs**: [mcp1], [mcp2], [mcp3]
Do this:
1. [action 1]
2. [action 2]
~5 lines. That's it.
# Already ran @before-task during discovery
# Now implementing tasks.md
# Task 1: Create database schema
@during-task Create user table schema
→ MCPs: encore-mcp, context7
# Code it
# Task 2: Add API endpoint
@during-task Add user registration endpoint
→ MCPs: encore-mcp, sequential-thinking
# Code it
# Task 3: Build UI component
@during-task Build registration form
→ MCPs: svelte, browser
# Code it
# All tasks done → Run @after-task
# Working on backend
@during-task Add database migration
→ Vibe: backend_vibe, MCPs: encore-mcp
# Now switching to frontend
@during-task Update UI to show new field
→ Vibe: frontend_vibe, MCPs: svelte, browser
# ✅ Vibe changed automatically!
@during-task Add password validation logic → 300 tokens ✅
@during-task Create login form component → 300 tokens ✅
@during-task Write unit test for auth endpoint → 300 tokens ✅
@during-task Implement entire authentication feature → 2000 tokens ❌
# This should be @before-task, not @during-task!
✅ Call for each subtask - Designed for frequent use
✅ Be specific - "Add validation" not "implement feature"
✅ Skip Graphiti re-search - Already have context from @before-task
❌ Don't use for discovery - That's @before-task
❌ Don't call for trivial changes - Changing a variable name doesn't need context
Skip @during-task for:
Use @during-task for:
Without @during-task:
→ Implement blindly
→ Use wrong MCP
→ Waste time
With @during-task:
→ Quick guidance (300 tokens)
→ Right MCP immediately
→ Stay on track
300 tokens to avoid 30 minutes of wrong direction = 100x ROI.
Purpose: Provide lightweight, frequent check-ins during implementation without burning tokens on redundant searches.