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context-curator

@duc01226/EasyPlatform
2
0

Use when starting a complex task to load relevant context from Memory MCP and suggest appropriate skills based on task type.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name context-curator
description Use when starting a complex task to load relevant context from Memory MCP and suggest appropriate skills based on task type.
allowed-tools Read, Grep, Glob, mcp__memory__search_nodes, mcp__memory__read_graph, mcp__memory__open_nodes

Context Curator Skill

Purpose

Dynamically load relevant context based on the current task type. This skill implements the "Curation Layer" from context engineering principles.

When to Use

  • Starting a new complex task
  • Resuming work on a feature after a break
  • Need to recall past decisions or patterns
  • Want to ensure consistent patterns with previous work

Task Type Detection

Analyze the user's request to determine task type:

Keywords Task Type Relevant Skills
bug, error, fix, broken, debug debugging bug-diagnosis, tasks-bug-diagnosis
command, save, create, update, delete backend-cqrs backend-cqrs-command, backend-entity-development
query, list, get, search, filter backend-query backend-cqrs-query, backend-entity-development
component, UI, frontend, Angular frontend frontend-angular-component, frontend-angular-store
form, validation, input frontend-form frontend-angular-form, frontend-angular-component
API, service, http api-integration frontend-angular-api-service, backend-cqrs-command
review, refactor, improve code-review code-review, tasks-code-review
test, spec, coverage testing test-generation, tasks-test-generation
message, event, bus, sync cross-service backend-message-bus, arch-cross-service-integration
migration, schema, database data-migration backend-data-migration, db-migrate
job, background, scheduled, recurring background-job backend-background-job
security, auth, permission security arch-security-review
performance, slow, optimize performance arch-performance-optimization
implement, feature, add, build feature-implementation feature-implementation, tasks-feature-implementation
document, readme, docs documentation documentation, tasks-documentation, readme-improvement
branch, compare, diff, changes branch-comparison branch-comparison, tasks-spec-update

Execution Steps

Step 1: Detect Task Type

Analyze user request keywords to identify task type from the table above.

Step 2: Load Memory Context

Search Memory MCP for relevant entities:

mcp__memory__search_nodes({ query: "[task-type] [feature-name]" })

Key entities to query:

  • ProjectContext - Overall codebase structure
  • FeatureProgress_[branch] - Current feature status
  • PatternHistory - Successfully used patterns
  • RecentDecisions - Recent architectural decisions

Step 3: Suggest Relevant Skills

Based on detected task type, recommend specific skills from the table.

Step 4: Load Reference Files

For each task type, suggest key reference files:

Backend CQRS:

  • src/PlatformExampleApp/PlatformExampleApp.TextSnippet.Application/UseCaseCommands/ - Command examples
  • .github/skills/backend-cqrs-command/SKILL.md - Patterns guide

Frontend Angular:

  • src/PlatformExampleAppWeb/apps/playground-text-snippet/ - Component examples
  • .github/skills/frontend-angular-component/SKILL.md - Patterns guide

Debugging:

  • .github/AI-DEBUGGING-PROTOCOL.md - Full debugging protocol
  • .github/instructions/debugging.instructions.md - Quick reference

Step 5: Output Context Summary

Provide formatted summary:

## Context Loaded

**Task Type:** [detected type]
**Suggested Skills:** [skill-1], [skill-2]
**Memory Context:** [relevant entities found]
**Reference Files:** [key files to read]

**Previous Work:**

- [Relevant past decisions]
- [Patterns used successfully]
- [Current feature progress]

**Next Steps:**

1. [Recommended first action]
2. [Recommended second action]

Memory Integration

After completing significant work, store learnings:

mcp__memory__create_entities({
  entities: [{
    name: "Decision_[timestamp]",
    entityType: "ArchitecturalDecision",
    observations: ["Chose X because Y", "Pattern used: Z"]
  }]
})

Example Usage

User: "I need to add a new endpoint for task management"

Context Curator Output:

## Context Loaded

**Task Type:** backend-cqrs + api-integration
**Suggested Skills:** backend-cqrs-command, backend-cqrs-query, frontend-angular-api-service
**Memory Context:** Found FeatureProgress_task-management with 3 observations

**Previous Work:**

- Task entity already created (TaskItemEntity.cs)
- Using SaveTaskItemCommand pattern

**Next Steps:**

1. Check existing TaskItemController for patterns
2. Use backend-cqrs-command skill for new command
3. Update frontend API service