Awesome-omni-skill context-curator
Use when starting a complex task to load relevant context from Memory MCP and suggest appropriate skills based on task type.
git clone https://github.com/diegosouzapw/awesome-omni-skill
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/development/context-curator" ~/.claude/skills/diegosouzapw-awesome-omni-skill-context-curator && rm -rf "$T"
skills/development/context-curator/SKILL.mdContext 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:
- Overall codebase structureProjectContext
- Current feature statusFeatureProgress_[branch]
- Successfully used patternsPatternHistory
- Recent architectural decisionsRecentDecisions
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:
- Command examplessrc/PlatformExampleApp/PlatformExampleApp.TextSnippet.Application/UseCaseCommands/
- Patterns guide.github/skills/backend-cqrs-command/SKILL.md
Frontend Angular:
- Component examplessrc/PlatformExampleAppWeb/apps/playground-text-snippet/
- Patterns guide.github/skills/frontend-angular-component/SKILL.md
Debugging:
- Full debugging protocol.github/AI-DEBUGGING-PROTOCOL.md
- Quick reference.github/instructions/debugging.instructions.md
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