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.

install
source · Clone the upstream repo
git clone https://github.com/diegosouzapw/awesome-omni-skill
Claude Code · Install into ~/.claude/skills/
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"
manifest: skills/development/context-curator/SKILL.md
source content

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:

KeywordsTask TypeRelevant Skills
bug, error, fix, broken, debugdebuggingbug-diagnosis, tasks-bug-diagnosis
command, save, create, update, deletebackend-cqrsbackend-cqrs-command, backend-entity-development
query, list, get, search, filterbackend-querybackend-cqrs-query, backend-entity-development
component, UI, frontend, Angularfrontendfrontend-angular-component, frontend-angular-store
form, validation, inputfrontend-formfrontend-angular-form, frontend-angular-component
API, service, httpapi-integrationfrontend-angular-api-service, backend-cqrs-command
review, refactor, improvecode-reviewcode-review, tasks-code-review
test, spec, coveragetestingtest-generation, tasks-test-generation
message, event, bus, synccross-servicebackend-message-bus, arch-cross-service-integration
migration, schema, databasedata-migrationbackend-data-migration, db-migrate
job, background, scheduled, recurringbackground-jobbackend-background-job
security, auth, permissionsecurityarch-security-review
performance, slow, optimizeperformancearch-performance-optimization
implement, feature, add, buildfeature-implementationfeature-implementation, tasks-feature-implementation
document, readme, docsdocumentationdocumentation, tasks-documentation, readme-improvement
branch, compare, diff, changesbranch-comparisonbranch-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