Agent skill
archon
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Install this agent skill to your Project
npx add-skill https://github.com/abdullah1854/ClaudeSuperSkills/tree/main/archon
SKILL.md
archon
Visual Kanban board integration with RAG (Retrieval Augmented Generation). Project management with AI-powered context retrieval. Use when: managing tasks visually, integrating project boards, building RAG systems.
Metadata
- Version: 1.0.0
- Category: productivity
- Source: workspace
Tags
kanban, rag, project-management, tasks, retrieval, boards
MCP Dependencies
None specified
Inputs
action(string) (required): Action: create-board, add-task, query, synccontext(string) (optional): Context for RAG queries or task detailsboard(string) (optional): Board name
Chain of Thought
- Initialize board structure. 2. Connect to knowledge base. 3. Enable RAG for smart suggestions. 4. Track task progress. 5. Generate insights from history.
Workflow
No workflow defined
Anti-Hallucination Rules
None specified
Verification Checklist
None specified
Usage
typescript
// Execute via MCP Gateway:
gateway_execute_skill({ name: "archon", inputs: { ... } })
// Or via REST API:
// POST /api/code/skills/archon/execute
// Body: { "inputs": { ... } }
Code
typescript
// Archon - Kanban + RAG Skill
const action = inputs.action || 'create-board';
console.log(\`## Archon: Visual Kanban + RAG
### Action: \${action}
---
## Kanban Board Structure
\`\`\`typescript
interface Board {
id: string;
name: string;
columns: Column[];
knowledge: KnowledgeBase;
}
interface Column {
id: string;
name: string; // "Backlog" | "In Progress" | "Review" | "Done"
tasks: Task[];
}
interface Task {
id: string;
title: string;
description: string;
priority: "low" | "medium" | "high";
labels: string[];
context: string[]; // RAG-retrieved context
}
\`\`\`
---
## RAG Integration
### Knowledge Base Setup
\`\`\`typescript
// Using vector store for context
const kb = {
// Store project documentation
addDocument: async (doc) => {
const embedding = await embed(doc.content);
await vectorStore.upsert({
id: doc.id,
values: embedding,
metadata: { source: doc.source, type: doc.type }
});
},
// Query relevant context
query: async (question) => {
const embedding = await embed(question);
const results = await vectorStore.query({
vector: embedding,
topK: 5
});
return results.matches;
}
};
\`\`\`
### Smart Task Suggestions
\`\`\`typescript
// When creating a task, auto-retrieve relevant context
async function createTask(title, description) {
// Query knowledge base for relevant docs
const context = await kb.query(description);
// Suggest related tasks
const similar = await findSimilarTasks(description);
return {
title,
description,
context: context.map(c => c.metadata.source),
relatedTasks: similar
};
}
\`\`\`
---
## Board Operations
### Create Board
\`\`\`bash
# Using TodoWrite for simple Kanban
TodoWrite([
{ content: "Task 1", status: "pending" }, // Backlog
{ content: "Task 2", status: "in_progress" }, // In Progress
{ content: "Task 3", status: "completed" } // Done
])
\`\`\`
### Sync with External Tools
- Linear: \`linear_*\` MCP tools
- GitHub Projects: \`gh project\` CLI
- Notion: \`notion_*\` MCP tools
---
## RAG Workflow
1. **Index** - Store project docs in vector DB
2. **Query** - Retrieve relevant context for tasks
3. **Augment** - Enhance task descriptions with context
4. **Generate** - AI-powered task breakdown
\`);
Created: Tue Dec 23 2025 00:34:29 GMT+0800 (Singapore Standard Time) Updated: Tue Dec 23 2025 00:34:29 GMT+0800 (Singapore Standard Time)
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