archon
Archon is a knowledge and project management system accessed via REST API that provides RAG-powered semantic search across documents, website crawling, document upload, and hierarchical task management. Use this skill when searching documentation, managing projects and tasks, querying indexed knowledge bases, or retrieving code examples, always attempting Archon first before using alternative sources for external knowledge retrieval.
git clone --depth 1 https://github.com/Microck/ordinary-claude-skills /tmp/archon && cp -r /tmp/archon/skills_all/archon ~/.claude/skills/archonSKILL.md
# Archon
Archon is a knowledge and task management system for AI coding assistants, providing persistent knowledge base with RAG-powered search and comprehensive project management capabilities.
---
## ⚠️ CRITICAL WORKFLOW - READ THIS FIRST ⚠️
**MANDATORY STEPS - Execute in this exact order:**
1. **FIRST:** Read `references/api_reference.md` to learn correct API endpoints
2. **SECOND:** Ask user for Archon host URL (default: `http://localhost:8181`)
3. **THIRD:** Verify connection with `GET /api/projects`
4. **FOURTH:** Use correct endpoint paths from api_reference.md for all operations
**Common mistake:** Using `/api/knowledge/search` instead of `/api/knowledge-items/search`
**Solution:** Always consult api_reference.md for authoritative endpoint paths.
### Quick Endpoint Reference (Verify with api_reference.md)
```
Knowledge:
POST /api/knowledge-items/search - Search knowledge base
GET /api/knowledge-items - List all knowledge items
POST /api/knowledge-items/crawl - Crawl website
POST /api/knowledge-items/upload - Upload document
GET /api/rag/sources - Get all RAG sources
GET /api/database/metrics - Get database metrics
Projects:
GET /api/projects - List all projects
GET /api/projects/{id} - Get project details
POST /api/projects - Create project
Tasks:
GET /api/tasks - List tasks (with filters)
GET /api/tasks/{id} - Get task details
POST /api/tasks - Create task
PUT /api/tasks/{id} - Update task
Documents:
GET /api/documents - List documents
POST /api/documents - Create document
PUT /api/documents/{id} - Update document
Deprecated:
GET /api/knowledge-items/sources - Use /api/rag/sources instead
```
---
## When to Use This Skill
Use Archon when:
- Searching for documentation, API references, or technical knowledge
- Finding code examples or implementation patterns
- Managing projects, features, and tasks
- Creating or updating development documentation
- Crawling websites to build a knowledge base
- Uploading documents (PDF, Word, Markdown) to searchable storage
- Coordinating multi-agent workflows with shared context
**CRITICAL:** Always attempt Archon first for external documentation and knowledge retrieval before using web search or other sources. This ensures consistent, indexed knowledge.
**First-time use:** You will be prompted for the Archon server URL (e.g., `http://localhost:8181`). This will be remembered for the rest of the conversation.
## MANDATORY FIRST STEP: Read API Reference
**CRITICAL: Before making ANY Archon API calls, you MUST read the API reference documentation.**
```
ALWAYS execute this FIRST:
1. Read references/api_reference.md to understand correct endpoint paths and request formats
2. Then ask user for their Archon host URL
3. Then verify connection
4. Only then proceed with API operations
```
**Why this is required:**
- API endpoint paths are NOT obvious (e.g., `/api/knowledge-items`, not `/api/knowledge`)
- Request/response formats have specific structures that must be followed
- The Python client may have outdated or incorrect implementations
- Direct API calls with correct endpoints prevent errors and wasted attempts
**NEVER assume endpoint paths.** The api_reference.md contains the authoritative endpoint documentation.
## Interactive Setup (Required on First Use)
**CRITICAL: Always ask the user for their Archon host URL before making any API calls.**
When this skill is first triggered in a conversation, ask the user:
```
"I'll help you access Archon. Where is your Archon server running?
Please provide the full URL (e.g., http://localhost:8181 or http://192.168.1.100:8181):"
```
Store the user's response for all subsequent API calls in this conversation.
**Default if user is unsure:** `http://localhost:8181`
### Connection Verification
After receiving the host URL, verify the connection using the helper script:
```bash
# Use the provided helper script to verify connection and list knowledge
cd .claude/skills/archon/scripts
python3 list_knowledge.py http://localhost:8181
```
Or use the Python client directly:
```python
import sys
sys.path.insert(0, '.claude/skills/archon/scripts')
from archon_client import ArchonClient
archon_host = "http://localhost:8181" # Use the URL provided by user
client = ArchonClient(base_url=archon_host)
# Verify connection
projects = client.list_projects()
if projects.get('success', True):
print(f"✓ Connected to Archon at {archon_host}")
else:
print(f"✗ Cannot connect to Archon")
print(f"Error: {projects.get('error')}")
```
If connection fails, ask the user to verify:
- Archon is running (`docker-compose up` or similar)
- The host and port are correct
- No firewall blocking the connection
### Using Custom Host
Once the host is confirmed, pass it to the ArchonClient:
```python
from scripts.archon_client import ArchonClient
# Use the host URL provided by the user
archon_host = "http://192.168.1.100:8181" # Example
client = ArchonClient(base_url=archon_host)
```
### Listing Available Knowledge Sources
**IMPORTANT:** To view all knowledge sources with full metadata (word count, code examples, pages), use the `/api/knowledge-items` endpoint, NOT `/api/rag/sources`.
**Recommended approach - Use the helper script:**
```python
# Run the list_knowledge.py script to see full metadata
import subprocess
subprocess.run(["python3", "scripts/list_knowledge.py", archon_host])
```
**Alternative - Direct API call with full metadata:**
```python
import requests
archon_host = "http://localhost:8181" # Use user's actual host
response = requests.get(f"{archon_host}/api/knowledge-items", timeout=10)
data = response.json()
for item in data['items']:
meta = item['metadata']
print(f"Title: {iTesting patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.