qlerify/download
plugins/mcp-companion/skills/download/SKILL.md
This skill should be used when the user asks to "save to file", "download", "export", "store in file", or any request that involves getting data from Qlerify and saving it locally. Bypasses AI processing and is ~100x faster than MCP tools for large data exports.
$ install --global
skillsauthnpx skillsauth add qlerify/qlerify-plugins downloadInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 16, 2026, 3:47 AM306.5s1 file scanned
SKILL.md
- name:
- download
- description:
- >
- allowed-tools:
- Bash, Read, Glob
Fast Download Qlerify Data
CRITICAL: When saving ANY Qlerify data to a file, use curl + jq instead of MCP tools. MCP responses pass through
AI context which takes minutes for large data. Shell pipes take seconds.
Step 1: Find MCP credentials
cat ~/.claude.json 2>/dev/null | jq -r '.mcpServers.qlerify // empty'
Extract the url and headers.x-api-key.
Step 2: Generic pattern for any MCP tool
curl -s "$MCP_URL" \
-H "x-api-key: $API_KEY" \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "TOOL_NAME",
"arguments": { ...ARGS... }
}
}' | jq -r '.result.content[0].text | fromjson' > output.json
Common examples
Full workflow → JSON file
curl -s "$MCP_URL" -H "x-api-key: $API_KEY" -H "Content-Type: application/json" \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_workflow","arguments":{"workflowId":"...","projectId":"..."}}}' \
| jq -r '.result.content[0].text | fromjson | .specification' > workflow.json
OpenAPI spec → YAML file
curl -s "$MCP_URL" -H "x-api-key: $API_KEY" -H "Content-Type: application/json" \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"generate_openapi_spec","arguments":{"workflowId":"...","projectId":"...","boundedContext":"..."}}}' \
| jq -r '.result.content[0].text' > swagger.yaml
Entities from workflow → JSON file
curl -s "$MCP_URL" -H "x-api-key: $API_KEY" -H "Content-Type: application/json" \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_workflow","arguments":{"workflowId":"...","projectId":"..."}}}' \
| jq -r '.result.content[0].text | fromjson | .specification.schemas.entities' > entities.json
Domain events from workflow → JSON file
curl -s "$MCP_URL" -H "x-api-key: $API_KEY" -H "Content-Type: application/json" \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_workflow","arguments":{"workflowId":"...","projectId":"..."}}}' \
| jq -r '.result.content[0].text | fromjson | .specification.domainEvents' > events.json
When to use what
| Data size | Method | Example |
|--------------------|-----------|-----------------------------------------|
| Small (< 50 lines) | MCP tool | list_workflows |
| Large (> 50 lines) | curl + jq | get_workflow, generate_openapi_spec |
| Any "save to file" | curl + jq | Always, regardless of size |
Finding IDs first
Use MCP tools for small lookups:
list_workflows→ get workflow ID and project ID
Then use curl for the actual data fetch.
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