docnify-automation
by ComposioHQdocnify-automation helps agents run Docnify tasks through Composio Rube MCP by discovering live tool schemas, checking the Docnify connection, and executing validated workflows.
This skill scores 64/100, which makes it an acceptable but limited directory listing. It gives agents enough trigger and setup guidance to use Docnify through Rube MCP, but directory users should expect a lightweight integration wrapper rather than a fully developed Docnify workflow pack.
- Valid frontmatter clearly identifies the skill as Docnify automation via Rube MCP and declares the required MCP dependency.
- Prerequisites and setup steps tell agents to verify RUBE_SEARCH_TOOLS, manage a Docnify connection, and confirm ACTIVE status before execution.
- The skill emphasizes searching tools first for current schemas, which reduces schema drift and helps agents trigger available Docnify tools more safely.
- No support files, scripts, references, README, or install command are provided beyond the single SKILL.md, so adoption guidance is thin.
- The workflow is mostly a generic Rube MCP discovery/connection pattern and provides little Docnify-specific task guidance or concrete examples.
Overview of docnify-automation skill
What docnify-automation does
docnify-automation is a Claude skill for running Docnify actions through Composio’s Rube MCP server. Its main value is not a fixed Docnify recipe; it teaches the agent to discover the current Docnify tool schema first, verify the Docnify connection, then execute the right Rube tool with validated inputs.
Best-fit users and jobs
Use the docnify-automation skill if you already use Docnify and want an AI agent to help create, update, retrieve, or manage Docnify-related work through the Composio toolkit. It fits teams that prefer MCP-based automation over manual dashboard work, especially when tool schemas may change and the agent must inspect live capabilities before acting.
Important differentiator: tool discovery first
The skill’s strongest operational rule is: call RUBE_SEARCH_TOOLS before execution. That matters because Rube returns current tool slugs, input schemas, execution plans, and known pitfalls. This reduces the risk of the agent guessing field names or using stale examples.
Adoption requirements and limits
This is a thin orchestration skill, not a standalone Docnify SDK. You need Rube MCP available, a Docnify connection managed through RUBE_MANAGE_CONNECTIONS, and an AI client that can call MCP tools. The repository path contains only SKILL.md, so expect guidance rather than helper scripts, templates, or local automation code.
How to Use docnify-automation skill
Install and connection context
If your skill client supports the common skills CLI, install with:
npx skills add ComposioHQ/awesome-claude-skills --skill docnify-automation
Then add Rube MCP as a server in your client configuration using:
https://rube.app/mcp
Before asking for a Docnify operation, confirm that RUBE_SEARCH_TOOLS is available. Then call RUBE_MANAGE_CONNECTIONS with toolkit docnify; if the status is not ACTIVE, complete the returned authorization flow.
Inputs the skill needs for reliable execution
A weak request is: “Update my Docnify docs.” A stronger docnify-automation usage prompt includes the exact Docnify goal, target object or workspace, desired change, constraints, and whether the agent may execute or should only prepare a plan.
Example:
“Use docnify-automation for Workflow Automation. First discover the current Docnify tools with RUBE_SEARCH_TOOLS. Check that my docnify connection is active. Then find the tool for updating a document title. If the schema supports it, update document DOC-123 title to Q1 onboarding checklist. Show the tool slug and inputs before executing.”
This gives the agent a discover-check-execute sequence and reduces accidental writes.
Practical workflow pattern
A safe docnify-automation guide looks like this:
- Search tools with
RUBE_SEARCH_TOOLSfor the specific Docnify task. - Reuse the returned session ID for follow-up discovery.
- Check connection status with
RUBE_MANAGE_CONNECTIONS. - Match your task to the returned tool slug and schema.
- Ask the agent to summarize planned inputs before write actions.
- Execute only after required identifiers, fields, and permissions are clear.
For read-only tasks, you can allow faster execution. For create, update, delete, or publish-style actions, require a preflight confirmation.
Repository files to read first
Start with composio-skills/docnify-automation/SKILL.md. It contains the complete operational contract: prerequisites, setup, tool discovery, connection checking, and the core workflow. There are no separate README.md, rules/, resources/, or scripts/ folders in the provided structure, so do not expect hidden implementation details elsewhere.
docnify-automation skill FAQ
Is docnify-automation an installable Docnify integration?
Not by itself. The docnify-automation skill is an instruction layer for an MCP-capable agent. The actual integration path is Composio’s Docnify toolkit through Rube MCP. Install the skill to guide the agent; connect Rube and Docnify to make it operational.
How is this better than an ordinary prompt?
An ordinary prompt may guess a Docnify API shape. This skill explicitly tells the agent to discover tools first, inspect live schemas, check connection state, and use the returned execution guidance. That makes it more suitable for production-like workflow automation where stale field names can cause failures.
Is it suitable for beginners?
It is beginner-friendly if you are comfortable connecting an MCP server and completing an OAuth-style connection flow. It is not ideal if you expect a one-click Docnify app, a local CLI, or prebuilt scripts. The key beginner habit is to ask the agent to show the discovered tool schema before running writes.
When should I not use this skill?
Avoid it when your task does not involve Docnify, when your client cannot use MCP tools, or when you need deterministic offline automation without live tool discovery. Also avoid using it for broad requests like “clean up everything” unless you can provide exact scope, identifiers, and approval rules.
How to Improve docnify-automation skill
Improve prompts by adding execution boundaries
The fastest way to improve docnify-automation results is to define what the agent may and may not do. Add phrases such as “read-only,” “do not execute until I approve,” “only update this document ID,” or “stop if the schema does not expose the required field.” These boundaries matter because Docnify actions may change live content.
Prevent common failure modes
Common failures include skipped tool discovery, inactive Docnify connection, missing object IDs, vague target names, and schema mismatch. Counter them by requiring the agent to report: the RUBE_SEARCH_TOOLS query used, the selected tool slug, the required fields, optional fields, and the exact payload it plans to send.
Iterate after the first output
After the first plan or tool result, refine with concrete corrections: “Use the tool that supports document metadata, not document content,” “retry with the existing session ID,” or “filter for documents created after this date.” Iteration works best when you keep the Rube session context and ask the agent to compare returned schemas rather than invent alternatives.
What to inspect before extending the skill
If you fork the repository, improve SKILL.md with task-specific prompt examples, approval rules for destructive operations, and sample RUBE_SEARCH_TOOLS queries for your most common Docnify workflows. Because the current skill has no scripts or references, the highest-impact enhancement is clearer decision logic, not more prose.