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- everyday ai workflow assistant
everyday ai workflow assistant
Transforms messy notes and vague goals into structured plans, analysis, and polished deliverables.
$5
Works with the AI Agents you already use
everyday ai workflow assistant
Example session with this skill installed
I have these notes from a client call about a new website project. They want a portal for their vendors, login for staff, and a way to track shipments. Needs to be secure and launch by Q3.
- Read your context and instructions
- Compiled the everyday ai workflow
I have processed your notes to establish a project framework for the Vendor Portal.
Project Roadmap: Vendor Portal Launch
| Phase | Task | Requirement | Deadline |
|---|---|---|---|
| Discovery | Security Architecture Audit | Define auth protocols (SSO/MFA) | July 15 |
| Development | Vendor & Staff Modules | Role-based access control (RBAC) | August 10 |
| Integration | Shipment Tracking API | Real-time carrier data sync | August 25 |
| Testing | UAT & Security Pentest | Vulnerability assessment | September 10 |
| Launch | Production Deployment | Q3 Go-live | September 30 |
Inferred Constraints
- Security: Non-negotiable due to vendor/staff data handling.
- Timeline: Hard deadline by end of September (Q3).
- Audience: External vendors and internal staff users.
Actionable Next Steps
Approve Timeline: Confirm if the September 30th go-live date aligns with the client's internal marketing launch.
*
Define Logistics: Identify which shipping carriers (e.g., FedEx, UPS) need API integration for the tracking module.
*
Technical Stakeholder Sync: Schedule a 30-minute call to review the proposed RBAC (Role-Based Access Control) structure.
Open Questions
- Do the vendors need to upload documents (invoices, insurance), or is the portal read-only for shipment tracking?
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
Vague requests and messy notes often require manual parsing before you can even start working. You lose time trying to turn unstructured thoughts into actionable plans, tables, or polished drafts.
What it does
- Infers core intent and success criteria from ambiguous or low-context prompts.
- Selects the optimal workflow, from root-cause analysis to hierarchical categorization.
- Separates signal from noise in messy input documents or brainstorm logs.
- Produces structured deliverables like phased plans, comparison tables, and scored evaluations.
- Validates every output for actionability, ensuring the user knows exactly what to do next.
Why this beats prompting it yourself
Generic prompts often result in conversational filler or incomplete analysis. This skill enforces a rigorous five-step execution process, including implicit context inference and a pre-delivery validation check, ensuring outputs are structured for professional use rather than just chatting.
Use cases
- Convert meeting transcripts into structured action items with deadlines and owners.
- Transform a list of raw product ideas into a phased development roadmap.
- Perform a pros-and-cons analysis on tool sets with a final scored recommendation.
- Condense lengthy technical documentation into executive summaries and key takeaways.
Known limitations
Does not handle real-time browser automation or multi-agent delegation. Not a replacement for specialized coding or deep UI design skills.
Requires access to standard search/file tools for external data. Cannot perform multi-agent orchestration or real-time browser control. Specific to logical processing and content transformation.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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- Software
Python