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Don Durrett 2.1 Agent – End-User Quickstart Guide
Getting Started
Attach the Don-Durrett-2.1.md agent definition to your prompt.
Begin your session with a simple invocation:
Hey Don, evaluate {TICKER}Replace {TICKER} with the mining company symbol you want analyzed (e.g., ABX, GOLD, TSLA).
Example Prompt
[attach Don-Durrett-2.1.md]
Hey Don, evaluate ABRA.Suggested Follow-Up Questions
After the initial analysis, refine your inquiry with practical follow-ups:
“What are the top three risks in this company’s resource estimate?”
“How does management’s track record compare to peers?”
“What are the odds that management will dilute the shareholders in the next 6, 12, 24 months?”
“Show me the 10-factor radar chart again with commentary.”
“What valuation multiple is implied by your recommendation?”
“What recent news events could change the outlook?”
“Which metal price scenarios would flip your recommendation?”
“Highlight any red flags from the Data Quality Audit.”
“Summarize the top drivers behind your Upside score.”
Providing Feedback
To ensure consistent future performance:
Rate Accuracy: Provide a 1–5 star rating on how well the analysis matched your expectations.
Flag Errors: If you spot outdated data or missing citations, reply with:
Don, resource figures for {TICKER} look outdated—please check NI 43-101 dated {DATE}Suggest Improvements: Comment on chart readability, depth of reasoning, or question scope. Example:
Don, add a sensitivity table for metal prices next time.Log Session IDs: If your platform provides an execution ID, note it when giving feedback.
Consistent, precise feedback will help the Don Durrett Agent adapt and improve its data sourcing, audit rigor, and overall reliability.
Sample Output
The model output should look the following way. Based on the simple “evaluate” prompt.
Detailed User Implementation Guide
Don Durrett 2.1 Workflow Documentation Guide
This guide provides a clear explanation of every section found within the Don Durrett 2.1 markdown workflow for mining stock valuation agents. It covers each module’s function, why it exists, and practical reference for customizing and reviewing the workflow.
Workflow Sections & Functions
Agent Metadata:
Identifies the agent, version, model, and author references. Ensures reproducibility and clear methodology alignment.
Template & Mode:
Sets the input/output format and the allowed workflow customizations, enforcing standardization for each agent run.
Tools Declaration:
Lists all core features available: research, structured reasoning, charting, data quality auditing, and Python-driven calculations.
Calculation Library & Settings:
Activates the Python calculation library and mandates deterministic execution parameters, guaranteeing consistent analysis across repeated evaluations.
Workflow Steps:
Outlines each analytic stage, from research and primary source checks to resource validation and investor concern reporting—modularizing every critical operation in the valuation chain.
Audit System:
Mandates data quality audits for every output, instituting systematic checks on accuracy, hallucination risk, and sycophancy.
Step Messages & Instructions:
Provides explicit instructions for each workflow step, such as NI-43-101 reporting, share count verification, or investor red flag triggers.
Description & Guidelines:
Summarizes the agent’s purpose, operational guardrails, and feature guarantees, including transparency and reproducibility requirements.
Primary Source & Validation Rules:
Defines authoritative data sources and sets stepwise validation logic, mitigating risk from incomplete or outdated information.
Error Handling:
Specifies fallback routines for missing data, reporting penalties, and analysis halts if critical data cannot be verified.
10-Factor Valuation System:
Explains how each of Don Durrett’s 10 factors is scored, rationalized, and documented for mining stock analysis, building a standardized rating mechanism that drives the radar chart and final decision.
Mandatory Calculations & Formulas:
Lists all essential formulas used for resources, share structure, growth, financials, and risk—ensuring calculations are transparent and reproducible.
Visualization Integration:
Requires charting every 10-factor analysis as a mandatory radar visualization, giving a clear graphic of strengths and weaknesses.
Data Quality Audit & Scoring:
Implements structured checkpoints for factual accuracy and bias risk, flagging outputs for further review as needed.
Output Template & Compliance:
Precisely defines every required report section and the order for publishing, enabling quick validation for completeness and best practice compliance.
Input & Output Specifications:
Enforces minimum required data, mandates up-to-date pricing and sourcing protocols, and sets explicit output validation standards.
Examples & Usage Patterns:
Includes templates and examples for users seeking best practices or troubleshooting, aiding workflow customization and deployment.
Notes on Customization and Adoption
Each section is modular and can be adapted by advanced users for specialized analysis, with validation and error handling extensible to other sector workflows.
The workflow is compliant with best practice investment research and risk standards and is suitable for professional adoption.