Deep-reading-analyst-skill automates document analysis for operations teams. It extracts insights from large text volumes, supports decision-making, and integrates with workflows like document management systems and business intelligence tools.
git clone https://github.com/ginobefun/deep-reading-analyst-skill.gitDeep-reading-analyst-skill automates document analysis for operations teams. It extracts insights from large text volumes, supports decision-making, and integrates with workflows like document management systems and business intelligence tools.
[{"step":"Prepare your document. Clean the text (remove headers, footers, or irrelevant metadata) and save it as a .txt or .pdf file. For large datasets (e.g., CSV/Excel), extract the relevant columns (e.g., feedback comments, survey responses) into a single text block.","tip":"Use tools like Python (Pandas) or Excel’s ‘TEXTJOIN’ function to concatenate text data. For PDFs, use Adobe Acrobat’s ‘Export Text’ feature or OCR tools like Tesseract if the document is scanned."},{"step":"Customize the prompt. Replace [DOCUMENT_TYPE], [COMPANY_NAME], and [SPECIFIC_TOPIC] with your context. For example: '[DOCUMENT_TYPE] = internal audit report', '[COMPANY_NAME] = GreenTech Solutions', '[SPECIFIC_TOPIC] = financial compliance gaps'.","tip":"Be specific about the topic to avoid generic outputs. For instance, instead of 'risks,' specify 'compliance risks related to GDPR Article 6' for precision."},{"step":"Run the analysis. Paste the cleaned document content into your AI tool (e.g., Claude, ChatGPT) and execute the prompt. For large documents (>500KB), split the text into chunks and analyze sections separately, then synthesize the results.","tip":"Use tools like LangChain or LlamaIndex to automate chunking and summarization for very large documents. For structured data (e.g., spreadsheets), pre-process the data to extract only the text columns you need."},{"step":"Review and refine. Validate the AI’s insights against your domain knowledge. If gaps exist (e.g., missing context), provide additional details or ask follow-up questions like, 'Did you consider the impact of seasonal demand on shipping delays?'","tip":"Cross-check 2-3 key insights with manual reviews or existing reports. For example, compare the AI’s durability complaints with your product team’s defect logs."},{"step":"Integrate into workflows. Export the AI’s recommendations into your document management system (e.g., SharePoint, Notion) or BI tool (e.g., Tableau, Power BI) for tracking. Assign tasks to team members using tools like Asana or Jira.","tip":"Use the AI’s output to auto-generate tickets or reports. For example, create a Trello board with each recommendation as a card, tagged with priorities and deadlines."}]
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/ginobefun/deep-reading-analyst-skillCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Act as a deep-reading analyst. Analyze the following [DOCUMENT_TYPE] for [COMPANY_NAME] or [PROJECT_NAME]. Extract key insights on [SPECIFIC_TOPIC], such as trends, risks, compliance gaps, or opportunities. Provide a summary with bullet points and highlight 3-5 actionable recommendations. Document type examples: contract, regulatory filing, customer feedback dataset, internal audit report, or market research study. [PASTE_DOCUMENT_CONTENT]
### Deep Reading Analysis Report: Acme Corp Q2 2024 Customer Feedback Dataset **Document Type:** Customer feedback dataset (1,247 entries) from Acme Corp’s post-purchase surveys (June-August 2024). **Key Insights:** - **Product Quality:** 38% of negative feedback (472 entries) cited durability issues with the "ProMax 3000" model, particularly in high-temperature environments. This represents a 12% increase from Q1 2024. - **Shipping Delays:** 22% of complaints (274 entries) mentioned late deliveries, with 68% of these tied to the Midwest distribution center. Average delay: 4.2 days (up from 2.8 days in Q1). - **Customer Service:** Response time to complaints improved by 18% (now averaging 2.1 hours), but 15% of customers still rated service as "poor" due to unresolved issues. - **Opportunity:** 14% of positive feedback (175 entries) highlighted the "EcoWrap" packaging as a differentiator, suggesting potential for a sustainability-focused marketing campaign. **Risks:** - **Revenue Impact:** Durability complaints correlate with a 7% increase in product returns (Q2 vs. Q1), costing Acme Corp an estimated $1.2M in Q2. - **Compliance:** 3 entries flagged potential regulatory violations (unlabeled hazardous materials in packaging), requiring immediate review by the legal team. **Actionable Recommendations:** 1. **Product Team:** Prioritize a durability review of the ProMax 3000, focusing on high-temperature stress tests. Target resolution by October 15, 2024. 2. **Logistics:** Audit the Midwest distribution center’s capacity and staffing. Consider temporary third-party logistics (3PL) support for peak seasons. 3. **Customer Service:** Implement a follow-up system for unresolved complaints within 48 hours. Assign a dedicated team to high-priority cases. 4. **Marketing:** Launch a "Sustainable Packaging" campaign in Q4, leveraging EcoWrap feedback. Coordinate with the product team to highlight durability improvements. 5. **Legal:** Schedule a compliance audit for packaging materials within 30 days to address regulatory flags. **Confidence Level:** High (dataset size and structured feedback ensure reliability).
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