Claude

A curated set of battle-tested Claude Code system prompts, covering common scenarios such as code review, refactoring, and documentation generation.

Contracts are long and dense; reading them page by page is slow and easy to miss things. This article uses PaddleOCR to process scans, Claude to locate and transcribe clauses, and NotebookLM to compare against your standard template, producing a clause comparison table that carries source locations and flags deviations. This is not legal advice — the final call belongs to qualified legal counsel.

Turn a one-hour earnings call into a usable research record: get the transcript, break down the management presentation and the Q&A, compare guidance changes, spot wording changes, distinguish factual statements from evasive answers, and finish with a source-annotated key-points page and a to-verify list.

Starting from one vague research question, build a complete workflow that ends in a traceable conclusion: break it into verifiable sub-questions, assign independent sources to each, record findings on evidence cards, cross-verify, mark uncertainty, and finally produce a cited conclusion page.

Anyone can write one piece of content with AI; the hard part is stable mass production. This article breaks marketing content production into a pipeline: where topics come from, how to use a style guide as the system prompt to lock in style, how to derive versions for multiple platforms, where to place human review, how to troubleshoot unstable quality, and how to measure whether the line is really producing.

When a meeting ends and its action items sink without a trace, that is one of the most common hidden drains in an organization. This article breaks down the complete automation of record and transcribe → extract action items and owners → push assignments → auto-recap before the next meeting, focusing on how to design the human-confirmation step so the system saves effort and stays trustworthy even when extraction accuracy is imperfect.

The common problem in resume screening is not slowness but that the criteria live in the interviewer's head — inconsistent and hard to articulate. This article turns the hiring criteria into a document that can actually be judged, then uses Claude, NotebookLM, and Dify to extract fields, match against the criteria with the original text as evidence, and generate interview questions, while holding the line on human decision-making and compliance boundaries.

A 30-minute quick due-diligence process you can follow as written: what to check in each of five phases, where to check it, what counts as a red flag, and what to record — closing with a one-page summary template. The goal is not an investment conclusion but judging whether a company deserves deep research.

Breaks the investment research process into seven stages and judges one by one which can be handed to AI, which AI can only assist with, and which cannot be given up one step — with a ready-to-apply division-of-labor table and an implementation checklist that help you draw AI's capability boundary and responsibility boundary clearly.

They are all called AI, yet their capabilities and cost structures differ enormously. This article breaks the tools on the market into three levels — chat, collaboration, and agent — and gives you a decision table to first clarify which level a need belongs to, then talk tool selection and budget, so you avoid spending money on the wrong tool.

The cost of AI hallucination in financial scenarios is severely underestimated. This article dissects the three most expensive mistakes — fabricating numbers, confusing periods and companies, and filling gaps with inference — explains what causes each one, and gives four implementable lines of defense: mandatory citation, separate statistics, low-confidence abstention, and human review.

How do you build an automated A/B testing pipeline — from hypothesis generation, experiment design, and traffic allocation to statistical-significance judgment and applying results? This article teaches you to build a data-driven optimization engine that puts every page element through scientific validation. The focus is on 'avoiding common statistical traps' and 'scaling experiments'.

The most expensive thing about contract review is not the time — it is the miss. This article uses Ironclad, DocuSign, and AI together to build automatic extraction, standard comparison, and risk grading for seven categories of key clauses, so the legal team spends its time where the real differences are.

Drowning in email every day? This article teaches you how to use AI to classify incoming messages automatically, match the best reply template, and generate drafts for you to confirm and send quickly. The focus is on designing the 'confidence threshold' and the 'human review mechanism', so the system saves time without ever sending the wrong thing.

The most expensive part of onboarding is not time, it is the things that slip through. This article uses Workday, BambooHR and AI to build an automated checklist running from offer signature to the end of probation, so every new hire gets a consistent experience and every task has an owner and a due date.

Need to translate product docs, marketing materials or internal standards into multiple languages? This article shows how to build an end-to-end translation workflow with AI plus an automation platform, automating everything from content creation to multilingual publishing. The focus is on balancing AI translation speed against human proofreading quality.

The most expensive thing about performance reviews is not the time — it is the bias. This article uses Lattice, 15Five, and AI together to build automatic aggregation of multi-source feedback, theme extraction, and trend visualization, so managers decide based on data instead of impressions.

How do you build an automated SEO keyword research system — from discovering opportunities, analyzing competition, and generating content outlines to tracking rank changes? This article teaches you to build an end-to-end content optimization workflow that makes every article hit the search intent precisely. The focus is on a 'data-driven content strategy' rather than guesswork.

Struggling every day over what to post? This article teaches you how to use AI to generate content ideas automatically, schedule publishing, and optimize strategy from the data. The focus is on designing 'content quality control' and 'the balance between automation and human creativity'.

Spending 3-4 hours every week collecting data, organizing progress and writing the report? This article shows how to use AI to pull data automatically from tools like Jira, Salesforce and Notion and generate a structured weekly-report draft, so you spend just 15 minutes confirming it and adding insight. The focus is on designing the balance between integrating data sources and adding human value.