Playbooks
35 articles. 閱讀中文版
A/B Test Automation and Analysis
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'.
AI Resume Screening and Candidate Scoring System
Automatically process high-volume resumes, use AI to match candidates precisely and make hiring bias easier to find, and cut screening time by 75%
API Documentation Sync System: From Manual Maintenance to Code-Driven Docs Automation
The API changed but the docs did not, so frontend colleagues guess parameters and the test environment becomes trial and error. This article wires Apidog, Fern, Mintlify, and an LLM node into a documentation-sync pipeline: extract interface definitions from code, generate examples automatically, detect inconsistencies and alert — keeping the docs permanently aligned with the implementation.
Automated Code Review Assistant: From Manual Line-by-Line Checks to an AI-Assisted Quality Gatekeeper
Code review is time-consuming, edge cases slip through, and newcomers struggle to learn the team's conventions. This article wires GitHub Copilot, CodeRabbit, SonarQube, and an LLM node into an automated review pipeline: static analysis, security scanning, style checks, semantic understanding, and risk grading, so senior engineers handle only high-risk changes and the machine takes the repetitive work.
Contract Clause Extraction and Risk Flagging System: From Page-by-Page Reading to Structured Risk Navigation
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.
Smart Email Classification and Reply Template System
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 Automated Employee-Onboarding Checklist: Structured Guidance from Day One to Day 90
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.
Internal-Document Knowledge-Base Q&A System
Can't find company policies or process docs, and keep asking colleagues instead? This article shows how to build an intelligent Q&A system with AI + RAG, so employees ask questions right in their instant-messaging tool and get accurate answers with the source document attached. The focus is on designing for retrieval accuracy and a miss-tracking mechanism.
Microservice Dependency Map Generation: From Black-Box Architecture to Visualized System Understanding
Under a microservice architecture nobody knows the full call relationships between services; change one API and you cannot tell which downstream services it affects, and a newcomer needs weeks to grasp the system. This article wires Jaeger, Prometheus, LLM nodes, and a graphing library into a dependency-map pipeline: extract distributed-tracing data, build call relationships, identify critical paths, and generate an interactive map so architects and engineers understand the system structure fast.
Multilingual Document Translation and Localization Workflow
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.
Performance Review Data Aggregation and Visualization: From Fragmented Feedback to Structured Insights
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.
The SEO Keyword Research and Content Optimization Pipeline
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.
Social Media Content Calendar Management System
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'.
Technical Debt Tracking and Refactoring Prioritization: From Gut Feel to Data-Driven Decisions
Once technical debt piles up, the team falls into the vicious cycle of 'bugs that are never fixed and no time for new features'. This article wires SonarQube, CodeClimate, LLM nodes, and a custom dashboard into a technical-debt quantification pipeline: extract code metrics, assess business impact, compute refactoring ROI, and rank priorities, so the tech lead can use data to persuade the product manager to allocate refactoring time.
Automated Weekly-Report Generation System
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.
300-Page Annual Report to Structured JSON: The Three Statements and Segment Data
Building on the PaddleOCR field practice, this pushes 'reading out the text' to 'usable data': locating the financial-statement pages, handling cross-page tables, field mapping and unit normalization for the three statements and segment information, JSON schema design, and a complete pipeline and checklist for automated proofreading via reconciliation relationships.
Building Your Own Investment-Research System: The Complete Design from Skill Library to Verification Gates
Gathers the methods of all the previous articles into one complete blueprint: the five-layer architecture of data, skills, orchestration, verification, and output; the input/output contract design of the skill library; five gates from format validation to low-confidence abstention; why coverage and accuracy must be counted separately; the seven-step method for building an evaluation set; post-launch operations mechanisms; and the four-stage evolution path from a solo tool to a team system.
Building Organizational AI Capability: From Individual Heroes to Repeatable Processes
A few people in the company being great at using AI does not mean the organization has AI capability. This article is about turning personal experience into organizational assets: capability inventory, seed users and a three-stage rollout path, consolidating personal prompts into a skill library and a template library, tiered training and documentation, and the dimensions and common failure modes for measuring whether it really landed.
Contract Key-Clause Extraction and Risk Flagging: An Alternative to Page-by-Page Reading
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.
Turning Earnings Call Notes into Investment Key Points
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.
Build Your First AI Workflow Without Writing Code
You can build a usable AI application with your own hands even if you cannot write code. Using a customer-support knowledge-base Q&A as the scenario and no-code platforms like Dify, Coze, and n8n, this article walks you through six steps — prepare the data, build the knowledge base, set the prompt, test, launch, monitor — each concrete enough to follow, without depending on interface details that go stale.
Your First Research Workflow: From Question to Traceable Conclusion
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.
The Marketing Content Mass-Production Pipeline: From Topic Selection to Finished Draft
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.
Automatically Turning Meeting Notes into Assigned Action Items
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.
A Multi-Agent Investment-Research Pipeline: Collect, Analyze, Report in Three Stages
A single Agent cannot finish an investment-research task: the context blows up, attention scatters, and errors cross-contaminate. This article gives the complete design for a three-stage collect / analyze / report division of labor — each stage's responsibilities and negative list, the structured JSON contract between stages, the write-scope partition, failure isolation and contamination detection, where to place the verification gates, and the cost-and-latency trade-off framework, with an architecture diagram and a failure-mode list.
Resume Screening and Interview-Question Generation: Write the Criteria Down Instead of Going by Feel
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.
Wiring Existing SaaS into One Flow with n8n: From Manual Copy-Paste to Automation
Copying data between a CRM, spreadsheets, and messaging apps by hand is slow, lossy, and error-prone. This article uses n8n plus an LLM node to automate one process: how to inventory the manual data-moving points, pick a process, wire the nodes, and do error handling and failure alerts — and makes clear which stages should not go to the LLM and should use deterministic nodes instead.
A 30-Minute Quick Due Diligence Pass on One Company
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.
Build a Working Prototype Website in a Weekend: From Idea to Shareable Link
To validate a product idea you do not have to wait for engineering capacity. Using AI generation tools like v0, Lovable, and Bolt, this article walks you through converging the idea into a one-page spec, producing the first version, wiring a database and login, deploying to a shareable link, and putting it in front of real users — and spells out which requirements mean you should stop and find people instead.
MemoryHub v2.0 Full Record of Ten-Database Sync: The 6-Hour Battle from 0 Points to 3,892 Records
A complete record of MemoryHub's technical evolution from a monolithic Qdrant to a unified embedding pipeline across ten databases: 18 bug fixes, Python scoping traps, BGE-m3 dimension errors, missing system Python dependencies, and the final architecture that achieves one embedding then ten-database sync.
agentmemory Full Feature Deployment Log: From GitHub Trending to Four Platform Automatic Memory Capture
A complete record of the deep research into agentmemory (13K stars), full feature deployment, OMLX LLM integration, Qdrant dual memory sync, and automatic memory capture configuration for four major platforms: OpenClaw, Claude Code, Hermes, and DeepSeek TUI.
Complete Guide to 14 Financial Services AI Skills: From Deal Sourcing and M&A Models to Catalyst Calendars
A complete introduction to the 14 OpenClaw financial skills derived from Anthropic Claude for Financial Services, covering three major areas: investment banking (7), private equity (6), and equity research (1), including each skill's command meanings, use cases, and hands-on tutorials.
Ten-Day Pitfall Log: 16 Fatal Lessons in Building an AI Assistant System
Over the past 10 days, our AI assistant evolved from a conversational bot into a system-level AI assistant. This is the complete pitfall record: 16 pitfalls, 5 root cause patterns, 12 mandatory rules, and the meta-principles for system building distilled from them.
From Zero to Launch: Agentics Website Development Retrospective and Key Lessons
A complete development retrospective on building a Next.js knowledge platform from scratch with AI Agents (our AI assistant + Claude Code): architecture design, pitfalls, key decisions, and best practices for AI-assisted development.
Deploying ComfyUI on Mac M2: A Record of Pitfalls
A record of deploying ComfyUI from scratch on an Apple Silicon Mac, including PyTorch MPS configuration, model downloads, common errors, and solutions.