How to combine Codex and Claude Code with one plugin

Automating Excel and Google Sheets reporting is no longer exclusive to software engineers. With the rapid evolution of AI models like GPT, office workers can now create custom workflow automation tools using simple instructions with Codex, freeing up hours of repetitive daily tasks. Why Excel Formulas and VBA Are No Longer Enough For weekly recurring reports or automated integrations with email, Slack, and messaging apps, traditional methods like nested Excel formulas or recording VBA (Visual Basic for Applications) macros require specific technical skills and break easily whenever a single column in the source file changes. This is the exact gap that OpenAI Codex fills: you describe precisely what needs to be done in natural language, and Codex generates complete Python or Google Apps Script code ready to run in seconds. Codex Is Not a Single Product A common misconception is how Codex is accessed: it can be used via CLI in the terminal, IDE extensions in VS Code, Codex Web on the cloud at https://chatgpt.com/codex/cloud for developers and coding enthusiasts, as well as desktop applications for both macOS and Windows. For office workers unfamiliar with command-line tools, the easiest way to start is downloading the Codex desktop app. It lets you manage multiple agents simultaneously right from a visual interface without opening a terminal or configuring API keys—just log in with your existing ChatGPT account. What an Example Prompt Looks Like You only need to provide Codex with a detailed prompt like: "Write a Python script that reads the Excel file 'sales_raw.xlsx', filters orders with status 'Completed', calculates total revenue by branch, and exports the result to 'revenue_report.xlsx' with dark blue header styling." Codex will instantly generate standard, high-quality code, and you simply run the script to get your finalized report. Automating Local Excel Reports with Python and Codex For Excel files stored locally, combining Codex with two popular Python libraries—pandas and openpyxl—delivers outstanding processing speed. Pandas handles hundreds of thousands of data rows in seconds, while openpyxl manages cell formatting, header colors, and formula insertion as illustrated in the example above. Automating Google Sheets in the Cloud When your team collaborates on Google Sheets instead of offline Excel files, the Python library gspread or Google Apps Script is the ideal choice. Codex can write code that connects directly to the Google Sheets API via a Service Account (JSON credential file) to read and write data continuously without opening a browser. A Sample Workflow Automatically pull new form submissions from Google Forms into the spreadsheet Automatically categorize customer feedback by priority Automatically send daily summary emails to leadership at 17:00 This entire pipeline runs in the background with zero manual intervention once configured. 4-Step Implementation Process for Non-Coders Step 1 - Standardize input data: Ensure the Excel or Google Sheets file has clean, clear headers without arbitrary merged cells. Step 2 - Write clear prompts for Codex: Explicitly mention file names, column names, filtering/calculation steps, and desired output formats. Step 3 - Test and paste errors for AI auto-debugging: If the script encounters an error, copy the full error traceback and paste it back into Codex for automatic correction. Step 4 - Schedule automatic runs: Use Windows Task Scheduler (Windows) or Cron jobs (macOS/Linux) to run the script automatically on a set schedule. Risks and Considerations Before Handing Reports Over to AI Codex is powerful but not completely free; however, simple operations with Excel or Google Sheets consume very little quota, so light users can comfortably rely on the free tier. If heavier workloads are required, consider Plus or Pro plans—avoid the Go tier as Codex capabilities there offer little advantage over the free plan. Never paste system passwords, financial records, or real customer data into public AI chat interfaces. When asking Codex to generate code, always substitute sensitive details with dummy data of the same structure. For critical validations such as monetary amounts or tax formulas, do not trust AI-generated code blindly on the first run. Always cross-check the output during the first 1-2 executions to ensure the logic perfectly matches your actual business requirements. Automating reports with Codex does not make you a programmer, nor should it. The true value lies in understanding your own data and business processes well enough to describe them clearly to AI—Codex handles the coding. If your company has weekly recurring reports, start today by picking the simplest one, writing a prompt following the sample above, and running your first automated test.

Sol, Terra, and Luna make GPT-5.6 look more like a product family than a single model. The naming also signals what OpenAI is trying to change: users no longer have to choose only between an expensive flagship and a much smaller model. Instead, they get three capability tiers designed for different workloads. The important caveat is that GPT-5.6 is currently in limited preview, and OpenAI says it is not available in ChatGPT during this preview period.On the other side, Anthropic positions Claude Fable 5 as a frontier model for reasoning, software engineering, scientific research, and long horizon agentic work. The useful question is therefore not simply which model is smarter. It is which product architecture helps a team complete real work with predictable quality, latency, and cost.What GPT-5.6 actually isAccording to OpenAI's preview announcement, GPT-5.6 consists of Sol, Terra, and Luna. Sol is the flagship and most capable option, Terra is a strong lower cost model, and Luna is the fastest and most cost efficient member of the family.The important change is how OpenAI divides demand into three tiers. A research team might use Sol for a difficult reasoning problem, a product team might run most daily work on Terra, and a high volume system might use Luna for thousands of short requests. This looks more like an infrastructure strategy than the launch of a single new chatbot.Availability matters: OpenAI says GPT-5.6 is not available in ChatGPT during the preview. An experience in an API, developer tool, or partner platform should not be treated as the final ChatGPT experience.Sol is designed for difficult, extended workSol is positioned as the strongest GPT-5.6 model for deep reasoning, complex coding, and long multi step tasks. A software team might ask it to understand a repository, identify the cause of a bug, propose a minimal patch, and write regression tests. Sol's value is not answering a short question quickly. It is maintaining the objective while working through a longer chain of decisions.OpenAI also highlights stronger cyber capability as reasoning increases. That can be useful for authorized security testing and vulnerability analysis, but it also makes access controls, logging, sandboxing, and human approval more important.Terra aims for the practical middleTerra targets the broadest category of work: document analysis, content production, application development, research synthesis, and operational support. If Sol is the specialist called for the hardest problem, Terra is the strong team member expected to work throughout the day without making every request unnecessarily expensive.A marketing team could use Terra to read market reports, extract insights, build an outline, and draft several content variants. A development team could use it for code review, test generation, and tickets with a clear scope. This tier could become the default if its real world quality remains consistent.Luna prioritizes speed and scaleLuna is designed for low latency and lower cost. Classification, conversation summaries, field extraction, drafting, and ticket routing do not always require the strongest model. In these cases, response time and total operating cost matter more than maximum reasoning capability.Fast does not mean suitable for everything. If a task requires source verification, a long plan, or a code change with a large blast radius, a team should move it to Terra or Sol instead of forcing Luna beyond its intended role.Claude Fable 5 takes a different routeAnthropic presents Claude Fable 5 as a frontier model for reasoning, software engineering, vision, scientific research, and long horizon agentic work. Instead of emphasizing three product tiers in one generation, Anthropic's message focuses on the capability of a powerful model working inside the Claude ecosystem.This difference changes deployment decisions. With GPT-5.6, an engineering team might build a router that sends each request to Sol, Terra, or Luna. With Fable 5, the focus may be on optimizing prompts, tools, context, and reasoning budgets around one primary model. Neither approach is universally better because the answer depends on workload and operational maturity.A fair comparison: Do not run one prompt and declare a winner. Build a test set covering short tasks, long reasoning, coding, extraction, and recovery from errors. Measure accuracy, latency, the number of human corrections, and the total cost of a completed task.Coding and agentic work depend on the surrounding toolsBoth GPT-5.6 Sol and Claude Fable 5 target complex software work, but the practical experience depends heavily on the system around the model. The ability to read a repository, execute commands, observe results, and correct mistakes can matter as much as a benchmark score. For OpenAI workflows, the Codex page is a useful starting point for understanding how a model participates in coding work.Fable 5 may be attractive to teams already invested in Claude and long running agentic workflows. Read our Claude Fable 5 coverage for more context on Anthropic's positioning and the types of work it targets.What early forum experience tells usEarly discussions on Reddit and developer communities focus on how different Sol, Terra, and Luna feel in real work. Some users describe Sol as the better fit for multi step tasks, Terra as the practical option for routine work, and Luna as the interesting choice for speed. These observations match OpenAI's positioning, but they do not establish a precise quality gap.Forum reports are useful because they reveal the questions real users care about. However, they are self selected evidence. People may use different prompts, access levels, integrations, and preview versions. A result from a developer platform does not guarantee the same result when a model eventually appears in ChatGPT.Early positivesThe three tiers make it easier to understand which model belongs to which workload.Luna creates a clear expectation of low latency for high volume systems.Terra could become a default if it delivers stable quality at a practical cost.Sol is expected to be stronger for coding, long reasoning, and tasks with several verification steps.Open questionsHow large the practical quality gap between Sol and Terra will be on common workloads.The total cost after retries, corrections, and human review are included.How Luna behaves with long prompts and many constraints.Whether performance remains stable as GPT-5.6 expands beyond preview access.Forum reports are not benchmarks: Community experience should help you choose test cases, not make a production purchasing decision by itself.Comparing GPT-5.6 and Fable 5 by workloadWriting and document analysisTerra appears positioned for most document work because it balances capability and cost. Fable 5 may be attractive when documents are long, questions are complex, and the model must maintain an argument across a large context. A useful evaluation should score citation accuracy, structural consistency, and how much editing is required before publication.Software development and debuggingSol and Fable 5 are both candidates for difficult coding tasks. A representative test should include reading existing code, identifying the root cause, producing a minimal fix, writing tests, and explaining risk. Asking a model to create an isolated function from scratch does not reflect how well it works in a real repository.High volume processingLuna has the clearest positioning advantage when speed and cost dominate. At thousands of extraction or classification requests per day, a small difference in price and latency can have a large effect. Fable 5 may be unnecessarily expensive for a workload that only needs short, structured outputs.Research and long reasoningSol and Fable 5 should be compared with tasks that have verifiable outcomes rather than open questions that merely sound impressive. Give both models the same research material and ask them to identify assumptions, detect contradictions, propose an experiment, and explain what evidence is missing. The better model is the one that helps users discover errors faster, not the one that writes the longest answer.Should you choose Sol, Terra, Luna, or Fable 5?If you want maximum capability inside the OpenAI ecosystem, Sol is the first model to test. If you need a strong model for regular use, Terra has the more practical position. If your workload contains many short and repetitive tasks, Luna could reduce operating cost. Fable 5 remains relevant for teams invested in Claude or focused on long reasoning and agentic work.Because GPT-5.6 is still in preview, replacing an entire production workload would be premature. Run the models in parallel on real but sanitized data, record failures, and use the same criteria for every candidate.A test plan you can use nowSelect 20 tasks that represent real work, including easy and difficult cases.Run each task on Sol, Terra, Luna, and Fable 5 when access allows.Score accuracy, response time, total cost, and required human correction.Track severe failures separately instead of relying only on averages.Choose a model for each workload category rather than forcing one model to do everything.Is GPT-5.6 worth switching to now?The most important change in GPT-5.6 may not be Sol's raw capability. It is OpenAI's decision to turn one model generation into three operational tiers. That could help organizations control cost, but only if they can classify workloads and route requests intelligently.The practical next step is to build a small benchmark from your own data. If Sol wins difficult tasks, Terra is good enough for routine work, and Luna handles high volume requests reliably, the three tier architecture has real value. If Fable 5 remains more consistent on long reasoning, a multi model strategy may still be better than committing to one provider.

You're out and suddenly remember a small detail in your project that needs fixing — you don't have to open your laptop or remote desktop in. With the right connection set up, ChatGPT app on your phone can become a control panel for Codex, while your computer at home or the office keeps running the actual code. ChatGPT app doesn't run Codex on your phone The easiest thing to misunderstand is thinking Codex is running directly on your phone. In reality, your phone only sends prompts, replies, approvals and follow-up messages, while the actual working environment lives on your Mac or Windows machine running Codex. In other words, ChatGPT app is the remote controller, and the host machine is where your repo, terminal, credentials, plugins, MCP servers and other tools actually live. This makes complete sense because codebases typically live on your development machine, not your phone. When you send a request like fixing a TypeScript error, running tests or checking a diff, Codex processes it inside the selected project on the host and sends results back for you to review. If you want to understand the foundation before using remote access, check out What is Codex and how to use Codex to get a clear picture of where this tool fits in your workflow. What do you need before connecting ChatGPT app to Codex? According to the latest Codex documentation from OpenAI, ChatGPT app supports controlling Codex on both macOS and Windows, though Linux is not supported yet. Notably, this feature works with all ChatGPT account types, including Free and Go — no paid plan required. You only need to make sure you're signed into the same account or workspace on both devices: ChatGPT mobile (latest version on iOS or Android) and Codex (latest version on your host machine, online and running). Your host machine must stay on and Codex must keep running for the entire time you're controlling it remotely. If the machine goes to sleep, loses its connection or Codex is closed, the connection from your phone drops immediately and any tasks in progress may be interrupted. What's worth noting is that the entire setup process starts from Codex App on the host machine and is surprisingly simple — just scan a QR code and you're done. Inside Codex App, select the mobile setup option in the sidebar, scan the QR code with your phone, then complete the confirmation in ChatGPT app. For enterprise workspaces, an admin may need to enable Remote Control permissions before you can connect. This QR code grants control over your computer, so keep it private and never share it with anyone to avoid unauthorized access to your machine. To summarize, connecting ChatGPT app to Codex is straightforward: Host machine must be online and running Codex ChatGPT app and Codex must be signed into the same account or workspace Generate the QR code in Codex on the host and complete setup on your phone MFA, SSO or passkey requirements may still apply depending on your workspace What can you do once connected? Once the host appears in Codex on your phone, you can start a new thread inside a project on the host or pick up an existing one. This is where the experience becomes genuinely useful: you can send follow-ups, answer Codex's questions, approve commands, view output, check diffs, review test results and even receive notifications when a task finishes or needs your attention. A real example: you're at a coffee shop and remember the login form has a validation bug. You open ChatGPT app, select the connected host, and ask Codex to check the auth flow, fix the email validation error and run the related tests. Codex works directly on the repo sitting on your host machine, while you review the results, approve actions when needed and decide whether to request further changes. This is also why people are starting to think of Codex and other AI-powered IDEs as a colleague working inside a real environment, not just a code suggestion tool anymore. Its strength lies in reading files, running commands, editing code and maintaining context across multiple rounds of back-and-forth. Limitations to keep in mind when using Codex from your phone Remote control depends entirely on the host machine — if your computer goes to sleep, loses its connection, closes Codex or gets signed out of the workspace, your phone loses its working environment immediately. That said, if Codex is mid-task when the connection drops, it will continue running on the host and notify you once your phone reconnects, so there's less to worry about if your phone suddenly loses signal during a running task. One more thing to note: on Windows, tasks using Computer Use require an appropriate foreground session, so this setup is not a complete replacement for sitting directly in front of your machine. It also helps to draw a clear line between handing off a focused task and reviewing large changes. Your phone works well for small bugs, running tests, quick questions about a specific file, reviewing short tasks or checking task status. However, anything requiring a high level of attention should still be reviewed on a larger screen to avoid missing details. How to use it effectively in practice The most effective approach is to hand off tasks with a clear scope and specific expected outcomes. Instead of saying "fix the login", describe exactly where the error occurs, what the expected behavior should be after the fix, which tests to run and which parts of the codebase to leave untouched. Codex performs better when it knows the boundaries of a task, especially since remote mobile means each feedback loop takes longer than when you're sitting right at your machine. A clean working rhythm might look like this: describe the task in detail whether small or medium-sized, ask Codex to read the relevant files, let it propose a solution, only approve when necessary and wait for the result report. Once you get used to this rhythm, you'll find that idle time outside can handle real work — while keeping the final decision firmly in your hands. Compared to Claude Code Remote and Telegram bot There are many ways to control an AI coding agent from your phone, though the three most common approaches each serve a different need. Criteria ChatGPT app + Codex Claude Code Remote Telegram + Codex Natural conversation ✅ Excellent ✅ Good ❌ Requires exact syntax Granular control Moderate Highest Low Connection stability Stable Stable Frequent drops Mobile UI Well optimized Not fully optimized Uses existing Telegram app Initial setup Easy, scan QR Easy Requires manual bot configuration Computer must stay on ✅ Required ✅ Required ✅ Required Claude Code Remote Control offers the strongest level of control — you get direct terminal output, can intervene mid-task and generally feel much closer to what the agent is doing. That said, the UI on small phone screens isn't fully optimized yet, and some interactions are still difficult to perform without a physical keyboard. Telegram bot has the advantage of not requiring a separate app and is easy to get started with, but the real-world experience has clear limits: it's prone to slowdowns, occasional silent disconnections mid-task, and because it lacks genuine AI context, anything slightly more complex than a simple command quickly falls apart — forcing you to type precise instructions rather than describe what you need naturally. ChatGPT app + Codex sits at the best balance point for most users — smooth enough, smart enough, quick to set up with a QR scan and no new syntax to learn before you can get to work. Connecting ChatGPT app to Codex doesn't turn your phone into a development machine — it turns your phone into a control surface for a development machine that's already ready to work. As long as the host stays on, permissions are configured correctly and the task is scoped tightly enough, this is the most practical way to handle real coding work when you're away from your laptop.

Token management is a hot topic in the AI community. In summer 2026, two open-source skills for saving tokens are being widely discussed: Ponytail cuts generated code lines by up to 54%, while Caveman slashes agent response tokens by 65%. Both target a familiar pain point for users of AI coding agents like Claude Code, Codex, or Gemini CLI—ballooning token costs—yet solve it from completely different angles: one trims unnecessary code, while the other trims unnecessary words.The Token Waste Problem in the AI Agent EraToday's AI Agents do not just answer single prompts; they operate in autonomous agentic loops by reading files, analyzing project structures, writing code, running builds, and checking for errors. Throughout this process, tokens are wasted mainly across three channels:Over-engineering (Excess Code): Instead of using native language or browser features, agents frequently install extra dependencies or construct unnecessarily complex components.Input Overhead (Excess Context): Build logs, JSON payloads, search results, and instruction files (SKILL.md) consume tens of thousands of input tokens on every API call.Output Bloating (Verbose Prose): Agents explain basic concepts at length before delivering the core answer.Ponytail: Turning your AI Agent into a "Lazy Senior Dev"Created by Dietrich Gebert, Ponytail is designed with a core philosophy: "The best code is the code you never wrote." Ponytail forces an AI Agent to think like a seasoned senior developer who always looks for the simplest, lowest-effort solution.Self-Questioning Ladder Before Writing CodeBefore touching any code, Ponytail requires the agent to run through a self-questioning ladder:YAGNI: Is this feature really necessary? If not, skip it immediately.Reusability: Is there an existing function or component in the codebase?Standard Library: Can the language's standard library handle it?Native Platform: Is it supported natively by the browser or OS? (e.g., using a native <input type="date"> instead of installing a heavy Flatpickr library)Installed Dependencies: Can already installed packages in package.json resolve it?One-liner: Can it be written in a single line of code?Only when the above steps fail will the Agent proceed to write the minimal working code required.Do Benchmark Results Match Reality?In tests using Claude Code (Haiku 4.5) on a full-stack FastAPI + React template, Ponytail reduced lines of code by 54% while maintaining 100% application safety.This figure was published by the author after the community pointed out baseline flaws in the initial benchmark (which claimed 80-94% reductions), so it should be treated as a reference signal rather than an independently verified statistic.Caveman: A Token Compression Ecosystem, Not Just a SkillWhile Ponytail targets generated code, Julius Brussee's Caveman attacks both agent input and output with the catchy slogan: "why use many token when few token do trick". Current Caveman is no longer a single skill, but a multi-layered toolkit.Caveman Proxy: Compressing Input DataA local proxy sits between the Agent and the API provider, automatically routing all traffic. It detects payload types like JSON, error logs, git diffs, or search results, compressing them to keep only essential content needed for the answer, while saving copies on disk for byte-exact recovery when required. In a 54-run pinned benchmark on Claude Code, this mechanism used 33.2% fewer input tokens than direct runs while passing all exact-answer checks.Caveman Skill: Compressing Response LanguageThis part returns agent communication to primal caveman-speak: dropping conversational filler and getting straight to the point without altering code or command integrity. Code, commands, and error logs remain byte-exact, with only explanatory prose compressed.Pixel Mode: Rendering Skill Files to ImagesVerbose SKILL.md files are rendered into PNG images upon skill installation, leveraging vision capabilities of modern LLMs to lower prompt token loads. Measured on Caveman's own skill file, this reduced size from ~1,069 down to an estimated 415 tokens, or 61%.The 61% reduction was measured on a single case (Caveman's own SKILL.md), not as an average across all skill files—actual results will vary based on file length and structure.Caveman Learn: Self-Diagnosing Token BottlenecksThe caveman learn command automatically reads local agent session history (running locally without accounts), scores the current setup, and pinpoints exact token sinks for user remediation.Numbers to Keep in Mind Reading Caveman MarketingCaveman's official documentation includes an "honest number warning": Caveman Skill alone reduces output tokens, while input and reasoning tokens remain mostly unchanged unless the proxy is enabled, adding ~1,000-1,500 input tokens per turn for the skill prompt. The 65% output token, 33.2% input token (via proxy), and 61% skill token (via Pixel Mode) metrics are separate measurements and do not stack into a single combined number—read context carefully before quoting.Can Ponytail and Caveman Be Combined in One Session or Project?Ponytail and Caveman do not conflict. Caveman keeps what the agent reads (input) and says (output) as concise as possible, while Ponytail ensures what the agent writes (code) is strictly minimal. Because their mechanisms do not overlap, they can be used side-by-side in the same session.While no independent benchmark has measured the exact savings of using both simultaneously, combining their individual metrics theoretically reduces overall session tokens significantly—making it well worth testing on your real codebase.Workflow Integration GuideBoth Ponytail and Caveman support quick installation for popular AI coding tools like Claude Code, Codex, Gemini CLI, Cursor, and Windsurf.Installing PonytailFor Claude Code:claude plugin marketplace add DietrichGebert/ponytail && claude plugin install ponytail@ponytailFor other agents without plugin marketplace support, copy rule files directly from the GitHub repository into your project directory.Installing CavemanFor Claude Code:claude plugin marketplace add JuliusBrussee/caveman && claude plugin install caveman@cavemanFor Gemini CLI:gemini extensions install https://github.com/JuliusBrussee/cavemanInstalling for Cursor, Windsurf, Cline, and OthersCaveman can be installed via the unified registry:npx skills add JuliusBrussee/caveman -a <agent-name>Will You Choose Ponytail, Caveman, or Both?Token optimization is not just about saving money; it keeps AI Agent context clean and prevents context drift during long sessions. However, the larger takeaway from both skills is not to blindly accept advertised percentages—even from authors—since each figure typically measures a specific scenario rather than a general average. The surest approach is running benchmarks on your own codebase before adding Ponytail, Caveman, or both to your daily workflow.