‘Claude Code-ifying’ as a Verb — Japan’s Qiita in May and the Standardization of the Agentic IDE

Why is the Japanese developer community, in May 2026, so fixated on operational know-how for Claude Code? And is the new verb “Claude Code-ify” a mere imitation, or is it a signal that the agentic IDE category is hardening into a standard pattern?

Introduction — A product name turned verb

Read Qiita’s trending page from the top down during the second week of May, and an odd commonality jumps out. Roughly half of the most-popular posts are about Claude Code. That isn’t surprising in itself. Since last fall, Anthropic’s coding agent has effectively become the tool Japanese engineers touch most often. What’s surprising is the texture of the posts. Not “how do I get started” or “how does it compare with X,” but instead operational know-how like “how do I squeeze 120% out of it,” “how do I Claude Code-ify another tool,” and “how do I cut costs while keeping the same effect.”

manchan’s early-May post, “3 settings to master Claude Code at 120% [ECC, Memory.md, Obsidian integration],” picked up 79 LGTMs and held the top of Qiita’s coding category for a week. katohiro_fi’s post, “I Claude Code-ified Copilot Studio, and Copilot Studio built a Power Platform app by itself,” gathered 64 LGTMs; faunsu’s “ChatGPT Pro is expensive, so I want to save my allowance with Codex + GitHub Copilot” pulled in 51. Three posts in the same week treat entirely different content, yet share one keyword: Claude Code化 (Claude Code-ify).

Here, “化” means “to make like Claude Code,” but read more precisely it means “to port the standard usage pattern of an agentic coding agent onto another tool.” The phrase “Claude Code-ifying” Copilot Studio reveals, in reverse, what Claude Code has hardened into a standard pattern. Local file reading and writing, shell command execution, glob search, grep, and a workflow that binds all of it together with natural-language instruction. That bundle is no longer one company’s product — it is becoming the definition of the category itself.

This piece uses three popular Qiita posts from May to organize how Japanese engineers are currently living with Claude Code. Then it asks why the Japanese community is accumulating know-how here so quickly, and finally asks what the verb-ification of “Claude Code-ify” means for the IT industry as a whole.

Section 1 — Three May posts from Japan’s Qiita: ECC, Memory.md, and the Codex+Copilot pragmatism

Start with manchan’s piece. As the title “3 settings to master Claude Code at 120%” promises, it tidies up the three settings he has been hardening in his own environment. The names are unfamiliar; the content is simple.

First, Everything Claude Code (ECC). A bundle of configurations published by GitHub user GreatScotty44: git clone it, run ./install.sh --profile full, and you install 48 agents, 79 commands, and 149 skills all at once. The core is a routing layer that automatically invokes specialist agents like code-reviewer, security-reviewer, and planner. manchan writes that security-reviewer caught an error in his automation tool where the X (Twitter) API key and the YouTube OAuth token had been placed in the wrong location. The one line “X APIキー・YouTube OAuthトークンの扱いを自動チェック。.env への移動漏れを指摘 (Auto-checks the handling of X API keys and YouTube OAuth tokens; flagged where they hadn’t been moved into .env)” shows, in the shortest form, what this category of tool does when it works. In Korean-language communities, even the name ECC barely circulates; in Japan, the practice of installing this bundle wholesale into one’s own environment has solidified into a flow as of May.

Second, the CLAUDE.md + Memory.md dual structure. CLAUDE.md defines work rules and roles; Memory.md captures the project’s current state, tech stack, completed automations, environment-variable locations, and a backlog of unfinished work. CLAUDE.md is touched only when policy changes; Memory.md is updated every session. The key move is pinning the line “セッション開始時に必ずMemory.mdを読み込む (At session start, always load Memory.md)” at the top of CLAUDE.md. With that in place, a single “resume the previous session” command at the start of a new session makes Claude load Memory.md first. The pattern is effectively a hand-rolled mini-RAG. Anthropic has been rolling out an official memory feature in beta, but Japanese users aren’t waiting; they’re achieving the same effect with two markdown files.

Third, Obsidian integration. manchan creates three folders — news/, raw-sources/, wiki/ — under docs/AI-DataBase/, and every morning at 8 a.m. collect_news.py runs via launchd to auto-collect Claude Code-tagged posts from Zenn and Qiita and drop them as Markdown. Claude then reads the folder and writes organized notes — with Wikilinks and Callouts placed correctly — into wiki/. It’s a structure that fuses Obsidian’s note graph with Claude’s natural-language summarization. Notably, kepano’s obsidian-skills is copied straight into ~/.claude/skills/ and used as-is. In Korea, while Notion and Roam have gone mainstream, the pattern of “letting Claude write directly into Obsidian markdown” is standardizing much faster on the Japanese side.

Next is katohiro_fi’s “Claude Code-ifying Copilot Studio.” The entry point is an MCP (Model Context Protocol) server called copilot-studio-code, published by Microsoft’s Taiki Yoshida. The MCP server attaches tools like read_file, write_file, run_shell, glob, and grep to Copilot Studio — meaning that from Copilot Studio’s chat window, you can read local files and run Python scripts directly. To let cloud-side Copilot Studio reach the local MCP server, an HTTPS path is opened through Microsoft’s Dev Tunnel.

The author then combined this with Hiromichi Fujiwara’s CodeAppsDevelopmentStandard repository and typed a single line: “I want to build an employee-onboarding tool on Power Platform.” From that one line, Copilot Studio auto-designed and provisioned Dataverse tables, wrote a large number of .tsx files, fixed its own TypeScript build errors, deployed the Power Apps app, and even attached auto-generated icons to finish a Copilot Studio agent. The subtitle the author appended to this piece is a one-line summary of the whole thing: “Copilot Studio が Copilot Studio を作る (Copilot Studio builds Copilot Studio).” It is the most striking single line among May’s popular Qiita posts.

The caveats are equally clear. The author plainly notes the constraint: “直近 10 ターンの会話履歴を参照する (only the most recent 10 turns of conversation history are referenced).” Copilot Studio wasn’t designed for AI coding; its token budget is small, and once you pass 10 turns, design decisions agreed earlier disappear and have to be re-entered. Reproducibility is low; the author admits it is PoC-level. But that doesn’t diminish the meaning of the attempt. The fact that “even a closed environment like Copilot Studio works once you dress it in Claude Code’s tool-calling pattern” is itself a signal that the standard interface of the agentic IDE is hardening.

The third post, faunsu’s “ChatGPT Pro is expensive, so I want to save my allowance with Codex + GitHub Copilot,” has a different mood. It doesn’t directly cover Claude Code, but it belongs in the same bundle because it shows how Japanese developers are simultaneously hardening cost-optimization know-how. The author uses Codex not as the “executor” but as the “director and reviewer,” and delegates the actual implementation to the GitHub Copilot CLI — a division-of-labor structure. By using ChatGPT and GitHub MCP first for research and issue scoping before entering Codex, you prevent Codex from burning tokens exploring the codebase from scratch.

Monthly cost runs about ¥3,000 for ChatGPT Plus plus roughly ¥1,500 for GitHub Copilot Pro — about ¥4,500 total. Far cheaper than a standalone ChatGPT Pro subscription. The author’s line, “Codex のトークンを「全部の作業」に使うのではなく、「判断が必要な所」に寄せられます (You can concentrate Codex’s tokens not on ‘every task’ but on ‘the places where judgment is required’),” reveals the shared aesthetic of this genre of post. Where and how to place expensive inference tokens has become a new engineering skill. And the author’s question, “トークンの消費を気にして Plan モードを経由せずにそのまま開始していませんか？ (Are you worried about token consumption and starting without going through Plan mode?)” surfaces another consensus that emerged in the Japanese community in May: don’t skip Plan mode. It looks like saving time, but it ends up burning more tokens. That’s the empirical finding.

Section 2 — Why this know-how hardens first in the Japanese community

The three posts share an underlying disposition. When a new tool arrives, fit it to your own environment and squeeze it to the limit, then write it up on Qiita and harden the consensus with LGTMs. Compared with Korea, the speed and density of this cycle is different. Korean posts on Claude Code mostly stay at “I tried it” or “compared to X.” The Japanese side has, almost without anyone noticing, moved one level deeper: “where in my workflow did I install it, and how.” Why?

The first reason is note-taking tool culture. In the Japanese engineering community, the share of people who daily use local-markdown note tools like Obsidian, Logseq, and Scrapbox is much higher than in Korea. Even with company wikis locked into Confluence, many people still accumulate personal knowledge as local markdown. On that soil, a pattern like “let Claude write directly into Obsidian notes” emerges naturally. Conversely, in an environment where notes themselves live inside cloud SaaS, it is hard to imagine a workflow where AI manipulates notes directly. manchan’s docs/AI-DataBase/ folder structure is, in itself, unremarkable — but the decisive point is that this unremarkable layout sits atop the average Japanese engineer’s environment.

The second reason is the tradition of small automations. Japan has a long-running culture of finely automating one’s own environment with tools like launchd, cron, AppleScript, and Hammerspoon. From this culture, a news-collection script running at 8 a.m. every morning is not new. When an agent like Claude Code is added, it slots in naturally. The frequent use of the term “定時自動化 (scheduled automation)” in Japanese IT media comes from the same context. In Korea, “automation” tends to first evoke cloud-based automation like GitHub Actions or Slack bots. The scenery of launchd running daily on a local machine is uncommon.

The third reason is the aesthetic of “120% use.” manchan’s title “120% 使い倒す (squeeze it to 120%)” uses a phrase common in the Japanese engineering community. The attitude of pushing a tool not to 100% but to 120% — extending the tool’s range of use beyond what was officially intended. It is not simply “using it well” but the operation of “reconstituting it for my environment.” Installing an unofficial bundle like ECC wholesale to test it, or forcing an MCP server into a closed tool like Copilot Studio so that it behaves like Claude Code — both come from the same aesthetic.

The fourth reason is the effect of the Qiita platform. Via the simple consensus mechanism of LGTM, Qiita rapidly manufactures the signal “this know-how is acknowledged by our community.” Korean platforms like velog or tistory have many good posts, but the structure where people working with the same tool gather in one place to form shared consensus through LGTM is weaker. Qiita’s tag system and LGTM rapidly mold a common sense of “this is the standard in operating the tool called Claude Code.” One person posts, then the next week another person cites that post and adds their own variation. The cycle goes around two or three times in a month.

The fifth reason is that the model developers themselves are now reaching directly into the Japanese-language community. Both Anthropic and OpenAI have held developer events back-to-back in Tokyo since May. Microsoft’s Taiki Yoshida releasing a side project like copilot-studio-code belongs in the same context. Japan is no longer a market where know-how popular in the English-speaking world arrives a beat late; it is becoming a market that manufactures know-how itself and pushes it back into the English-speaking world. Look at the star distribution of ECC’s GitHub repository and the share of Japanese users is conspicuously high. The Japanese fork of obsidian-skills is being updated more actively than the upstream.

That said, it would be wrong to assume this Japanese lead is permanent. Similar posts have begun to appear in the Korean community in May. It is simply that Korea is still strongly oriented toward “what can be done with the latest model,” and the consolidation of “how do I squeeze it in my environment” know-how is a beat slower. That speed difference ultimately resolves into a difference in the thickness of consensus shared by the people who operate the tool. Once that thickness diverges, it is hard to close again. While the Japanese side has already hardened the consensus that “Memory.md is refreshed every session,” it will take two or three more quarters before the same consensus achieves comparable thickness in Korea.

Section 3 — “Claude Code-ify” as verb: the agentic IDE category is hardening into a standard pattern

Now back to katohiro_fi’s piece. The line “Copilot Studio が Copilot Studio を作る” carries two layers of meaning. On the surface, it’s a self-referential structure: Copilot Studio itself builds another Copilot Studio agent on top of Power Platform. One layer deeper, it means that Copilot Studio — Microsoft’s closed SaaS — could behave that way only after wearing the usage pattern Anthropic’s Claude Code defined. Put differently, Microsoft borrowed the standard way of using its own tool not from Microsoft but from Anthropic.

This fact should not be taken lightly. As recently as last year, the category definition of “AI coding tool” was held by GitHub Copilot. Inline autocomplete was the core of that definition. Cursor moved the definition one notch. Chat panel and multi-file editing were added. Then Claude Code moved it another notch. The pattern of putting the terminal as a first-class interface and binding shell commands, file read/write, and search via natural-language instruction became the standard. The May case of “Claude Code-ifying” Copilot Studio is a visible signal that Microsoft has acknowledged this new standard and is aligning its own tool with it.

This category standardization echoes other moments in the IT industry. It resembles the flow in which PaaS took Heroku’s usage pattern as standard and then bifurcated into Cloud Foundry, OpenShift, and Vercel. It resembles container orchestration taking Kubernetes’ manifest and controller patterns as standard. In the agentic IDE category, Claude Code’s “terminal + MCP + file tools + Plan mode” sits in the analogous position. Cursor has already moved in the same direction, and open-source tools like Aider, Cline, and Roo Code are converging on the same pattern. And in May, Microsoft’s Copilot Studio joined the flow as well.

That a standard hardens means two things at once. One is good for users. The patterns learned in one tool transfer to others. The dual structure of CLAUDE.md and Memory.md is conceptually identical to Cursor’s .cursorrules or Aider’s .aider.conf.yml. MCP, an external interface standard, was created by Anthropic, but OpenAI’s ChatGPT, Google’s Gemini, and Microsoft’s Copilot Studio have all adopted it or are adopting it. Users can carry know-how learned once across many tools. The know-how organized in the Japanese Qiita posts is, for that reason, not limited to Claude Code. It quickly becomes “operational know-how for agentic IDEs in general.”

The other meaning is that, for tool suppliers, the locus of differentiation moves from the model itself to operational ergonomics. Model performance still rises a notch each quarter, but that difference is no longer the decisive variable in user satisfaction. The decisive variables are the naturalness of memory management, the depth of the MCP tool ecosystem, the speed of Plan mode, the precision of cost visibility, the safety of the shell-command permission model. That all of May’s Japanese Qiita posts treat these operational details is, in itself, a signal that users have lost interest in comparing model performance. Who first cracks operational ergonomics wins the next quarter.

There’s a risk here too. When a standard hardens quickly, the company that defined it effectively becomes the category gatekeeper. Anthropic began as a model company, but through Claude Code’s tool-calling spec and the MCP interface, it is effectively taking hold of the category standard. If a structure hardens in which other models must follow that spec to remain compatible, competition in the model market risks being locked to the category definition. That Microsoft accepted MCP wholesale instead of forcing its own standard onto Copilot Studio looks like a follow-on decision made after the gate had already closed. Google’s Gemini Code Assist announcing MCP compatibility in May belongs to the same context.

Conclusion — The era is operational know-how, not the model

Back to the opening two questions. Why is the Japanese developer community right now fixated on Claude Code operational know-how? And is the new verb “Claude Code-ify” mere imitation?

The answer to the first question is environmental. Local-markdown note culture, the tradition of small automations like launchd, the aesthetic of “squeezing to 120%,” Qiita’s LGTM consensus cycle, and the trend of model companies reaching directly into the Japanese market all overlap. On that ground, people like manchan, katohiro_fi, and faunsu rapidly harden the operational know-how they have created in their own environments into posts, and those posts become the starting points for the next week’s know-how. The Korean community can build a similar cycle, but it is standing two or three quarters behind.

The answer to the second question is that it isn’t mere imitation. Copilot Studio’s “Claude Code-ification” is an event in which Microsoft has acknowledged the standard of a new category, and a signal that the agentic IDE category has now entered the stage of industry standardization, not the stage of one company’s product. Once this stage hardens, tool-supplier differentiation is decided not by model performance but by operational ergonomics. Memory management, the MCP ecosystem, the speed of Plan mode, the precision of cost visibility, and the safety of the permission model matter more than model benchmark scores.

This shift leaves a message for the individual engineer too. The time spent worrying about which model to use next quarter is worth less than the time spent hardening the operational pattern of the tool already in hand to fit your environment. Refreshing Memory.md every session, never skipping Plan mode, plugging one or two MCP tools into your workflow at a time — these small habits decide the next year’s productivity. The verb “Claude Code-ify” teaches us one thing again. The moment a tool’s name becomes a verb, that tool is no longer one company’s product — it is everyone’s way of working. And the next round of difference is decided by who first, and who more deeply, embeds that way of working in their own environment.