Managing 255 Articles Solo: A Content Operations System

When you go from 10 articles to 50, 100, then 200 articles, you hit a real problem: more articles, more chaos. Which articles have been written? Which haven't? What position does each keyword's article rank at? Which articles need updating? Without a tool to manage all this, relying on memory alone simply doesn't work. I learned this the hard way. By 50 articles, things were already getting messy. By 80, it was complete chaos — I had written 3 articles targeting the same keyword without realizing it.

By the time AgentClaw reached 255 articles, I had built a complete content operations management system. The core tool: Feishu's (Lark's) multidimensional tables. You might find it strange that someone running a content site uses Feishu. Honestly, I tried Notion, Airtable, and Google Sheets too. But Feishu's multidimensional tables beat them all in features and free-tier generosity. Especially for Chinese users, Feishu is fast to access, easy to collaborate with, and the free version is incredibly powerful. It's not that I didn't try other tools — I compared them all and found Feishu's multidimensional tables the best fit for a solo entrepreneur's content operations.

Why This Topic Matters

My Feishu multidimensional table has several core fields. First, Content ID — a standardized naming convention like TOOL-001, TOOL-002, incrementing. This ID is each article's unique identifier, used from writing to publishing to subsequent optimization. Next, Title — Chinese title and English slug for URLs. Then, Keyword field — records the target keyword and related long-tail keywords for that article. After keywords comes a Status field for tracking the article's lifecycle.

The Status field is what I find most valuable. I set five statuses: To Plan, Writing, Published, Optimizing, and Archived. Every article has one status. Open the table and you can instantly see: how many are "To Plan" (content still needed), how many are "Writing" (in progress), "Published" (already live), "Optimizing" (ranking dropped, needs work), and "Archived." Every Monday, I open this table, sort by status, and immediately know my focus for the week.

Keyword association is the second thing I built into the system. Each keyword can be associated with multiple articles. For example, the keyword "sports suit fabric" might be associated with two articles: "A-023 Fabric Buying Guide" and "A-056 Cotton vs. Polyester Comparison." One keyword mapped to multiple articles covers various user search intents. And when a particular article's ranking drops, I can quickly locate related articles to supplement content or add internal links. The implementation is simple: create a linked table in Feishu's multidimensional tables, connecting the keyword table to the article table in a many-to-many relationship.

Step 1: Find Your Positioning

Besides status and keywords, I added a "Ranking Data" column where I manually enter keyword ranking positions exported weekly from GSC. For example, last week the keyword was at position 9, this week at position 5 — I note it. Over time, this data helps you analyze which article types rank better and which need improvement. Data-driven content strategy isn't just talk — data gives you real insights.

Many people ask: do you just publish and forget? Absolutely not. A core principle of content operations is that "evergreen content needs continuous updating." I set a rule: every article gets reviewed 3 months after publishing. If ranking drops or information becomes outdated, I change the status to "Optimizing," update data, add new content, rewrite the title, and resubmit to GSC for indexing. This 3-month update cycle ensures my 200+ articles don't lose rankings due to stale information.

Content production tasks are also managed through the Feishu table. I open the table daily, check what topics are in the "To Plan" column. Topic sources are primarily GSC's search query reports — see what users are actually searching for, then prioritize. Daily writing tasks are also noted in the table. After finishing, I change the status to "Published" and sync the update to GitHub. This process sounds simple, but it supported the continuous production and maintenance of 255 articles.

Step 2: Build the System

Another very practical feature is kanban views and filters. Feishu's multidimensional tables support multiple views. I mainly use the kanban view — one column per status. Drag and drop an article from one status to another. For example, when a "Published" article's ranking drops, drag it to "Optimizing." When optimization is done, drag it back. The whole operation is fluid — no database or config file modifications needed.

I also designed internal link management into the table. Each article's fields include a "Related Articles" column, recording which internal articles this article links to. When writing a new article, I first check the table for existing related articles, ensuring each new article links to at least 2 to 3 existing internal articles. This improves user dwell time and helps Google crawlers crawl deeper into the site. Checking the association table takes 5 minutes before each new article, and the payoff is huge.

If you have dozens of articles and management is getting chaotic, I recommend spending one afternoon setting up a Feishu multidimensional table. It's an incredibly high-ROI activity. Start with just the most basic fields: article title, status, target keyword, publication date. Add more fields as article count grows. Don't overcomplicate it initially — get it running first, then iterate.

Step 3: Content Output

Compared to traditional CMS systems, Feishu's multidimensional tables as a content management system have several unique advantages. It's free — features that require Notion's paid tier are available free in Feishu. It's flexible — you can add fields, adjust views, create associations anytime, unlike the rigid backend database of WordPress. It's collaborative — if you ever hire an assistant, sharing documents on Feishu is much faster than teaching them a CMS backend.

Another overlooked benefit: Feishu's multidimensional tables can serve as a "publishing review layer." My articles go through a manual review step between AI generation and publishing. After review passes, I mark "Review Approved" in the Feishu table, then git push to GitHub. Managing this review status with a table is clearer than any other method — open the kanban and all pending articles are visible at a glance. No articles get published without review.

In AgentClaw's OPS system, I also did something interesting: tagging every article with a content type label. Labels include "Buying Guide," "Product Comparison," "Scenario Recommendation," "Fabric Education," "Care & Maintenance," "Brand Introduction," and so on — about 7 to 8 types. Filtering by label, I can quickly analyze which content types perform best SEO-wise. After two months, I found that Buying Guide and Product Comparison articles averaged 2 to 3 times the UV of Scenario Recommendation articles. This data directly guided my subsequent content strategy — write more comparison and recommendation articles.

Step 4: Traffic Acquisition

Summary: Feishu's multidimensional tables don't replace a CMS. They add an operations management layer on top of the CMS. The CMS handles "how articles are displayed"; the Feishu table handles "how articles are planned, produced, and optimized." The two complement each other — neither is dispensable. If you're running a content site solo, don't just use your CMS's draft folder to manage writing tasks. Build a Feishu multidimensional table and operate your content like a product. The difference will be night and day.

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