Systematic Article Review for AI Agents

Where this fits

You've built an agent that drafts articles, but the output often misses logical gaps, weak evidence, or awkward transitions. You need a systematic way to ensure quality before publishing. The reviewing-article skill gives your agent a structured four-pass review process—logic, evidence, flow, and polish—so every draft gets the same rigorous QA you'd expect from a human editor.

Why agents benefit

• Focused passes reduce errors: Instead of trying to catch everything at once, the agent checks one dimension at a time, making issues easier to spot and fix.
• Generates an audit trail: The skill saves a review log (YYYY-MM-DD-<topic>-review.md) that documents every issue found and how it was resolved—useful for accountability and future reference.
• Forces consistency: By announcing the skill at the start, the agent commits to a repeatable process, avoiding ad-hoc, incomplete reviews.
• Improves final output quality: The final article (-final.md) is saved separately, ensuring the agent works from a clean copy after fixes are applied.
• Integrates with existing writing workflows: It pairs naturally with executing-article-plan, making it a drop-in addition to a content generation pipeline.

Practical scenarios

Blog post review before publishing

Your agent writes a technical blog post. Instead of shipping the first draft, it runs reviewing-article to check that each argument builds logically, claims are backed by data or citations, paragraphs transition smoothly, and the tone is consistent. The result: a publication-ready post with fewer revisions from human editors.

Documentation accuracy check

For API docs or internal wikis, the skill verifies that code examples match the actual behavior, that steps are in the correct order, and that no steps are missing. The evidence pass catches outdated commands or misquoted parameters.

Newsletter content QA

A weekly newsletter agent uses the skill to review multiple short pieces. The flow pass ensures each item reads well in sequence, and the polish pass catches typos or formatting inconsistencies before the email goes out.

Add it to your agent workflow

Install the skill with:

npx skills add binbinao/document-superpowers --skill reviewing-article

Then, in your agent's prompt, instruct it to invoke the skill before finalizing any article. For example:

You have drafted an article. Use the reviewing-article skill to perform a systematic four-pass review. Announce your intention, then follow the passes and save the review log and final article to docs/writing/.

The agent will read the full draft, run each pass in order, and produce a documented review.

Compare before adopting

Before committing, compare this skill with alternatives based on: quality signals—does it actually catch meaningful issues or just surface trivial typos? Maintenance freshness—is the repo actively updated and compatible with current agent frameworks? Alternatives—are there other review skills that fit your specific content type or language? Workflow fit—does it integrate cleanly with your existing drafting and publishing pipeline? A quick review of the GitHub repo and its issues can help.

Why it is worth tracking

With 44 stars, reviewing-article is still early but shows promise. Its structured, pass-based approach is a solid pattern for agent-driven QA. Watch for updates that add configurable checklists or support for different content domains. If you frequently rely on agents to produce long-form content, this skill is worth evaluating now—and revisiting as it matures. For more skills in this space, check out OpenAgentSkill.