How to Implement the wechat-article Skill for Generating Research Content

The wechat-article skill is a multi-agent workflow in the ai-berkshire repository that transforms raw topic descriptions into polished, research-ready WeChat articles through four automated stages orchestrated by Author, Editor, and Reader agents.

The ai-berkshire repository provides a structured framework for automating research content creation. When you implement the wechat-article skill, you enable a pipeline that conducts parallel research, drafts technical content in Chinese, performs multi-perspective editing, and finalizes assets—all triggered by a single CLI command.

Architecture Overview

The skill executes a four-stage pipeline defined in skills/wechat-article.md. Each stage leverages specialized sub-agents to handle distinct aspects of content creation, from initial research to final publication formatting.

Stage 1: Research & Material Collection

This stage begins with parameter clarification—defining target audience, depth, length, and style requirements. The workflow then spawns 2-3 parallel research agents using the repository's generic Agent toolset (which includes web search, PDF download, and data extraction capabilities).

Each agent focuses on a specific domain:

• Core content agent extracts technical thesis and key data points
• Industry background agent gathers contextual market information
• Optional competitor analysis agent benchmarks against related work

Outputs consolidate into a structured framework containing the core thesis, key datasets, figure inventory, and article outline.

Stage 2: Author Agent Drafting

The Author Agent receives the research framework and a detailed prompt template (lines 84-106 in skills/wechat-article.md). This agent generates a full-draft article in Chinese following strict formatting rules:

• Maximum 4 lines per paragraph (optimized for mobile reading)
• LaTeX formula support with plain-language explanations
• Image placeholders using ![描述](相对路径) syntax
• Specific structural requirements: strong hook in first 3 paragraphs, technical depth with "plain language translation," and screenshot-worthy conclusion

Stage 3: Editor and Reader Review

Two agents run in parallel to evaluate the draft:

Editor Agent (prompt defined at lines 119-146) evaluates:

• Title clickability and length (must not exceed 30 characters)
• Hook strength in opening paragraphs
• Logical flow and structural coherence
• Balance between technical depth and readability
• Figure placement and accessibility

Reader Agent (prompt defined at lines 144-162) adopts the target persona to identify comprehension gaps, weak engagement points, and overall readability issues.

Stage 4: Finalization and Asset Integration

The workflow merges feedback streams, prioritizes high-frequency revision issues, and rewrites accordingly (steps 71-78). The image extraction pipeline executes pdftoppm → PIL operations to embed high-resolution figures (≥ 500 KB) directly into the document. Final output saves to the reports/ directory following the naming convention defined in the File Naming & Storage table (lines 113-117).

Core Source Files and Their Roles

The skill implementation spans three synchronized files:

Component	File Path	Purpose
Skill Definition	skills/wechat-article.md	Human-readable workflow containing all prompts, stage logic, and execution steps
Codex Adapter	codex-skills/wechat-article/SKILL.md	Auto-generated machine-readable artifact that wraps the markdown workflow for Codex invocation
Slash-Prompt Entry	codex-prompts/wechat-article.md	CLI shortcut interface used by end-users to trigger the skill

Setting Up and Syncing the Skill

After modifying skills/wechat-article.md, you must regenerate the Codex-compatible artifacts using the repository's sync script:

python3 scripts/sync-codex-skills.py

This script parses the human-readable skill definition and updates codex-skills/wechat-article/SKILL.md with the machine-readable version, ensuring the $ARGUMENTS placeholders remain correctly mapped.

Invoking the Skill

Once synced, trigger the skill via the CLI wrapper:

ai-berkshire wechat-article "大模型 OPD 技术解读"

This command reads codex-prompts/wechat-article.md, loads the Codex skill, injects the topic argument, and streams the multi-agent execution. The skill writes the final markdown to a path like reports/AI产业研究/公众号-OPD技术解读-20260711.md, containing the article body, embedded figures, and citation blocks.

Programmatic Invocation

You can also trigger the skill from Python scripts:

from subprocess import run, PIPE

def generate_wechat_article(topic: str) -> str:
    """Runs the wechat-article skill and returns the path of the generated markdown."""
    cmd = ["ai-berkshire", "wechat-article", topic]
    result = run(cmd, stdout=PIPE, stderr=PIPE, text=True)

    if result.returncode != 0:
        raise RuntimeError(f"Skill failed: {result.stderr}")

    # The skill prints the output file path on success

    return result.stdout.strip()

# Usage

article_path = generate_wechat_article("大模型 OPD 技术解读")
print(f"Article generated at: {article_path}")

Customizing the Agent Prompts

All prompts use plain Chinese with explicit $ARGUMENTS placeholders, enabling reuse across any topic. The Author Agent receives this template (lines 84-106):

你是一位深度技术写作者（Author Agent），需要写一篇微信公众号文章。

## 目标读者

{根据第一步确认的读者画像}

## 写作风格要求

- 纯中文表达，避免中英文夹杂（技术术语首次出现时给英文，后续用中文）
- 像写给聪明的朋友看的技术科普，不是学术论文翻译
- 用类比帮助理解，但类比要贴切、不俗套
- 关键公式/数据要有，但每个都要用大白话解释
- 不用emoji
- 段落不超过4行（公众号阅读环境）

## 核心内容

{整理好的素材、数据、论点}

## 文章结构要求

1. **开头（前3段）**：必须有强钩子——用数据冲击力或反直觉的结论开场，不要用温和的类比开场
2. **背景**：为什么这件事重要？解决什么问题？
3. **核心内容（2-3节）**：技术深度在这里体现，但每个技术点都要有"大白话翻译"
4. **实证/案例**：用数据和案例说话，不要空谈
5. **行业影响/展望**：这件事对行业意味着什么
6. **结尾**：一句有传播力的判断收束，适合被截图转发

## 配图要求

- 论文解读类文章：必须从论文PDF中提取原图，直接用 `![描述](相对路径)` 插入文章，不要用 [图X：描述] 占位符

请写出完整的文章初稿，约{目标字数}字。

The Editor Agent uses this review checklist (lines 119-146):

你是一位资深公众号编辑（Editor Agent）。请对以下文章进行精修审阅。

## 审阅标准

1. **标题**：是否在朋友圈能吸引点击？是否会被截断（超过30字）？
2. **开头**：前3段能否留住读者？钩子是否足够强？
3. **结构**：逻辑链是否流畅？有无跳跃或断层？
4. **深度与可读性平衡**：公式/技术部分是否真的通俗？有无"假装通俗但没解释清楚"的地方？
5. **节奏**：有无太长的段落？每节长度是否合适？
6. **配图**：图片是否已实际插入（非占位符）？位置是否在读者最需要视觉辅助时出现？
7. **结尾**：有无传播力？读者看完会想转发吗？

## 文章全文

{完整初稿}

Summary

• The wechat-article skill implements a four-stage multi-agent workflow (Research → Author → Editor/Reader → Finalization) defined in skills/wechat-article.md.
• Three synchronized files handle the implementation: the human-readable skill definition, the auto-generated Codex adapter at codex-skills/wechat-article/SKILL.md, and the CLI entry point at codex-prompts/wechat-article.md.
• Run python3 scripts/sync-codex-skills.py after editing the skill definition to regenerate machine-readable artifacts.
• Execute the skill via ai-berkshire wechat-article "topic" to generate research-ready articles with embedded figures and citations.
• Customize agent behavior by modifying the Chinese prompt templates in the skill definition file, which support variable injection through $ARGUMENTS placeholders.

Frequently Asked Questions

What makes the wechat-article skill different from a standard LLM prompt?

The wechat-article skill implements a structured multi-agent architecture where distinct AI agents (Research, Author, Editor, Reader) operate sequentially and in parallel to enforce quality controls. Unlike single-prompt approaches, this workflow extracts source PDF figures automatically, validates content against mobile-reading constraints (such as the 4-line paragraph limit), and performs adversarial review through the Editor and Reader agents before finalizing output.

How do I modify the research parameters for a specific article?

Edit the Skill Definition file at skills/wechat-article.md to adjust the Stage 1 parameters—specifically the target audience profile, desired word count, and research depth indicators. After modification, run python3 scripts/sync-codex-skills.py to propagate changes to the Codex adapter. The $ARGUMENTS placeholder system allows runtime topic injection without hardcoding subject matter into the prompts.

Can I use the wechat-article skill without the Codex environment?

Yes. While the repository provides Codex integration through codex-skills/wechat-article/SKILL.md, the core workflow in skills/wechat-article.md contains human-readable prompts that can be executed manually or integrated into other agent frameworks. The Python subprocess example demonstrates how to invoke the skill through any environment that supports the ai-berkshire CLI wrapper.

Where are the generated articles stored and how are they named?

The skill automatically saves finalized articles to the reports/ directory following the naming convention specified in lines 113-117 of the skill definition. File paths typically follow the pattern reports/{category}/公众号-{topic}-{date}.md (for example: reports/AI产业研究/公众号-OPD技术解读-20260711.md), including the article body, embedded high-resolution figures, and source citation blocks.

{
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    "instagit": {
      "command": "npx",
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}