Convert a local Markdown file into a Feishu document, with automatic image upload.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmd-to-feishuExecute the skills CLI command in your project's root directory to begin installation:
Fetches md-to-feishu from zc277584121/marketing-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate md-to-feishu. Access via /md-to-feishu in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Convert a local Markdown file into a Feishu document, with automatic image upload.
The user only needs to provide a Markdown file path. Title is optional — if not provided, extract it automatically (see below).
# heading — use that as the title.# heading exists, scan the content and generate a concise, descriptive title based on the topic.Try each option in order. Use the first one that works.
which uvx
If uvx is available, the run command is:
uvx feishu-docx create "<TITLE>" -f <MARKDOWN_FILE_PATH>
If uvx runs with Python < 3.11, add --python 3.11:
uvx --python 3.11 feishu-docx create "<TITLE>" -f <MARKDOWN_FILE_PATH>
which feishu-docx
If found, check Python version:
python3 --version
If Python >= 3.11, the run command is:
feishu-docx create "<TITLE>" -f <MARKDOWN_FILE_PATH>
If neither uvx nor feishu-docx is found, tell the user:
feishu-docxrequires Python >= 3.11. Install with one of:# Recommended: install uv, then run directly without global install curl -LsSf https://astral.sh/uv/install.sh | sh uvx feishu-docx create "Title" -f file.md # Or: install globally with pip (Python >= 3.11 required) pip install feishu-docxFeishu credentials must be configured first:
feishu-docx config set --app-id <APP_ID> --app-secret <APP_SECRET>
Then stop and wait for the user to set up the environment.
The feishu-docx tool cannot handle Mermaid code blocks. Before uploading, check if the Markdown contains any ```mermaid blocks and convert them to images first.
Read the Markdown file and check if it contains any ```mermaid fenced code blocks. If none are found, skip to Step 4.
Copy the original Markdown file to a temp file in the same directory (so relative image paths still work):
<original-name>.feishu-tmp.md
For example: blog_post.md → blog_post.feishu-tmp.md
All subsequent modifications happen on this temp copy. The original file is never modified.
For each ```mermaid ... ``` block in the temp file, render it to a PNG image using the mermaid.ink API:
import base64, urllib.request
def render_mermaid(code: str, output_path: str):
"""Render a Mermaid diagram to PNG via mermaid.ink API."""
encoded = base64.urlsafe_b64encode(code.encode()).decode()
url = f"https://mermaid.ink/img/{encoded}?bgColor=white"
req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
resp = urllib.request.urlopen(req, timeout=30)
with open(output_path, "wb") as f:
f.write(resp.read())
Important: The User-Agent header is required — mermaid.ink returns 403 without it.
Save rendered images to the same directory as the Markdown file, using descriptive filenames based on diagram content:
mermaid-architecture-overview.png, mermaid-data-flow.pngmermaid-1.png, diagram.pngIn the temp copy, replace each ```mermaid ... ``` block with a Markdown image reference:

Use relative paths from the temp file to the rendered images.
From this point, the temp file becomes the <MARKDOWN_FILE_PATH> used in Step 4.
If a temp file was created in Step 3:
*.feishu-tmp.md)Show the user:
If it fails with authentication errors, remind the user to configure credentials:
feishu-docx config set --app-id <APP_ID> --app-secret <APP_SECRET>
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
md-to-feishu reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: md-to-feishu is focused, and the summary matches what you get after install.
md-to-feishu has been reliable in day-to-day use. Documentation quality is above average for community skills.
md-to-feishu reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added md-to-feishu from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: md-to-feishu is focused, and the summary matches what you get after install.
We added md-to-feishu from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added md-to-feishu from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
md-to-feishu fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: md-to-feishu is focused, and the summary matches what you get after install.
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