md-to-feishu▌
zc277584121/marketing-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Convert a local Markdown file into a Feishu document, with automatic image upload.
Markdown to Feishu Document
Convert a local Markdown file into a Feishu document, with automatic image upload.
User Input
The user only needs to provide a Markdown file path. Title is optional — if not provided, extract it automatically (see below).
Step 1: Determine the Document Title
- Read the Markdown file and look for the first
# heading— use that as the title. - If no
# headingexists, scan the content and generate a concise, descriptive title based on the topic. - If the user explicitly provides a title, use that instead.
Step 2: Check the Runtime Environment
Try each option in order. Use the first one that works.
Option A: uvx (preferred)
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>
Option B: feishu-docx already installed
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>
Option C: Nothing available — install guidance
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.
Step 3: Pre-process Mermaid Blocks
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.
3a: Scan for Mermaid blocks
Read the Markdown file and check if it contains any ```mermaid fenced code blocks. If none are found, skip to Step 4.
3b: Create a temporary copy
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.
3c: Render Mermaid diagrams to PNG
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:
- GOOD:
mermaid-architecture-overview.png,mermaid-data-flow.png - BAD:
mermaid-1.png,diagram.png
3d: Replace Mermaid blocks with image references
In the temp copy, replace each ```mermaid ... ``` block with a Markdown image reference:

Use relative paths from the temp file to the rendered images.
3e: Use the temp file for upload
From this point, the temp file becomes the <MARKDOWN_FILE_PATH> used in Step 4.
Step 4: Run the Command
- Run from the directory where the Markdown file lives (or where its relative image paths resolve correctly), so that local image references work.
- The tool will:
- Convert Markdown blocks to Feishu format
- Automatically upload local images referenced in the Markdown
- Wait ~10s for block consistency before uploading images
Step 5: Clean Up
If a temp file was created in Step 3:
- Delete the temp Markdown file (
*.feishu-tmp.md) - Delete all rendered Mermaid PNG files created in Step 3c (they were only needed for the upload)
Step 6: Report Result
Show the user:
- Number of blocks converted and images uploaded
- The created document ID
- Success or failure status
If it fails with authentication errors, remind the user to configure credentials:
feishu-docx config set --app-id <APP_ID> --app-secret <APP_SECRET>
How to use md-to-feishu on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add md-to-feishu
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches md-to-feishu from GitHub repository zc277584121/marketing-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate md-to-feishu. Access the skill through slash commands (e.g., /md-to-feishu) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★43 reviews- ★★★★★Pratham Ware· Dec 28, 2024
md-to-feishu reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chaitanya Patil· Dec 20, 2024
Solid pick for teams standardizing on skills: md-to-feishu is focused, and the summary matches what you get after install.
- ★★★★★Noor Iyer· Dec 12, 2024
md-to-feishu has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ama Huang· Dec 12, 2024
md-to-feishu reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Hiroshi Abbas· Dec 12, 2024
We added md-to-feishu from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kwame Khan· Dec 4, 2024
Solid pick for teams standardizing on skills: md-to-feishu is focused, and the summary matches what you get after install.
- ★★★★★Ama Harris· Nov 23, 2024
We added md-to-feishu from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Piyush G· Nov 11, 2024
We added md-to-feishu from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Maya Torres· Nov 3, 2024
md-to-feishu fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Meera Jackson· Nov 3, 2024
Solid pick for teams standardizing on skills: md-to-feishu is focused, and the summary matches what you get after install.
showing 1-10 of 43