markdown-token-optimizer

microsoft/github-copilot-for-azure · updated May 28, 2026

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$npx skills add https://github.com/microsoft/github-copilot-for-azure --skill markdown-token-optimizer
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summary

Analyzes markdown files and suggests token-reduction optimizations while preserving clarity.

  • Counts tokens using a 4-character-per-token approximation and identifies token-wasting patterns including emojis, verbosity, duplication, and oversized code blocks
  • Generates a detailed suggestions table showing issue location, description, recommended fix, and estimated token savings
  • Targets SKILL.md files for under 500 tokens and reference files for under 1000 tokens
  • Provides recommendati
skill.md

Markdown Token Optimizer

This skill analyzes markdown files and suggests optimizations to reduce token consumption while maintaining clarity.

When to Use

  • Optimize markdown files for token efficiency
  • Reduce SKILL.md file size or check for bloat
  • Make documentation more concise for AI consumption

Workflow

  1. Count - Calculate tokens (~4 chars = 1 token), report totals
  2. Scan - Find patterns: emojis, verbosity, duplication, large blocks
  3. Suggest - Table with location, issue, fix, savings estimate
  4. Summary - Current/potential/savings with top recommendations

See ANTI-PATTERNS.md for detection patterns and OPTIMIZATION-PATTERNS.md for techniques.

Rules

  • Suggest only (no auto-modification)
  • Preserve clarity in all optimizations
  • SKILL.md target: <500 tokens, references: <1000 tokens

References

how to use markdown-token-optimizer

How to use markdown-token-optimizer on Cursor

AI-first code editor with Composer

1

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 markdown-token-optimizer
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/microsoft/github-copilot-for-azure --skill markdown-token-optimizer

The skills CLI fetches markdown-token-optimizer from GitHub repository microsoft/github-copilot-for-azure and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/markdown-token-optimizer

Reload or restart Cursor to activate markdown-token-optimizer. Access the skill through slash commands (e.g., /markdown-token-optimizer) 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.

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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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.749 reviews
  • Dev Martin· Dec 12, 2024

    markdown-token-optimizer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Min Torres· Dec 4, 2024

    markdown-token-optimizer reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Jin Mehta· Nov 23, 2024

    I recommend markdown-token-optimizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Sakshi Patil· Nov 15, 2024

    markdown-token-optimizer has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • James White· Nov 3, 2024

    markdown-token-optimizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Aarav Thomas· Oct 22, 2024

    We added markdown-token-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Valentina Ndlovu· Oct 14, 2024

    Useful defaults in markdown-token-optimizer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chaitanya Patil· Oct 6, 2024

    Solid pick for teams standardizing on skills: markdown-token-optimizer is focused, and the summary matches what you get after install.

  • Dev Thompson· Sep 25, 2024

    markdown-token-optimizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Oshnikdeep· Sep 21, 2024

    Registry listing for markdown-token-optimizer matched our evaluation — installs cleanly and behaves as described in the markdown.

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