SkillAtlasSkill 详情

图表生成

Version 5.

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项目 README

来源文件:README.md

抓取于 2026年7月28日

Skills

Version 5.27.0 - Added walkthrough-script-agent: generate timed walkthrough video scripts for app features

Personal collection of agent skills using the open SKILL.md standard. Works with Claude Code and other AI assistants.

Installation

Claude Code

# Add the marketplace
/plugin marketplace add michaelboeding/skills

# Install the plugin
/plugin install skills@michaelboeding-skills

Other Tools

Copy the skills/ folder to your project or follow your tool's skill installation docs.


Python Dependencies

Many skills require Python packages. Run the install script:

# From the skills directory
./scripts/install.sh

Or install manually:

pip install -r requirements.txt

Requirements:

  • Python 3.10+ (for google-genai package)
  • pip

What gets installed:

PackageVersionUsed By
google-genai≥1.0.0image-generation, video-generation, voice-generation, music-generation
matplotlib≥3.7.0chart-generation
numpy≥1.24.0chart-generation
python-pptx≥0.6.21slide-generation
Pillow≥10.0.0slide-generation, image processing
rembg≥2.0.50background-remove, icon-generation

Optional tools:

ToolInstallUsed By
ffmpegbrew install ffmpegmedia-utils, audio/video processing

Setup

API Keys (Required for some skills)

Some skills require API keys to function. Copy the example environment file and add your keys:

# Copy to your config directory (recommended - keeps keys safe from git)
mkdir -p ~/.config/skills
cp env.example ~/.config/skills/.env
# Edit ~/.config/skills/.env with your keys

Then export the variables in your shell profile (~/.bashrc, ~/.zshrc, or ~/.bash_profile):

# Core APIs (used by multiple skills)
export OPENAI_API_KEY="sk-..."          # DALL-E, Sora, TTS
export GOOGLE_API_KEY="..."             # Imagen, Gemini (AI Studio)
export ELEVENLABS_API_KEY="..."         # ElevenLabs TTS

# Music Generation
export SUNO_API_KEY="..."               # Suno music
export UDIO_API_KEY="..."               # Udio music

# Model Council (optional)
export ANTHROPIC_API_KEY="sk-ant-..."   # Claude API
export XAI_API_KEY="..."                # Grok API

Restart your terminal or run source ~/.bashrc (or equivalent) for changes to take effect.

Google Cloud / Vertex AI (Default for All Google Skills) ⭐

Vertex AI is the default backend for all Google-powered skills with higher rate limits:

SkillAI StudioVertex AI
Video (Veo)10/day10/min
Voice (Gemini TTS)LimitedHigher
Music (Lyria)LimitedHigher
Image (Imagen)LimitedHigher

Setup Vertex AI (one-time):

# 1. Install Google Cloud SDK: https://cloud.google.com/sdk/docs/install

# 2. Login and set project
gcloud auth application-default login
gcloud config set project YOUR_PROJECT_ID

# 3. Enable Vertex AI API
gcloud services enable aiplatform.googleapis.com

# 4. Export project (add to .env or shell profile)
export GOOGLE_CLOUD_PROJECT="your-project-id"
export GOOGLE_CLOUD_LOCATION="us-central1"  # or us-east4

The video generation scripts auto-detect and use Vertex AI when GOOGLE_CLOUD_PROJECT is set.

Where to get API keys:

⚠️ Credential Security

✅ Do❌ Don't
Store keys in ~/.config/skills/.envCommit .env files to git
Use gcloud auth for local devHardcode keys in scripts
Use service accounts for CI/CDShare API keys publicly
Rotate keys if exposedStore keys in repo, even private

For CI/CD / Production:

# Option 1: Service Account (recommended)
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"

# Option 2: Workload Identity (GKE/Cloud Run)
# Automatically authenticated, no keys needed

Skills vs Agents

Everything is a skill (has a SKILL.md file), but there are two types:

┌─────────────────────────────────────────────────────────────────────────────┐
│                    AGENT SKILLS (Higher-Level)                              │
│         Skills that orchestrate other skills + have sub-agents              │
│                                                                             │
│  ┌─────────────────────┐  ┌─────────────────────┐  ┌─────────────────────┐ │
│  │ patent-lawyer-agent │  │ product-engineer-   │  │ video-producer-     │ │
│  │   5 sub-agents      │  │     agent           │  │     agent           │ │
│  │   uses: image-gen   │  │   5 sub-agents      │  │   uses: video-gen   │ │
│  │         chart-gen   │  │   uses: image-gen   │  │         voice-gen   │ │
│  └─────────────────────┘  └─────────────────────┘  └─────────────────────┘ │
│                                      │ calls                                │
├──────────────────────────────────────▼──────────────────────────────────────┤
│                    BASE SKILLS (Single-Purpose)                             │
│               Do ONE thing well - can be used directly or by agents         │
│                                                                             │
│  ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐       │
│  │ image-gen    │ │ video-gen    │ │ voice-gen    │ │ music-gen    │       │
│  │ Generate     │ │ Generate     │ │ Generate     │ │ Generate     │       │
│  │ images       │ │ videos       │ │ speech       │ │ music        │       │
│  └──────────────┘ └──────────────┘ └──────────────┘ └──────────────┘       │
│  ┌──────────────┐ ┌──────────────┐ ┌──────────────┐                        │
│  │ chart-gen    │ │ slide-gen    │ │ media-utils  │                        │
│  │ Data charts  │ │ PPTX slides  │ │ Concat/mix   │                        │
│  └──────────────┘ └──────────────┘ └──────────────┘                        │
└─────────────────────────────────────────────────────────────────────────────┘

Key Difference:

  • Skills = Single-purpose tools. Do ONE thing (generate an image, create a chart, make a video).
  • Agents = Higher-level skills that orchestrate multiple other skills + have specialized sub-agents.

Note: Agents are still skills (they have SKILL.md files), but they're a higher-level type that combines other skills in their execution. Think of it as: agents are skills that use skills.


Base Skills (Single-Purpose)

Base skills are focused tools that do one thing well. They can be used directly or called by agent skills.

SkillWhat It DoesAPI Keys
image-generationGenerate/edit images (Gemini, DALL-E)GOOGLE_API_KEY or OPENAI_API_KEY
icon-generationGenerate app icons with transparent backgroundsGOOGLE_API_KEY
background-removeRemove backgrounds from images (AI-based)None (pip install rembg)
video-generationGenerate videos (Veo, Sora)GOOGLE_API_KEY or OPENAI_API_KEY
voice-generationText-to-speech (Gemini TTS, ElevenLabs, OpenAI)GOOGLE_API_KEY, ELEVENLABS_API_KEY, or OPENAI_API_KEY
music-generationGenerate music (Lyria, Suno, Udio)GOOGLE_API_KEY, SUNO_API_KEY, or UDIO_API_KEY
chart-generationData-driven charts (matplotlib)None (pip install matplotlib)
slide-generationPowerPoint slides from JSONNone (pip install python-pptx)
device-framerWrap screenshots/recordings in iPhone framesNone (pip install Pillow, brew install ffmpeg)
media-utilsConcat/mix audio/video (FFmpeg)None (brew install ffmpeg)
docxCreate/edit Word documents (OOXML)None
pptxCreate/edit PowerPoint (advanced)None (npm install pptxgenjs)
xlsxCreate/edit Excel spreadsheetsNone
pdfPDF forms, extraction, validationNone

Coding Skills

Skills for development workflows (no API keys needed):

SkillWhat It Does
style-guideAnalyze codebase conventions, generate style guide
ios-to-androidPort iOS/Swift features to Android/Kotlin
android-to-iosPort Android/Kotlin features to iOS/Swift
add-to-xcodeAuto-register new files with Xcode projects
sidequestSpawn parallel Claude sessions in new terminal tabs
debug-councilMulti-agent debugging with majority voting
feature-councilMulti-agent feature implementation, synthesize best parts
parallel-builderDecompose plans into parallel tasks
model-councilGet consensus from multiple AI models
auto-permissions-reviewPer-session AI permission review using Claude Haiku

Auto Permissions Review

Reduces permission prompt fatigue by auto-approving safe operations and sending ambiguous commands to Haiku for review. Per-session — each terminal enables independently.

ToolDefault modeAccept-edits mode (Shift+Tab)
Read, Glob, Grep, LS, Agentinstant allowinstant allow
Simple Bash (ls, cat, find, git status)instant allowinstant allow
Complex Bash (pipes, substitution)Haiku reviewsHaiku reviews
Edit, Writenormal prompt (you decide)Haiku reviews
/auto-permissions-review-install   # one-time setup
/auto-permissions-review-enable    # turn on (this session)
/auto-permissions-review-disable   # turn off (this session)

Agent Skills (Orchestrators)

Agent skills are higher-level skills that:

  • Call other base skills (image-gen, chart-gen, voice-gen, etc.)
  • Have specialized sub-agents for different perspectives
  • Handle complete workflows from start to finish

All agent skills use the -agent suffix to indicate they orchestrate other skills.

Professional Agents

Business analysis, research, and strategy:

AgentWhat It DoesSub-AgentsSkills Used
cmo-agentAI CMO: SEO audit, content, Reddit, HN, X growth6 (seo, geo, content-writer, reddit, hackernews, x)site_audit.py, chart-generation
brand-research-agentAnalyze brands from websites5 (visual, voice, product, audience, competitive)None
product-engineer-agentDesign products with specs + visuals5 (industrial, mechanical, user, manufacturing, innovation)image-generation
market-researcher-agentResearch markets (TAM/SAM/SOM)4 (trend, consumer, industry, opportunity)chart-generation
patent-lawyer-agentPatent drafting + IP guidance5 (prior-art, patentability, claims, strategy, drafter)image-generation
competitive-intel-agentAnalyze competitors4 (feature, pricing, positioning, market)chart-generation, image-generation
copywriter-agentMarketing copy4 (headlines, body, ads, CTA)None
review-analyst-agentAnalyze product reviews4 (scraper, sentiment, issues, recommendations)chart-generation
pitch-deck-agentCreate pitch decksWorkflowslide-generation, chart-generation, image-generation

Producer Agents

Create complete media by combining multiple generation skills:

AgentWhat It CreatesSkills Used
walkthrough-script-agentWalkthrough video scripts for app featuresapp-demo-agent, voice-gen
video-producer-agentComplete videos with voiceover + musicvideo-gen, voice-gen, music-gen, media-utils
podcast-producer-agentPodcast episodes, dialoguesvoice-gen, music-gen, media-utils
audio-producer-agentAudiobooks, ads, jinglesvoice-gen, music-gen, media-utils
social-producer-agentMulti-asset content packsimage-gen, video-gen, voice-gen
app-demo-agentPolished demos from screen recordingsdevice-framer, voice-gen, music-gen, media-utils

How Agents Use Skills

Example: patent-lawyer-agent workflow:

User: "Draft a patent for my self-watering planter"
                    │
                    ▼
┌─────────────────────────────────────────────────────┐
│             patent-lawyer-agent                     │
│                                                     │
│  1. prior-art-searcher    → Finds existing patents  │
│  2. patentability-analyst → Assesses novelty        │
│  3. claims-strategist     → Drafts claims           │
│  4. ip-strategy-advisor   → Recommends approach     │
│  5. patent-drafter        → Writes full application │
│                    │                                │
│                    ▼ calls                          │
│         ┌─────────────────────┐                     │
│         │  image-generation   │ → Patent figures    │
│         └─────────────────────┘                     │
│         ┌─────────────────────┐                     │
│         │  chart-generation   │ → Patent landscape  │
│         └─────────────────────┘                     │
└─────────────────────────────────────────────────────┘
                    │
                    ▼
Output: Complete patent document + generated figures

How Producers Work

  1. Understand - Parse your request (duration, style, assets)
  2. Plan - Create storyboard/manifest of what to generate
  3. Generate - Call generation skills (Veo, Gemini TTS, Lyria, etc.)
  4. Assemble - Stitch everything together with FFmpeg
  5. Deliver - Provide final file + offer adjustments

Example: Creating a Product Video

USER: "Create a 30-second product video for my new wireless earbuds"

PRODUCER WORKFLOW:
1. Asks: Duration? Style? Have product images?
2. Plans: 5 scenes (reveal, features, lifestyle, CTA)
3. Generates:
   - 5 video clips (Veo 3.1)
   - Voiceover script (Gemini TTS)
   - Background music (Lyria)
4. Assembles:
   - Concat clips with transitions
   - Mix voice + music (music ducks under voice)
   - Merge audio with video
5. Delivers: final_product_video.mp4

OUTPUT: Professional video with VO, music, transitions

Prerequisites for Producers

# FFmpeg for media assembly
brew install ffmpeg      # macOS
apt install ffmpeg        # Linux

# Python package for Google APIs
pip install google-genai

Using Professional Agents with Producers

Combine professional agents with producer agents for complete workflows:

USER: "Analyze Nike's brand, then create a product video for my sneakers"

WORKFLOW:
1. brand-research-agent analyzes nike.com
   → Extracts colors, typography, voice, audience
   → Saves brand_profile.json

2. video-producer-agent uses brand_profile.json
   → Matches Nike's visual style
   → Uses appropriate music mood
   → Follows voice guidelines

RESULT: Video that feels "Nike-like"
USER: "Research the smart home market, design a new product, then create a pitch deck"

WORKFLOW:
1. market-researcher-agent → Market report with TAM/SAM/SOM
2. product-engineer-agent → Product spec with BOM
3. patent-lawyer-agent → IP assessment
4. pitch-deck-agent → Investor presentation

RESULT: Complete product launch package

Agents

Debug Solvers (for debug-council)

10 debug solver agents focused on finding bugs:

AgentsPurpose
debug-solver-1 through debug-solver-10Independent bug finding and fixing

Focus: Root cause analysis, finding the ONE correct fix, chain-of-thought debugging.

Feature Solvers (for feature-council)

10 feature solver agents focused on building features:

AgentsPurpose
feature-solver-1 through feature-solver-10Independent feature implementation

Focus: Codebase pattern matching, edge case coverage, comprehensive implementation.

Builder Solvers (for parallel-builder)

10 builder solver agents focused on implementing assigned pieces:

AgentsPurpose
builder-solver-1 through builder-solver-10Implement assigned piece of decomposed plan

Focus: File ownership, shared contracts, parallel execution, integration.

Style Analyzers (for style-guide)

5 specialized analyzer agents, each focused on one aspect:

AgentFocus
style-structureFolder organization, file layout, module patterns
style-namingNaming conventions for files, variables, functions, classes
style-patternsError handling, data access, logging, configuration
style-testingTest location, naming, structure, assertions
style-frontendComponent patterns, styling, state (if applicable)

Focus: Language-agnostic detection, real examples from codebase, structured output.

CMO Specialists (for cmo-agent)

6 specialized marketing agents that work in parallel:

AgentFocus
seo-analystTechnical SEO audit with exact HTML fix snippets
geo-analystAI search visibility (ChatGPT, Perplexity, Google AI Overview)
content-writerFull SEO articles (1500-3000 words) + 4-week content calendar
reddit-scoutActive thread discovery + copy-paste-ready comments with risk assessment
hackernews-scoutShow HN submission + founder comment + objection responses
x-scoutTweet threads + standalone tweets + 7-day calendar + influencer mapping

Also includes site_audit.py: stdlib-only technical SEO crawler with 3-tier scoring (static analysis, PageSpeed Insights API, Lighthouse CLI).

Brand Analysts (for brand-research-agent)

5 specialized brand analysts that work in parallel:

AgentFocus
visual-analystColors, typography, logo, imagery style
voice-analystTone, messaging, taglines, copy patterns
product-analystOfferings, features, USPs, pricing
audience-analystDemographics, psychographics, pain points
competitive-analystMarket position, competitors, differentiation

Focus: Web scraping, pattern extraction, structured brand profile output.

Product Engineers (for product-engineer-agent)

5 specialized engineering perspectives + visual generation:

AgentFocus
industrial-designerForm, ergonomics, aesthetics + generates concept renders
mechanical-engineerMechanism, materials, assembly + generates exploded views
user-researcherUser needs, pain points, usability
manufacturing-advisorFeasibility, costs, production
innovation-scoutExisting solutions, patents, differentiation

Market Researchers (for market-researcher-agent)

4 specialized market analysis perspectives:

AgentFocus
trend-analystMarket size, growth, trends, future outlook
consumer-researcherCustomer segments, behavior, needs
industry-analystMarket structure, players, dynamics
opportunity-finderGaps, opportunities, entry points

Patent Analysts (for patent-lawyer-agent)

5 specialized IP perspectives:

AgentFocus
prior-art-searcherFind existing patents, publications
patentability-analystAssess novelty, non-obviousness
claims-strategistDraft claims, claim strategy
ip-strategy-advisorProtection strategy, timing, costs
patent-drafterDraft complete patent applications with generated figures

Copywriters (for copywriter-agent)

4 specialized copywriting perspectives:

AgentFocus
headlines-writerHeadlines, hooks, taglines
body-copy-writerLong-form persuasive copy
ad-copy-writerPlatform-specific ad copy
cta-specialistCalls to action, conversion copy

Competitive Analysts (for competitive-intel-agent)

4 specialized competitive analysis perspectives:

AgentFocus
feature-analystProduct features, capabilities
pricing-analystPricing models, value comparison
positioning-analystBrand positioning, messaging
market-position-analystMarket share, company health

Review Analysts (for review-analyst-agent)

4 specialized review analysis perspectives:

AgentFocus
review-scraperFind and collect reviews from platforms
sentiment-analyzerAnalyze sentiment, emotions, trends
issue-identifierCategorize complaints, find patterns
improvement-recommenderPrioritize fixes, create action plans

Both debug and feature agent types:

  • Same temperature (0.7) for sampling diversity
  • Same tools (Read, Grep, Glob, LS)
  • Use ultrathink (extended thinking)
  • Explore the codebase independently

Builder agents are different:

  • Lower temperature (0.4) for consistency
  • Full tools including Write and Shell
  • Implement assigned pieces only
  • Follow shared contracts exactly

Council skills will ask you how many agents to use (3-10), or specify directly:

ModeAgentsUse Case
debug council of 33Fast, simple bugs
debug council of 55Standard debugging
debug council of 1010Critical bugs
feature council of 33Simple features
feature council of 55Standard features
feature council of 1010Complex features

Minimum 3 agents for councils - needed for meaningful voting/synthesis.

Parallel-builder uses as many agents as needed based on task decomposition (up to 10).

These agents are invoked automatically by their skills and should not be called directly.


Usage Examples

style-guide

Analyze a codebase to extract its conventions and patterns. Generates a reusable style guide:

style guide

generate style guide for this project

analyze codebase conventions

How it works:

  1. Quick language detection - Identifies project type
  2. 5 specialized analyzers spawn in parallel:
    • Structure: folder layout, modules
    • Naming: files, variables, functions, classes
    • Patterns: error handling, data access, logging
    • Testing: test location, naming, structure
    • Frontend: components, styling (if applicable)
  3. Synthesize findings into comprehensive guide
  4. Save to .claude/codebase-style.md

Output:

  • Structured style guide with real examples
  • Can be referenced by other skills (feature-council, debug-council)
  • Run once per codebase, update when patterns change

ios-to-android

Use iOS/Swift code as reference to implement the equivalent Android feature:

ios to android: implement this feature for Android

convert this Swift code to Kotlin

port UserProfile from iOS to Android

How it works:

  1. Analyze iOS code - Understand feature behavior, data structures, logic
  2. Check Android context - Look for existing patterns, style-guide
  3. Create implementation plan - Map iOS components to Android equivalents
  4. Implement idiomatically - Kotlin/Compose, not literal translation

Key principle: Same behavior, same data shapes, but idiomatic for each platform.


android-to-ios

Use Android/Kotlin code as reference to implement the equivalent iOS feature:

android to ios: implement this feature for iOS

convert this Kotlin code to Swift

port UserProfile from Android to iOS

Works the same as ios-to-android but in reverse direction.


add-to-xcode

Automatically register newly created source files with Xcode projects:

Create a new ProfileViewModel.swift in the ViewModels folder

What happens:

  1. Agent creates the Swift file
  2. Agent runs add_to_xcode.rb to register it with the .xcodeproj
  3. File appears in Xcode navigator and compiles with the target

Manual usage:

# After creating any source file in an Xcode project
ruby ${CLAUDE_PLUGIN_ROOT}/skills/add-to-xcode/scripts/add_to_xcode.rb Sources/MyNewFile.swift

Supported files: .swift, .m, .mm, .c, .cpp, .h

Requires: gem install xcodeproj


sidequest

Spawn a new Claude Code session in a separate terminal to work on a different task:

/sidequest "Add a settings page with dark mode toggle"

/sidequest "Set up the database schema" --no-context

/sidequest  # Interactive prompt for task description

What happens:

  1. Claude asks if you want to include a summary of the current chat
  2. Opens a new Terminal/iTerm tab
  3. Starts Claude with the sidequest task (and optional context)
  4. You continue working in your original session

Use when: You're deep in a task but need to branch off for something else without losing your place.

macOS only (uses osascript for terminal control)


debug-council

Research-aligned self-consistency for debugging. Each agent explores and debugs independently - no shared context:

debug council: fix this bug in my function

debug council of 5: important production issue

debug council of 10: critical bug, need maximum confidence

How it works (pure Wang et al., 2022):

  1. Raw user prompt sent to all debug agents (no pre-processing)
  2. Each agent independently explores the codebase
  3. Each agent uses ultrathink to find the root cause
  4. Solutions are grouped by their core fix
  5. Majority voting selects the most common answer
  6. Confidence based on voting distribution (5/7 agree = HIGH)

Note: This is slower than shared-context approaches because each agent explores independently. Use for critical bugs where accuracy matters more than speed.

feature-council

Multi-agent feature implementation. Each agent builds the feature independently, then synthesizes the best parts:

feature council: implement user authentication with OAuth

feature council of 5: add caching layer to the API

feature council of 10: complex payment integration

How it works:

  1. Raw user prompt sent to all agents (no pre-processing)
  2. Each agent independently explores the codebase
  3. Each agent implements the complete feature
  4. Implementations are compared across multiple dimensions
  5. Synthesis combines the best elements from each
  6. Implementation Plan created with exact files and order
  7. Execute plan step-by-step

Output shows:

  • What each agent contributed
  • Implementation plan with file order
  • Synthesis breakdown (which agent provided what)

parallel-builder

Divide-and-conquer implementation from specs, PRDs, or plans. Decomposes into parallel tasks:

parallel-builder from docs/auth-prd.md

parallel-builder: full CRUD API for blog with posts, comments, users

parallel-builder something like src/features/users but for products

How it works:

  1. Analyze the plan - identify independent work units and dependencies
  2. Define shared contracts - types/interfaces all agents must use
  3. Show execution plan - user confirms task breakdown and waves
  4. Execute in waves - parallel agents build their pieces simultaneously
  5. Integrate - merge all pieces, resolve conflicts, verify

Key differences from feature-council:

  • Each agent builds a different piece (not the same feature)
  • Focus on speed via parallelization (not diversity of approaches)
  • Agents respect file ownership (no overlaps)
  • Results are integrated (not synthesized)

Where it shines (maximum speedup):

  • Multi-file specs (types + services + routes + UI)
  • CRUD APIs (each resource in separate files)
  • Microservices (independent service files)
  • Plugin/module systems

Falls back to sequential when:

  • Multiple tasks modify the same file (to avoid conflicts)
  • Still useful for organized task breakdown

Output shows:

  • Wave execution progress
  • Files created per agent
  • Integration results
  • Verification status
  • Estimated vs actual speedup

model-council

Get consensus from multiple AI models (Claude, GPT, Gemini, Grok):

model council: review this architecture decision

model council with claude, gpt-4o: is this code secure?

model council all: critical decision, need all perspectives

image-generation

Generate images with AI:

generate an image of a sunset over mountains

create a cyberpunk cityscape at night

make a watercolor painting of a cat

icon-generation

Generate app icons with transparent backgrounds:

generate an icon for a music app

create a flat style settings gear icon

make a 3D shopping cart icon for my e-commerce app

background-remove

Remove backgrounds from images:

remove the background from this photo

make this image transparent

cut out the product from this image

video-generation

Generate videos with AI:

generate a video of waves crashing on a beach at sunset

create a cinematic drone shot flying over mountains

make a video of a cat playing with yarn

voice-generation

Generate speech and audio:

read this text aloud: "Hello, welcome to my podcast"

generate a voiceover for this script

create narration for my video using a deep male voice

music-generation

Generate music and songs:

create an upbeat pop song about summer

generate a cinematic orchestral soundtrack

make a lo-fi hip hop beat for studying

slide-generation

Create presentation slides:

create slides from this content: [paste JSON]

generate a PowerPoint presentation for my pitch

make slides for my market research report

device-framer

Wrap screenshots and screen recordings in photorealistic iPhone frames:

frame this screenshot in an iPhone 16 Pro

wrap this screen recording in a device mockup

put this in an iPhone 17 Pro in cosmic orange on a dark background

chart-generation

Generate data-driven charts from data:

create a bar chart comparing our features to competitors

plot our monthly revenue: [100, 150, 220, 350]

generate a competitive positioning matrix

create a TAM/SAM/SOM chart: TAM $50B, SAM $5B, SOM $500M

make a pie chart showing use of funds

Professional Agent Examples

cmo-agent

AI Chief Marketing Officer — enter a URL and get a full marketing team deployed:

be my AI CMO for https://mysite.com

run a full SEO audit on https://myapp.io and give me exact fixes

find Reddit and Hacker News opportunities for my product

write SEO articles for my site and create a content calendar

How it works:

  1. Onboarding - Just provide a URL (+ optional context)
  2. Site Audit - site_audit.py crawls the site, scores SEO/Accessibility/Performance/Best Practices
  3. 6 agents deploy in parallel - SEO, GEO, Content Writer, Reddit, HN, X/Twitter
  4. Cross-channel synthesis - Narrative spines + content cascades across channels
  5. Dashboard - Terminal-formatted overview with scores, opportunities, and prioritized actions

Output includes:

  • SEO audit with exact HTML fix snippets (copy-paste ready)
  • GEO recommendations with JSON-LD schema code
  • Full 1500-3000 word SEO articles ready to publish
  • Reddit comments for specific active threads (with risk levels)
  • Show HN submission + founder comment + objection responses
  • Tweet threads + 7-day content calendar + influencer targets
  • Prioritized "Do This Now" action list

Optional API keys: GOOGLE_PSI_API_KEY for PageSpeed Insights scores, lighthouse CLI for full browser audit.


brand-research-agent

Analyze a brand from their website:

analyze the Nike brand from their website

research Apple's brand guidelines

what's the brand voice for Stripe?

product-engineer-agent

Design new products with specs and visuals:

design a new portable phone charger

I have an idea for a smart water bottle, help me develop it

create a product spec for a pet feeding device

design a modular desk organizer and show me concept renders

create an exploded view of my product design

market-researcher-agent

Research markets and opportunities:

what's the market size for smart home devices?

research the plant-based food market trends

is there an opportunity in sustainable packaging?

patent-lawyer-agent

IP guidance and patent drafting (informational only):

is my invention patentable?

search for prior art on foldable drone designs

should I patent this or keep it as trade secret?

draft a full patent application for my invention

create a patent with figures for my self-watering planter

pitch-deck-agent

Create investor presentations:

create a pitch deck for my AI startup

build a seed round presentation

make investor slides for my SaaS company

copywriter-agent

Write marketing copy:

write headlines for our product launch

create ad copy for our Black Friday sale

write landing page copy for our new app

competitive-intel-agent

Analyze competitors:

analyze our competitors: Salesforce, HubSpot, Pipedrive

what are Notion's weaknesses?

create a competitive battlecard for sales

review-analyst-agent

Analyze customer reviews:

analyze reviews for our product on Amazon

what are people complaining about with [competitor]?

find the top issues we should fix from customer feedback

Producer Agent Examples

video-producer-agent

Create complete videos with voiceover and music:

create a 30-second product video for my headphones

make a demo video for my SaaS app

create an explainer video about how our service works

podcast-producer-agent

Create podcast episodes and dialogues:

create a 5-minute podcast about AI with two hosts

make a fake interview between Einstein and Elon Musk

create an educational podcast episode about climate change

audio-producer-agent

Create voiceovers, audiobooks, and audio ads:

create a 30-second radio ad for our coffee brand

generate an audiobook narration for this chapter

make a meditation audio with calming background music

social-producer-agent

Create social media content packs:

create a launch kit: 1 reel, 5 carousel images

make a week of social content for our product

create TikTok content for our new feature

app-demo-agent

Turn screen recordings into polished demo videos:

here's a screen recording of my app — turn it into a polished demo video

add voiceover to this screen recording: ~/Desktop/demo.mp4

take ~/Desktop/recording.mov, frame it in iPhone 17 Pro, add narration and music

Troubleshooting

Agents Hitting Token Limits

If you see errors like:

API Error: Claude's response exceeded the 32000 output token maximum

Solution: Increase the max output tokens (only uses more when needed):

# Add to ~/.bashrc or ~/.zshrc
export CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000

Then restart Claude Code.

This commonly happens with feature-council on complex features where agents generate complete implementations. The 64K limit allows full outputs without truncation.


Missing API Key Error

If you see an error like:

OPENAI_API_KEY environment variable not set

Solution:

  1. Get your API key from the provider (links above)
  2. Export it in your terminal:
    export OPENAI_API_KEY="sk-your-key-here"
    
  3. For persistence, add the export to your shell profile (~/.bashrc, ~/.zshrc, or ~/.bash_profile)
  4. Restart your terminal or run source ~/.bashrc

API Rate Limit / Quota Exceeded

If you hit rate limits:

  • Wait a few minutes and try again
  • Check your API usage dashboard
  • Upgrade your plan if needed
  • Try a different API (e.g., Google instead of OpenAI)

Generation Failed

Common causes:

  • Content policy violation: Rephrase your prompt to be more appropriate
  • Network error: Check your internet connection
  • Invalid parameters: Check the error message for specifics

Skill Not Triggering

If Claude doesn't use a skill when you expect it to:

  • Use explicit trigger phrases (e.g., "generate an image of...")
  • Check that the plugin is installed: /plugin list
  • Update the plugin: /plugin update skills@michaelboeding-skills

Plugin Not Updating / Missing Skills

If you update the plugin but Claude Code still uses an old version, or skills are missing:

Quick fix - run the update script:

# From the skills repo directory
./scripts/update-plugin.sh

Or manually clear the cache:

rm -rf ~/.claude/plugins/cache/michaelboeding-skills
rm -rf ~/.claude/plugins/cache/temp_local_*

Then in Claude Code:

/plugin update skills@michaelboeding-skills

Then restart Claude Code (quit and reopen - required for changes to take effect).

Script Errors

If a script fails to run:

  1. Ensure Python 3 is installed: python3 --version
  2. Check the API key is exported: echo $OPENAI_API_KEY
  3. Run the script directly to see detailed errors:
    python3 ~/.claude/plugins/marketplaces/michaelboeding-skills/skills/image-generation/scripts/dalle.py --prompt "test" 
    

Architecture Mismatch Error (Apple Silicon Macs)

If you see this error:

Architecture Mismatch Error
dlopen(...pydantic_core...incompatible architecture (have 'x86_64', need 'arm64'))

Cause: Pip installed x86_64 packages when running under Rosetta emulation.

Fix:

# Force arm64 architecture for pip installs
/usr/bin/arch -arm64 pip3 install --force-reinstall pydantic pydantic-core google-genai

Prevention:

  1. Run the install script (it auto-detects Apple Silicon):
    ./scripts/install.sh
    
  2. Or ensure Claude Code isn't running under Rosetta:
    • Right-click Claude Code app → Get Info
    • Uncheck "Open using Rosetta"
    • Restart Claude Code

Error Messages

All skills provide clear error messages when something goes wrong:

ErrorMeaningSolution
API_KEY environment variable not setMissing API keyExport the required key (see Setup section)
API error (401)Invalid API keyCheck your key is correct and active
API error (429)Rate limit exceededWait and retry, or use different API
API error (400)Bad requestCheck your prompt/parameters
Content policy violationPrompt rejectedRephrase to be appropriate
Text too longExceeded character limitShorten your text or split into parts

License

MIT

数据与 AI内容与创作
chartdatavisualization

低风险

  • 来源需自行核对维护者身份。
  • 未检测到明显脚本安装指令。
  • 未检测到明显外部权限要求。
  • 未检测到高风险命令。
  • 扫描发现:3 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/example/repo.git
  3. 将 "repo" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/example/repo.git
  3. 将 "repo" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/example/repo.git
  3. 将 "repo" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/example/repo.git
  3. 将 "repo" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/example/repo.git
  3. 将 "repo" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: chart-generation
description: >
  Use this skill for generating data-driven charts and visualizations using Python.
  Triggers: "create chart", "generate graph", "plot data", "visualize data", "bar chart",
  "line chart", "pie chart", "comparison chart", "positioning matrix", "trend chart",
  "market size chart", "TAM SAM SOM", "growth chart", "data visualization"
  Outputs: PNG/SVG chart images with accurate data representation.
  Used by: competitive-intel-agent, market-researcher-agent, pitch-deck-agent, review-analyst-agent

Chart Generation Skill

Generate accurate, data-driven charts and visualizations using Python (matplotlib/plotly).

Use this for real data. For concept art and illustrations, use image-generation instead.

What It Produces

Chart TypeUse CaseScript
Bar ChartCompare values across categoriesbar_chart.py
Line ChartShow trends over timeline_chart.py
Pie ChartShow proportions/percentagespie_chart.py
Positioning Matrix2x2 competitive positioningpositioning_matrix.py
Comparison TableFeature comparison gridcomparison_table.py
TAM/SAM/SOMMarket size visualizationtam_sam_som.py

Prerequisites

pip install matplotlib numpy pillow

No API keys required - runs locally.

When to Use This vs Image Generation

ScenarioUse ThisUse image-generation
Real data from analysis✅❌
Accurate numbers/labels✅❌
Reproducible charts✅❌
Concept/mockup visuals❌✅
Artistic illustrations❌✅
Icons and graphics❌✅

Chart Types

1. Bar Chart

Compare values across categories.

python3 ${SKILL_PATH}/skills/chart-generation/scripts/bar_chart.py \
  --labels '["Product A", "Product B", "Product C"]' \
  --values '[85, 62, 45]' \
  --title "Feature Comparison" \
  --ylabel "Score" \
  --output bar_chart.png

Options:

  • --horizontal - Horizontal bars instead of vertical
  • --colors - Custom colors: '["#4CAF50", "#2196F3", "#FF9800"]'
  • --show-values - Display values on bars

2. Line Chart

Show trends over time or progression.

python3 ${SKILL_PATH}/skills/chart-generation/scripts/line_chart.py \
  --x '["Jan", "Feb", "Mar", "Apr", "May", "Jun"]' \
  --y '[100, 150, 180, 220, 310, 450]' \
  --title "Monthly Revenue Growth" \
  --xlabel "Month" \
  --ylabel "Revenue ($K)" \
  --output growth_chart.png

Options:

  • --multi - Multiple lines: --y '[[100,150,200], [80,120,180]]' --legend '["Product A", "Product B"]'
  • --fill - Fill area under line
  • --markers - Show data point markers

3. Pie Chart

Show proportions and percentages.

python3 ${SKILL_PATH}/skills/chart-generation/scripts/pie_chart.py \
  --labels '["Engineering", "Marketing", "Sales", "Operations"]' \
  --values '[40, 25, 20, 15]' \
  --title "Use of Funds" \
  --output pie_chart.png

Options:

  • --donut - Donut chart (hollow center)
  • --explode - Explode a slice: --explode 0 (first slice)
  • --show-percent - Show percentages on slices

4. Positioning Matrix (2x2)

Competitive positioning on two axes.

python3 ${SKILL_PATH}/skills/chart-generation/scripts/positioning_matrix.py \
  --companies '["Your Product", "Competitor A", "Competitor B", "Competitor C"]' \
  --x-values '[70, 90, 50, 30]' \
  --y-values '[80, 85, 60, 45]' \
  --x-label "Price (Low → High)" \
  --y-label "Features (Basic → Advanced)" \
  --title "Competitive Positioning" \
  --output positioning.png

Options:

  • --quadrant-labels - Label quadrants: '["Niche", "Leaders", "Laggards", "Challengers"]'
  • --highlight - Highlight your position: --highlight 0
  • --sizes - Bubble sizes for market share

5. Comparison Table

Feature comparison grid as an image.

python3 ${SKILL_PATH}/skills/chart-generation/scripts/comparison_table.py \
  --features '["Feature A", "Feature B", "Feature C", "Feature D"]' \
  --companies '["You", "Comp A", "Comp B"]' \
  --data '[["✓", "✓", "✗"], ["✓", "✗", "✓"], ["✓", "✓", "✓"], ["✓", "✗", "✗"]]' \
  --title "Feature Comparison" \
  --output comparison.png

Options:

  • --highlight-column - Highlight your column: --highlight-column 0
  • --colors - Use colors instead of symbols

6. TAM/SAM/SOM Chart

Market size visualization (concentric circles).

python3 ${SKILL_PATH}/skills/chart-generation/scripts/tam_sam_som.py \
  --tam 50 \
  --sam 8 \
  --som 0.5 \
  --unit "B" \
  --title "Market Opportunity" \
  --output market_size.png

Options:

  • --unit - "B" for billions, "M" for millions
  • --labels - Custom labels: '["Total Market", "Serviceable", "Obtainable"]'

Usage by Other Skills

competitive-intel-agent

# Generate positioning matrix from analysis
positioning_matrix.py \
  --companies '["You", "Salesforce", "HubSpot"]' \
  --x-values '[30, 95, 70]' \
  --y-values '[75, 90, 60]'

market-researcher-agent

# Generate TAM/SAM/SOM from research
tam_sam_som.py --tam 120 --sam 15 --som 2.5 --unit "B"

pitch-deck-agent

# Generate traction chart
line_chart.py \
  --x '["Q1", "Q2", "Q3", "Q4"]' \
  --y '[50, 120, 280, 500]' \
  --title "Revenue Growth"

review-analyst-agent

# Generate sentiment distribution
pie_chart.py \
  --labels '["Positive", "Neutral", "Negative"]' \
  --values '[65, 20, 15]' \
  --title "Review Sentiment"

Output Formats

All scripts support:

  • --output file.png - PNG image (default)
  • --output file.svg - SVG vector
  • --output file.pdf - PDF document

Styling Options

All scripts support these common options:

OptionDescriptionExample
--titleChart title"Monthly Revenue"
--widthWidth in inches12
--heightHeight in inches8
--dpiResolution150
--styleMatplotlib style"seaborn", "dark_background"
--colorsCustom color palette'["#4CAF50", "#2196F3"]'
--font-sizeBase font size12

Integration Pattern

Higher-level skills call chart-generation like this:

## In competitive-intel-agent workflow:

1. Analyze competitors (gather data)
2. Structure data as JSON
3. Call chart-generation script with data
4. Embed resulting PNG in report

Example flow:

# 1. Analysis produces this data
data = {
    "companies": ["You", "Competitor A", "Competitor B"],
    "features": [8, 6, 5],
    "prices": [29, 49, 39]
}

# 2. Generate chart
python3 bar_chart.py \
  --labels '["You", "Competitor A", "Competitor B"]' \
  --values '[8, 6, 5]' \
  --title "Feature Count Comparison" \
  --output features.png

# 3. Embed in report
![Feature Comparison](features.png)

Example Prompts

Direct chart creation:

"Create a bar chart comparing our features to competitors"

As part of analysis:

"Analyze these companies and generate a positioning matrix"

Data visualization:

"Plot our monthly revenue growth from this data: [100, 150, 220, 350]"

Market sizing:

"Create a TAM/SAM/SOM chart: TAM $50B, SAM $5B, SOM $500M"

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