7 Claude skills for creative teams (full setup guide)

npm install -g @anthropic-ai/claude-code
claude

---
name: voice-dna
description: Analyze writing samples and extract a reusable voice profile. Use when the user wants to capture their writing voice.
allowed-tools: Read, Write
---

(skill instructions go here)

---
name: remotion-video
description: Generate a programmatic video from a script or brief using Remotion. Renders MP4 in 9:16 or 16:9 with text animations and brand styling. Use when the user asks to make a video, create a reel, or render a video from a script.
allowed-tools: Bash, Read, Write
---

# Remotion video

You are a Remotion video engineer. Generate a programmatic video from the provided script or brief.

## Step 1: ensure project exists

If a remotion-project/ folder doesn't exist in the working directory, create one by running:

	npx create-video@latest --yes --blank remotion-project
	cd remotion-project && npm install

## Step 2: parse the brief

Extract from the user's input:
1. Main message or headline
2. Key talking points or scenes
3. Duration (default 60s for 9:16, 30s for 16:9)
4. Aspect ratio (9:16 for reels, 16:9 for YouTube)
5. Brand colours (default cream #FAF7F2, Claude orange #D97757)

## Step 3: write src/Composition.tsx

A React component using Remotion primitives (AbsoluteFill, Sequence, useVideoConfig, interpolate, useCurrentFrame). Divide the script into Sequences (one per scene). Animate text with interpolate. Apply brand colours.

## Step 4: register in src/Root.tsx

Use Composition with id "MyVideo", durationInFrames at fps * total seconds, width/height set for the chosen aspect ratio (1080x1920 for 9:16, 1920x1080 for 16:9).

## Step 5: render

Run:

	npx remotion render src/index.ts MyVideo output.mp4

Render takes 2 to 5 minutes on typical hardware.

## Step 6: report

Print the file path of the rendered MP4 to the user.

{
  "mcpServers": {
    "apify": {
      "command": "npx",
      "args": ["-y", "@apify/actors-mcp-server"],
      "env": { "APIFY_TOKEN": "your_token_here" }
    }
  }
}

[
  { "name": "BrandA", "facebook_page_url": "https://facebook.com/branda", "linkedin_company_url": "https://linkedin.com/company/branda" },
  { "name": "BrandB", "facebook_page_url": "https://facebook.com/brandb", "linkedin_company_url": "https://linkedin.com/company/brandb" }
]

---
name: competitor-ads-extractor
description: Pulls Meta and LinkedIn ads for a list of competitor brands, diffs week-over-week, extracts hook patterns and offer structures. Use when the user asks about competitor ads, ad library, or weekly ad scan.
allowed-tools: Bash, Read, Write
---

# Competitor ads extractor

## Inputs

Read the competitors list from competitors.json. If none exists, ask the user for 3 to 10 competitor brand URLs and create the file.

## Run

For each competitor:
1. Call Apify Actor apify/facebook-ads-scraper with input { "urls": [facebook_page_url], "country": "AU", "activeStatus": "active" }
2. Call Apify Actor isotonic/linkedin-ads-library-competitive-creative-scraper with { "companyUrls": [linkedin_company_url] }
3. Save raw output to runs/YYYY-MM-DD/<brand>.json

## Diff

Compare today's run to the most recent previous run. For each brand surface:
- New ads launched this week (in today, not in last)
- Scaling ads (live in both, longer run-time, more variants)
- Killed ads (in last, not in today)

## Synthesise

Group new and scaling ads across competitors. Output a markdown brief with:
1. Hook patterns. First 5-10 words of every ad. Cluster by pattern (question, stat, callout, fear, curiosity).
2. Offer structures. Headline + CTA + price point. Note free trials, lead magnets, tripwires.
3. Visual formats. Static, carousel, video, UGC, talking head.
4. What's working. Brands with 3+ scaling ads of the same hook style.
5. Steal list. Top 5 hooks worth adapting.

## Output

Save brief to briefs/YYYY-MM-DD-brief.md. Print the steal list to chat.

{
  "env": {
    "TAVILY_API_KEY": "tvly-xxxxxxxxxxxx"
  }
}

---
name: deep-research
description: Researches a topic using Tavily web search, synthesises findings with citations, and generates 5 content angles. Use when the user asks for research, a brief, sources on a topic, or content angles.
allowed-tools: Bash, Read, Write
---

# Deep research

## Input

Topic from user. If vague, ask one clarifying question (audience, depth, recency window).

## Search

Run 3 Tavily search calls with different angles via curl POST to https://api.tavily.com/search with header "Authorization: Bearer $TAVILY_API_KEY" and a JSON body containing query, search_depth "advanced", max_results 8, include_answer true.

Query angles:
1. Plain topic plus current year
2. Topic plus "data," "stats," or "study"
3. Topic plus "what nobody talks about" or "underrated"

## Read deeper

For the 3 to 5 highest-scoring URLs, run Tavily extract via POST to https://api.tavily.com/extract with the URLs.

## Synthesise

Output a markdown brief with:
1. Executive summary. 3 to 5 sentences on the current state of play.
2. Key findings. 5 to 8 bullets, each cited as [source](url).
3. Counterpoints. Anything contradicting consensus.
4. Gaps. What's missing or stale in public coverage.
5. Five content angles. Each with a hook line, format (reel, carousel, long-form), and 1-line payoff.

## Output

Save to briefs/YYYY-MM-DD-<slug>.md. Print executive summary and 5 angles to chat.

---
name: voice-dna
description: Analyze 10-20 samples of the user's writing and produce a structured voice profile other skills can read. Use when the user says /voice-dna or wants to capture their writing voice.
allowed-tools: Read, Write
---

# Voice DNA

You are extracting a reusable voice signature from a user's existing writing. The output is a reference document, not a vibe summary. Be analytical. Pull concrete examples and quote them verbatim.

## Step 1: gather samples

Ask the user how they want to provide samples:
- Paste 10 to 20 pieces directly in chat
- Or give a folder path. Read every .md and .txt file in it.

If fewer than 10 samples, warn the profile will be thinner than ideal but proceed.

## Step 2: analyze across all samples

1. Sentence length. Average words per sentence. Range. Sentence-fragment frequency as %.
2. Vocabulary level. Formality on a 1-10 scale with justification. Top 20 distinctive words and bigrams. Skip stopwords.
3. Tonal markers. Place voice on three axes: formal-casual, warm-edgy, contrarian-agreeable. Quote evidence per axis.
4. Pacing. Hook openers, mid-piece pivots, sign-offs. Quote 3-5 examples of each.
5. Anti-patterns. Words and moves the user never uses. Cross-check common tics: em dashes, "in today's world," "let's dive in," "the truth is," exclamation points, hashtags.
6. Signature moves. 5 to 10 repeatable patterns unique to this voice. Quote one occurrence each.
7. Audience address. Frequency of "you" vs "we" vs "I." Direct vs indirect.
8. Emotional range. Which emotions show up, which are absent. One quoted example per emotion present.

## Step 3: write the profile

Save to ~/.claude/voice-profile.md. Overwrite if it exists.

Sections in order: Snapshot (one paragraph another skill can paste at the top of a system prompt), Sentence Mechanics, Vocabulary, Tonal Markers, Pacing, Signature Moves, Anti-patterns, Audience Address, Emotional Range, Reference Lines (5 fully-quoted lines that best capture the voice).

## Step 4: confirm

Print the file path and the snapshot section. Tell the user to rerun /voice-dna any time their voice evolves.

{
  "env": {
    "ELEVENLABS_API_KEY": "sk_your_key_here"
  }
}

---
name: elevenlabs-podcast
description: Turn any long-form text into a two-host podcast episode. Generates a conversational script, voices each line through ElevenLabs, and stitches the audio into a finished MP3. Use when the user wants to convert a blog post, newsletter, or article into podcast audio.
allowed-tools: Bash, Read, Write
---

# ElevenLabs two-host podcast

## Inputs

User gives a file path or pasted text. If unclear, ask which.

## Voice IDs (defaults)

HOST_A: 21m00Tcm4TlvDq8ikWAM (Rachel, warm)
HOST_B: pNInz6obpgDQGcFmaJgB (Adam, analytical)

If a default voice ID is unavailable on your account, replace it with a live voice_id from GET https://api.elevenlabs.io/v1/voices.

## Step 1: read the source

Read the file or use the pasted text directly.

## Step 2: generate the script

Write a natural two-host conversation, ~1500 to 1800 words (8 to 12 min at 150 wpm).

Structure:
- Cold open. Host A hooks the topic in one sentence.
- Intro. Both hosts greet, name the show, set up.
- Body. 3 to 5 segments. Each = Host A introduces, Host B reacts/challenges/builds, 4 to 8 exchanges.
- Mini-debate. At least one moment of respectful disagreement.
- Wrap. Key takeaway from each host. Sign-off.

Format every line on its own row, prefixed exactly [HOST A] or [HOST B]. No stage directions, no music cues, no host names inside dialogue.

Save script to ./podcasts/script_<timestamp>.txt

## Step 3: voice each line

Create ./podcasts/audio_<timestamp>/ as a working folder.

For each line: use the matching voice ID, strip the [HOST X] prefix, save as 0001.mp3, 0002.mp3, etc. (zero-padded for concat order).

Call: POST to https://api.elevenlabs.io/v1/text-to-speech/<voice_id>?output_format=mp3_44100_128 with header "xi-api-key: $ELEVENLABS_API_KEY", body { "text": "...", "model_id": "eleven_multilingual_v2" }, save response as the indexed MP3.

If a line fails, retry once, then skip and log.

## Step 4: stitch with ffmpeg

Build filelist.txt inside the audio folder, one line per clip:

	file '0001.mp3'
	file '0002.mp3'

Then run:

	ffmpeg -f concat -safe 0 -i filelist.txt -c copy ../episode_<timestamp>.mp3

## Step 5: report

Tell the user: final file path, episode length (use ffprobe), approximate character count used, path to script file in case they want to tweak.

---
name: content-engine
description: Turn one pillar piece into 8 platform-ready formats (LinkedIn, X thread, IG caption, email, short-form script, blog intro, pull-quote carousel, cold outreach). Use when the user wants to repurpose a piece or spin one article into multi-platform content.
allowed-tools: Read, Write
---

# Content engine

Voice consistency comes from the profile. Hook variation comes from you.

## Step 1: load voice

Read ~/.claude/voice-profile.md if it exists. Apply the snapshot, signature moves, and anti-patterns to every output.

If missing, surface this warning before proceeding: "No voice profile found. Run /voice-dna first for best results. Continuing with neutral defaults."

## Step 2: get the pillar piece

Ask the user to paste content or give a file path. Read the full piece before drafting.

## Step 3: extract candidate hooks

Pull 2 to 3 hook candidates from the pillar. Look for: most contrarian claim, most specific number or proof, most relatable pain point, most surprising reframe. List them at the top of working notes. Different hooks go to different formats so the 8 outputs don't all open the same way.

## Step 4: draft 8 formats

Hard rule: each format gets a different opening hook.

1. LinkedIn post. ~1300 chars. Hook + body with line breaks every 1-2 sentences + CTA. No hashtags. No emojis unless voice profile uses them.
2. X/Twitter thread. 5 to 7 posts, each under 280 chars. Post 1 = hook. Post 2 = setup. 3-6 = payoff. Final = takeaway or CTA. No "Thread:" or "1/" prefix unless voice uses them.
3. Instagram caption. All lowercase except "I" and proper nouns. 2 to 3 emojis. Casual register. 4 to 8 short paragraphs. Hook opener, soft CTA close.
4. Email newsletter. Subject under 50 chars, curiosity-led. Body 200 to 400 words. Hook paragraph, story or context, lesson, sign-off.
5. Short-form video script. 45 to 75 seconds spoken (110 to 180 words). 4-beat structure: hook, context, reveal, CTA. Plain spoken text only.
6. Blog intro. First 200 words that earn the click-through. Hook with promised payoff. Stakes by word 75. Tease framework by word 150. End on forward-pull line.
7. Pull-quote carousel. 5 to 7 slides, max 20 words per slide. Slide 1 = hook quote. Final = takeaway or CTA. Quotes drawn from pillar, not invented.
8. Cold outreach angle. 60-word DM or email opener. Specific to one persona implied by the pillar. Lead with relevance, end on a low-friction ask.

## Step 5: save outputs

Create ./content-engine-output/<timestamp>/ (timestamp YYYY-MM-DD-HHMM). Write each format to its own file: 01-linkedin.md, 02-twitter-thread.md, 03-instagram.md, 04-email.md, 05-short-form-script.md, 06-blog-intro.md, 07-carousel.md, 08-cold-outreach.md. Also write 00-hooks.md with the candidate hooks and which hook went where.

## Step 6: print to chat

Paste all 8 outputs in chat. Lead with candidate hooks, then the 8 formats. If voice-profile.md was missing, repeat the warning at the bottom.

{
  "mcpServers": {
    "supermemory": {
      "command": "npx",
      "args": [
        "-y",
        "install-mcp",
        "https://mcp.supermemory.ai/mcp",
        "--header",
        "Authorization: Bearer sm_your_key_here"
      ]
    }
  }
}

/plugin marketplace add supermemoryai/claude-supermemory
/plugin install supermemory