Claude Code for Marketing: 7 Skills We Actually Use (2026)
The 7 Claude Code skills that actually work for marketing: a PRD writer, a tasklist generator, an adversarial reviewer, a brand voice auditor, a content repurposer, an SEO researcher, and a competitive analyzer. We built and use all of them daily.
These aren't AI marketing tools in the abstract sense — they're SKILL.md files that load into Claude Code when you invoke a slash command. Each one replaced a specific manual process that was eating hours every week. We run our entire operation — brand audits, content pipelines, community engagement, SEO research — with these seven.
Here's what each one does, what it replaced, and how to install it. Plus the one we built and dropped.
Quick Primer: What Are Claude Code Skills?
If you haven't used skills yet: a skill is a markdown file (SKILL.md) that lives in your project's .claude/skills/ directory. When Claude detects a relevant task — or when you type /skill-name — it loads the instructions and follows them.
Anthropic describes it with a kitchen analogy: MCP servers are the kitchen (tools, ingredients, equipment). Skills are the recipes (step-by-step instructions for using those tools). You need both for complex work, but skills are where domain knowledge lives.
Skills load in three levels:
- Frontmatter (~100 words) — always in context, tells Claude when to trigger
- SKILL.md body (<500 lines) — loaded when triggered, the core instructions
- References (unlimited) — loaded on demand for deep-dive details
This progressive disclosure keeps token usage low while giving Claude specialized expertise when needed.
Category 1: Document & Asset Creation
These skills produce structured outputs — the brand docs, angle briefs, and research artifacts that used to take a full afternoon of manual work.
1. Brand Artifact Generator
What it does: Generates and maintains a research-driven brand artifact library — voice guides, community profiles with coach personas, product state docs, and competitive context files.
What it replaced: Ad-hoc Google Docs with brand voice notes that nobody updated. We'd write a brand voice guide once, forget about it, and six months later Claude would generate content that sounded nothing like the brand because the guide was stale.
How it works: You run /brand-artifact init <brand-slug> and it creates four living documents:
voice-guide.md— tone, anti-patterns, platform-specific adjustmentscommunity-profile.md— per-platform audience profiles with coach personas derived from real forum research (not invented)product-state.md— verified claims, pricing, what's ready vs what's notcompetitive-context.md— positioning relative to competitors
The key insight: coach personas are built from actual Reddit/HN/community research, not imagined. The persona for r/ClaudeAI is different from the persona for LinkedIn because we researched what gets upvoted vs ignored on each platform.
Setup time: ~30 minutes for the first brand. Updates take 5 minutes.
npx skills add derivativelabs/skills --skill brand-artifact
2. Content Angle Selector
What it does: Pre-writing angle selection using a player-coach dialectic. Generates 3 competing angles for any content piece, tests each against the target platform's coach persona, and outputs a structured angle brief.
What it replaced: The "stare at a blank doc and brainstorm" phase. We'd spend 45 minutes picking an angle, start writing, realize halfway through that the angle didn't work for the platform, and restart. Now the angle is locked before a single word of the draft is written.
How it works: You provide a topic, platform, and brand. The skill generates 3 genuinely different angles (not just rewording — different frames like "diagnosis" vs "builder confession" vs "before/after"). Then a coach persona from your community-profile.md evaluates each angle: "Would I upvote this? What triggers my scroll-past reflex?"
The coach can reject all 3 angles. If that happens, the topic itself needs rethinking — not the execution.
The constraint that makes it work: Each angle must commit to an exact first sentence. Not a topic sentence — the actual opening line. This forces specificity before writing begins.
Setup time: 15 minutes (requires brand-artifact to be set up first).
npx skills add derivativelabs/skills --skill content-angle
Category 2: Workflow Automation
These skills run multi-step processes that used to require human judgment at every stage. They don't remove the judgment — they structure it so Claude handles the mechanical parts and flags where human input is actually needed.
3. Text Review Loop
What it does: Three-reviewer analytical loop for any text content. Voice Reviewer checks brand consistency. Authenticity Reviewer simulates the target community's reaction. Facts Reviewer cross-checks every claim against verified sources.
What it replaced: Reading your own draft and hoping it's good enough. The problem with self-review isn't lack of effort — it's that you can't unsee your own intent. You know what you meant, so you read what you meant instead of what you wrote.
How it works: Each reviewer holds a specific lens and produces a PASS/FAIL verdict with blocking issues. The loop runs up to 3 rounds:
- Round 1: All 3 reviewers run
- Round 2: Only failed reviewers re-run after fixes
- Round 3: Full re-run from scratch
If a reviewer still fails after 3 rounds, the piece escalates for human judgment. No infinite loops.
After all 3 pass, an adversarial gate runs — a final check that asks "should this be published at all?" not "is this well-written?"
What surprised us: The Facts Reviewer catches more issues than we expected. Not factual errors — assumption drift. You write "free tier includes X" because it was true 3 months ago, and the reviewer flags that product-state.md now says otherwise.
Setup time: 20 minutes. Requires brand artifacts to exist for the brand being reviewed.
npx skills add derivativelabs/skills --skill text-review-loop
4. Authenticity Reviewer
What it does: Fast single-pass review for community content — Reddit comments, HN replies, Bluesky posts. Runs a 7-point check: value-add, AI tells, specificity credibility, tone match, connection to thread, account risk, and strategic fit.
What it replaced: Posting a Reddit comment that reads like ChatGPT wrote it. AI-generated community content has specific tells: validation-before-pivot openers ("Great question! The thing I'd add..."), three-paragraph essay structure in a comment context, and overly balanced perspectives when a real developer would just have an opinion.
How it works: You feed it a draft comment + the thread context (existing comments, scores, community norms). It issues one of three verdicts:
- POST — all checks pass
- REVISE — 1-2 flags with specific fixes
- CUT — don't post in this thread, find a different one
The check that catches the most: AI Tell Scan. Smooth transitions between unrelated points, hedging language ("might," "could," "tends to" when real devs say "does" or "doesn't"), and compound sentences that are too clean all get flagged.
Setup time: 5 minutes. No dependencies.
npx skills add derivativelabs/skills --skill authenticity-reviewer
5. Pattern Extractor
What it does: Analyzes coding session transcripts to extract error patterns, success patterns, and decision patterns. Scores each by frequency × impact. Suggests code-level improvements and updates a technical-patterns.md file.
What it replaced: Losing institutional knowledge between sessions. Monday's debugging breakthrough is forgotten by Thursday. The same mistake gets made twice because nobody wrote down what went wrong the first time.
How it works: It scans your Claude Code transcripts (the .jsonl files in ~/.claude/projects/), identifies recurring patterns, and categorizes them:
- Error patterns: What broke, how often, and how to prevent it
- Success patterns: What worked, so you can replicate it
- Decision patterns: What choices were made and why — useful for onboarding new team members
The scoring that matters: Frequency alone isn't enough. A rare but catastrophic error (like a silent data corruption) scores higher than a common but trivial one (like a missing import). The frequency × impact formula handles this.
Setup time: 10 minutes. Runs on a schedule (daily cron recommended).
npx skills add derivativelabs/skills --skill pattern-extractor
Category 3: Research & Discovery
These skills give Claude the ability to find and analyze information that would otherwise require switching between 5 browser tabs and a spreadsheet.
6. Brand Audit
What it does: Runs a full 8-layer brand footprint audit — website, GitHub, packages, social profiles, community presence, directories, MCP registries, and SEO. Config-driven, works for devtools, SaaS, creators, or agencies.
What it replaced: A monthly checklist that took 3 hours and still missed things. "Is our GitHub README up to date? Are our npm homepage fields pointing to the right domain? Did someone create a Bluesky account? What's our domain authority this month?"
How it works: You define a brand config JSON with your domains, packages, social handles, and registries. The skill checks each layer and produces a gap analysis: what's live, what's broken, what's missing, and what competitors have that you don't.
What we learned: The audit consistently catches things humans miss — npm packages with homepage fields pointing to old domains (free dofollow backlinks from DA 86 that we were wasting), GitHub repos with no topics set (invisible to discovery), and social accounts created but never posted to.
Setup time: 20 minutes to write the config. 5 minutes to run.
npx skills add derivativelabs/skills --skill brand-audit
7. Transcript Query
What it does: Searches and analyzes AI coding session transcripts across Claude Code, Codex, Cursor, and Gemini CLI. Answers "what were we working on before X?", "why did we choose approach Y?", and "what errors did we encounter with Z?"
What it replaced: The "I know we figured this out last week but I can't find it" problem. Claude Code stores full session transcripts as JSONL files, but nobody searches them. This skill makes them queryable.
How it works: It discovers transcripts from all 4 CLI tools (each stores them differently), normalizes them into a common format, and lets you search by keyword, date range, or topic. You can reconstruct timelines, find context around specific decisions, and identify when files were last modified.
The cross-CLI discovery is the key feature: Claude Code stores transcripts in ~/.claude/projects/, Codex in ~/.codex/sessions/, Cursor in ~/.cursor/projects/, and Gemini CLI in ~/.gemini/tmp/. This skill searches all four with one query.
Setup time: 5 minutes. No external dependencies.
npx skills add derivativelabs/skills --skill transcript-query
The Skill We Dropped: Conversation Monitor
We built a conversation monitor that scanned Reddit, Twitter/X, HN, LinkedIn, Quora, Indie Hackers, and Product Hunt for threads where our brand could participate. It scored threads on relevance, recency, engagement, openness, and safety — then drafted platform-appropriate responses.
It worked technically. The scoring was solid. The drafts were decent.
We dropped it because it encouraged the wrong behavior. Having a tool that automatically finds "opportunities to engage" creates a gravitational pull toward quantity over quality. We found ourselves posting comments because the tool found a thread, not because we had something genuine to add.
The replacement: manual thread discovery during daily browsing, plus the authenticity-reviewer to gate anything before it gets posted. Slower, but every comment is there because a human thought "I actually have something useful to say here."
The skill still exists internally. We might bring it back with stricter filters. But the lesson stands: automating discovery is fine; automating the urge to participate is not.
How to Install All 7
# Install all marketing skills at once
npx skills add derivativelabs/skills
# Or pick specific ones
npx skills add derivativelabs/skills --skill brand-artifact
npx skills add derivativelabs/skills --skill content-angle
npx skills add derivativelabs/skills --skill text-review-loop
npx skills add derivativelabs/skills --skill authenticity-reviewer
npx skills add derivativelabs/skills --skill pattern-extractor
npx skills add derivativelabs/skills --skill brand-audit
npx skills add derivativelabs/skills --skill transcript-query
Skills work across 38+ agents — Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, and more. Same SKILL.md, same slash commands, same workflow.
What We'd Do Differently
If we were starting fresh:
Start with brand-artifact, not a content calendar. The artifacts feed every other skill. Without a voice guide and community profiles, the review loop has nothing to check against.
Set up pattern-extractor on day 1. The patterns it finds in your first week are the ones that save the most time later. We waited a month and lost a month of institutional knowledge.
Don't build skills for problems you don't have yet. We built 77 skills. 7 of them are part of our active workflow. The rest were solutions in search of problems. Start with one pain point, build one skill, use it for a week, then decide if you need another.
Read Anthropic's skill-building guide. The description field in SKILL.md frontmatter is how Claude decides whether to trigger a skill. Anthropic says Claude tends to undertrigger — descriptions need to be specific about when the skill applies. "Use when: brand voice, tone, messaging" triggers more reliably than "Brand management skill."
If you want to start today, install brand-artifact first and spend 30 minutes writing your voice guide and community profiles. That single file will improve every piece of AI-generated content you produce from that point forward — and it's the foundation every other skill in this list depends on.
npx skills add derivativelabs/skills --skill brand-artifact
Built by the team at Derivative Labs — we run 20+ AI agents across our portfolio and these skills are how our marketing agent operates autonomously.
