Skills de Claude Code · página 95

Design a statistically rigorous A/B or multivariate test plan — If/Then/Because hypothesis, control and variant specs, required sample size per variant (absolute vs relative MDE via sample-size-calculator.py), test duration, guardrail metrics, stopping rules, and go/no-go decision criteria. Triggers on \"/digital-marketing-pro:ab-test-plan\", \"set up an A/B test\", \"how long should my test run\", \"calculate sample size for an experiment\", \"is this test result significant\". Reads the brand profile and past campaign-tracker results to avoid re-testing validated hypotheses; finished tests are evaluated with significance-tester.py by the cro-specialist agent.

Generate 3-5 ad copy variations per platform — headlines, descriptions, and CTAs formatted to Google, Meta, LinkedIn, TikTok, X, and Pinterest specs — each scored 1-10 with policy-compliance flags, A/B testing groupings, and a message-match check against the landing page. Triggers on \"/digital-marketing-pro:ad-creative\", \"write ad copy for Meta\", \"give me RSA headline variations\", \"we need LinkedIn ad copy\", \"draft TikTok ad creative\". Reads the brand profile, guidelines, and compliance rules; routes video ad scripts to /digital-marketing-pro:video-script and gates AI-generated visuals for EU campaigns through /digital-marketing-pro:c2pa-metadata and /digital-marketing-pro:check.

Walk through adding a custom MCP server integration to the plugin — searches npm for an existing MCP package (or scaffolds a custom server from the plugin's guide), generates the exact .mcp.json entry, sets up environment-variable credentials, tests connectivity, and documents the tools the new server exposes. Triggers on \"/digital-marketing-pro:add-integration\", \"connect Ahrefs to the plugin\", \"add a new MCP server\", \"integrate our internal API\", \"hook up Stripe data\". Reads the brand profile and agency credential profiles at ~/.claude-marketing/credentials/ to map client-specific keys; custom builds follow skills/context-engine/custom-mcp-guide.md.

Audit how a brand appears across the 6 canonical AI answer surfaces — ChatGPT, Perplexity, Google AI Mode, AI Overviews, Gemini, Copilot — probing 10-25 queries into a numbered output bundle with per-platform visibility scorecards, citation-accuracy checks, a competitor matrix, content gaps, and an optimization playbook behind a four-gate quality scorecard. Triggers on \"/digital-marketing-pro:aeo-audit\", \"does ChatGPT know about our brand\", \"check our AI search visibility\", \"how does Perplexity describe us\", \"are we showing up in AI Overviews\". Reads the brand profile; reconciles probes against GSC actuals via /digital-marketing-pro:gsc-ai-performance and defines the AI-visibility scoring standard reused by geo-monitor and share-of-voice.

Strategy module for Answer Engine / Generative Engine Optimization — audits AI visibility, restructures content for citation, runs entity-consistency checks across Knowledge Graph, Wikidata, Wikipedia, Crunchbase, and LinkedIn, and produces JSON-LD schema specs, monitoring frameworks, and a 90-day LLM content strategy. Triggers on \"/digital-marketing-pro:aeo-geo\", \"how do we get cited by AI\", \"optimize for AI Overviews\", \"fix our entity consistency\", \"do we need llms.txt\". Reads the brand profile, compliance rules, and industry benchmarks; its measurement counterpart is /digital-marketing-pro:aeo-audit, with GSC actuals via /digital-marketing-pro:gsc-ai-performance.

Generate a portfolio-level dashboard across ALL client brands — per-client RAG health scores, campaign activity, budget pacing, aggregate KPIs, team utilization, pending approvals, upcoming deadlines, and an alerts panel — built for agency standups and weekly reviews. Triggers on \"/digital-marketing-pro:agency-dashboard\", \"how are all our clients doing\", \"portfolio health check\", \"budget pacing across accounts\", \"which accounts are at risk\". Enumerates every brand under ~/.claude-marketing/brands/ and pulls data via campaign-tracker.py, execution-tracker.py, and team-manager.py; drill into a single client with /digital-marketing-pro:performance-report or /digital-marketing-pro:client-report.

Marketing measurement module — builds KPI trees per business model, reporting templates (weekly, monthly, QBR, campaign), anomaly root-cause diagnosis, MMM and incrementality guidance, dark-social tracking, and privacy-first cookieless measurement architecture, including the GA4 AI Assistant channel group for attributing AI-referred traffic. Triggers on \"/digital-marketing-pro:analytics-insights\", \"why did traffic drop\", \"define our KPIs\", \"design an executive dashboard\", \"can we do marketing mix modeling\". Reads the brand profile, industry benchmarks, and campaign history; pairs with /digital-marketing-pro:gsc-ai-performance and /digital-marketing-pro:aeo-audit to triangulate AI-surface impressions against actual traffic.

Scan all connected marketing platforms for statistically significant deviations from stored baselines — traffic drops, CPA spikes, deliverability collapse, budget overruns, or unexpected wins — classified critical/warning/info with probable causes, correlation to recent changes, and recommended actions. Triggers on \"/digital-marketing-pro:anomaly-scan\", \"why did our CPA spike\", \"did anything weird happen this week\", \"check for anomalies\", \"our conversions suddenly dropped\". Runs performance-monitor.py for baselines and detection, correlates flags against execution-tracker.py history and the diagnostic framework in skills/analytics-insights/anomaly-diagnosis.md, and persists critical findings as insights via campaign-tracker.py. Reads the brand profile.

Design a multi-touch attribution strategy — recommends the best-fit model for the business's sales cycle and data maturity, defines credit-distribution rules and lookback windows, maps platform-specific setup (GA4, HubSpot, Salesforce, warehouse), and documents tracking gaps and known blind spots. Triggers on \"/digital-marketing-pro:attribution-model\", \"set up multi-touch attribution\", \"which attribution model should we use\", \"configure GA4 attribution\", \"how should we credit channels for conversions\". Reads the brand profile and consumes the canonical model taxonomy in skills/funnel-architect/attribution-models.md; to run the models against real conversion data, pair with /digital-marketing-pro:attribution-report.

Run multi-touch attribution analysis on real conversion-path data — applies two or more models side-by-side (first-touch, last-touch, linear, time-decay, position-based, data-driven), computes per-channel attributed revenue and ROAS, assisted-conversion ratios, path-length and time-to-conversion distributions, and budget reallocation recommendations. Triggers on \"/digital-marketing-pro:attribution-report\", \"which channels actually drive revenue\", \"compare first-touch vs last-touch\", \"run an attribution analysis\", \"is paid social undervalued\". Pulls journeys from Google Analytics, Google Ads, Meta, and CRM MCPs and includes GA4's AI Assistant channel; model definitions come from skills/funnel-architect/attribution-models.md, strategy design from /digital-marketing-pro:attribution-model.

Audience research module — builds six-dimension buyer personas (demographic, psychographic, behavioral, need-state, information, decision), Jobs-to-Be-Done maps, RFM/behavioral/lifecycle segmentation models, anti-personas with exclusion criteria, B2B buying-committee maps, and lookalike seed specs. Triggers on \"/digital-marketing-pro:audience-intelligence\", \"who are our customers\", \"build buyer personas\", \"segment our audience\", \"run a JTBD analysis\". Reads the brand profile, industry benchmarks, and campaign history, and works from CRM/survey/analytics data when supplied — or labels hypothesis personas explicitly when data is thin. For a single quick persona document, /digital-marketing-pro:audience-profile is the lighter sibling.

Build a named, narrative buyer persona document — demographic snapshot, psychographic drivers, jobs-to-be-done, day-in-the-life scenario, buyer journey map, objections with counter-messaging, and content/channel preferences — for the 2-4 personas a brand actually needs. Triggers on \"/digital-marketing-pro:audience-profile\", \"create a buyer persona\", \"profile our target customer\", \"who is our ideal customer\", \"map the buyer journey for this segment\". Reads the brand profile, guidelines, and any customer data supplied (surveys, CRM exports, analytics demographics); run by the marketing-strategist agent. For the deeper research module — segmentation, anti-personas, buying committees — see /digital-marketing-pro:audience-intelligence.

Campaign autopilot operations dashboard — 0-100 health scores for all active campaigns, a chronological log of auto-corrections taken (bid, budget, audience, creative, pause) with before/after metrics, the current guardrail rule table, campaigns escalated for human attention ranked by urgency, and estimated savings from automated interventions. Triggers on \"/digital-marketing-pro:autopilot-status\", \"how is autopilot doing\", \"what did the autopilot change\", \"which campaigns need my attention\", \"show guardrail settings\". Runs campaign-health-monitor.py for health scores, corrections history, and the savings report; reads the brand profile for KPI targets, naming conventions, and budget constraints.

Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \"/digital-marketing-pro:budget-optimizer\", \"optimize my marketing budget\", \"which channels should get more spend\", \"reallocate budget based on ROAS\", \"is our channel split right\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing.

Track advertising spend pacing in real time across connected ad platforms (Google Ads, Meta, LinkedIn, TikTok) — produces a budget dashboard with daily burn rates, end-of-period projections, overspend/underspend alerts, and dollar-specific reallocation recommendations backed by CPA/ROAS context. Monitors and recommends only; it never edits platform budgets. Triggers on \"/digital-marketing-pro:budget-tracker\", \"are we overspending this month\", \"how is our ad budget pacing\", \"track spend across platforms\", \"will we blow through the budget cap\". Reads budget targets from the brand profile, runs scripts/ad-budget-pacer.py, and saves snapshots for trend history; pairs with /digital-marketing-pro:budget-optimizer.

Embed a C2PA provenance manifest into an AI-generated marketing asset (PNG, JPG, WebP, GIF, TIFF, MP4, MOV, WebM, MP3, WAV, PDF) via scripts/embed-c2pa.py — produces a signed copy of the file carrying IPTC digital-source-type AI claims, an optional c2pa.ai-disclosure assertion for EU AI Act Article 50 (applicable 2 Aug 2026), and a JSON status report. Triggers on \"/digital-marketing-pro:c2pa-metadata\", \"sign this AI image for EU compliance\", \"add content credentials to this asset\", \"embed provenance metadata\", \"mark this video as AI-generated\". Uses a self-signed dev certificate unless --signing-cert/--signing-key are supplied; pairs with /digital-marketing-pro:check, which verifies manifests pre-publish.

Inventory and score everything currently running for a brand across paid search, paid social, email, organic, SEO, AEO/GEO, CRM, and analytics — produces a dated audit document with a 4-tier triage (healthy / quick win / strategic gap / red flag), a quick-wins backlog, and a compliance posture section. Strictly read-only: it never pauses, edits, or launches anything. Triggers on \"/digital-marketing-pro:campaign-audit\", \"what's currently running for this brand\", \"audit our existing campaigns\", \"we just inherited this account\", \"where is budget leaking\". Requires a validated brand profile (run validate-profile first); missing connectors degrade gracefully into findings. Feeds /digital-marketing-pro:campaign-plan and pairs with /digital-marketing-pro:performance-check.

Full campaign-lifecycle module — produces campaign briefs, budget allocations via three models (70/20/10, efficiency-ranked, funnel-weighted), channel-mix and media plans, UTM taxonomies with governance rules, launch checklists, ABM plans, and post-mortem reports. Plans and documents; it does not launch or edit live campaigns. Triggers on \"/digital-marketing-pro:campaign-orchestrator\", \"build a media plan\", \"how should we split budget across channels\", \"set up UTM naming conventions\", \"run a post-mortem on the campaign\". Reads the brand profile, guidelines, and campaign history via campaign-tracker.py; its reference docs are consumed by /digital-marketing-pro:campaign-plan rather than duplicated.

Unified status dashboard for every tracked campaign across connected platforms — produces a summary table with health indicators, live spend and performance metrics, a 7-day execution history, pending approvals with age, KPI variance classification (on track / at risk / behind), flagged issues, and next scheduled actions. Reports only; it changes nothing on any platform. Triggers on \"/digital-marketing-pro:campaign-status\", \"what campaigns are running right now\", \"any failed executions or stuck approvals\", \"status of the Q1-Launch campaign\", \"which campaigns are behind target\". Reads the brand's campaign registry, execution log, and approval queue via campaign-tracker.py, execution-tracker.py, and approval-manager.py, plus live metrics from connected platform MCPs.

Build a complete case-study creation blueprint — a Challenge-Solution-Results narrative framework, 15-20 client interview questions plus 10 internal-team questions, a data-visualization plan, format specifications (PDF, web page, slide deck, video script outline, social snippets, sales one-pager), a distribution strategy, a permission/approval checklist, and a draft executive summary. Plans the case study; it does not produce the finished designed asset. Triggers on \"/digital-marketing-pro:case-study-plan\", \"turn this client win into a case study\", \"what should we ask the client in the interview\", \"plan a success story for sales enablement\", \"case study formats and distribution plan\". Reads the brand profile, guidelines, custom templates, and agency SOPs.

Score customer segments for churn risk from behavioral signals — email engagement decline, purchase recency, usage drops, support sentiment — producing a 0-100 risk scorecard with four tiers, per-tier intervention playbooks (actions, timing windows, channels, messaging), LTV-at-risk totals, and retention-ROI prioritization. Assesses and recommends; it does not send outreach or launch campaigns. Triggers on \"/digital-marketing-pro:churn-risk\", \"which customers are about to churn\", \"score our segments for churn risk\", \"email engagement is dropping, who is at risk\", \"build a retention intervention plan\". Pulls behavioral data from a connected CRM MCP (Salesforce or HubSpot) or user-provided exports, runs scripts/churn-predictor.py, and reads the brand profile for lifecycle context.

Generate a complete onboarding package for a new marketing client — kickoff meeting agenda, 20-30 question discovery questionnaire, stakeholder map with RACI matrix, platform-by-platform access checklist, 30-60-90 day milestone plan, communication cadence, escalation protocol, welcome email template, internal team brief, risk register, and a day-by-day first-week action plan. Triggers on \"/digital-marketing-pro:client-onboarding\", \"we just signed a new client\", \"build a kickoff agenda and discovery questionnaire\", \"30-60-90 day plan for the new account\", \"what access do we need from the client\". Reads the brand profile, guidelines, custom templates, and agency SOPs so the package matches house process.

Draft a professional agency proposal or pitch document for a prospective client — executive summary, situation analysis, scope of services with a deliverables matrix, KPI targets with baselines and stretch goals, 2-3 pricing options, team bio structure, case-study placeholders, terms outline, and next steps, written from the agency's perspective. Triggers on \"/digital-marketing-pro:client-proposal\", \"write a proposal for this prospect\", \"scope of work for a 6-month retainer\", \"respond to this RFP\", \"pitch deck outline with pricing tiers\". Loads the agency's own brand profile (not the client's), plus custom templates and agency SOPs; industry benchmarks inform the KPI and pricing sections.

Generate a white-labeled client report in agency voice — weekly pulse, monthly review, or QBR — with a KPI scorecard vs targets and comparison period, channel breakdowns, top wins with attribution, root-cause analysis of misses, 3-5 strategic recommendations, and budget efficiency. Requires explicit approval before any external send; only then can it deliver via connected Slack, email, or Google Sheets MCPs and log the delivery. Triggers on \"/digital-marketing-pro:client-report\", \"prepare the monthly report for the client\", \"build the QBR for this account\", \"send the weekly performance pulse\", \"white-labeled performance report\". Reads the brand profile and pulls data via campaign-tracker.py, execution-tracker.py, and connected platform MCPs; formats via report-generator.py.

Produce the Part 5 Client Validation Document — the one true stop of the 12-Part engagement where unbiased v1 findings from Parts 2-4 are compiled into 12-25 evidence-cited finding blocks, each awaiting an ACCEPT / REJECT / EDIT / DEFER client decision, plus a paired JSON response template. Recorded responses feed the Part 6 Decision Matrix (engagement-state.py) to determine v2 re-runs. Triggers on \"/digital-marketing-pro:client-validation-document\", \"prepare v1 findings for client review\", \"run part 5 client validation\", \"the one true stop\", \"record the client's validation responses\". Requires Parts 3-4 marked completed in _engagement.json; reads the eight v1 core documents; pairs with /digital-marketing-pro:engagement-workflow and /digital-marketing-pro:four-core-documents.

Segment customers into cohorts — by acquisition period, channel, first behavior, or revenue tier — and produce a color-coded retention matrix, overlaid retention curves, LTV-by-cohort comparisons with LTV:CAC where cost data exists, best/worst cohort rankings with hypothesized drivers, stabilization-point analysis, and intervention recommendations for underperformers. Analyzes and recommends; it launches nothing. Triggers on \"/digital-marketing-pro:cohort-analysis\", \"are newer customers retaining better than older ones\", \"which channel produces the highest-LTV customers\", \"build a retention matrix\", \"when does our churn stabilize\". Pulls customer data from connected CRM and analytics MCPs, reads the brand profile for business-model context, and saves the summary as a campaign-tracker.py insight for trend tracking.

Configure a tiered competitor alert system — per-competitor trigger thresholds across content, pricing, ads, social, ranking, and SERP changes, with critical/warning/info urgency routing, digest batching, and alert-fatigue guardrails — then send test alerts to verify Slack or email delivery. Triggers on \"/digital-marketing-pro:competitor-alerts\", \"alert me when a competitor changes pricing\", \"set up competitor alerts\", \"too many competitor notifications\", \"tell me when competitors launch new ads\". Requires baselines from /digital-marketing-pro:competitor-monitor and persists config via competitor-tracker.py; reads the brand profile for competitive context.

Set up ongoing competitor monitoring — captures per-competitor baselines across content, pricing, ads, social, SEO, and SERP features, saves them via competitor-tracker.py, configures per-dimension scan schedules and alert rules, and produces an initial competitive intelligence brief with threat rankings. Triggers on \"/digital-marketing-pro:competitor-monitor\", \"track our competitors over time\", \"watch competitor pricing pages\", \"set up competitor tracking\", \"detect when competitors change their site\". Baselines power /digital-marketing-pro:competitor-alerts and share-of-voice trends. Reads the brand profile for competitive landscape context.

Build competitor comparison and alternatives pages — X-vs-Y head-to-heads, alternatives-to-X lists, category roundups, and feature-matrix pages — delivering a 1,500+ word page template, comparison table, JSON-LD schema via schema-generator.py, keyword and title-tag targeting, an internal-linking plan, and fairness/accuracy guardrails. Triggers on \"/digital-marketing-pro:competitor-pages\", \"create a vs page\", \"build an alternatives page\", \"comparison page for our product\", \"best tools roundup for our category\". Produces page content and markup, not a live publish. Reads the brand profile and guidelines, especially restrictions on competitor mentions.

Guide the connection of a known MCP integration to the plugin — looks up the connector registry via connector-status.py, checks current status, and returns transport-specific setup steps (OAuth for HTTP connectors, env-var credentials plus the exact .mcp.json block for npx connectors), verification steps, and the skills each connector unlocks. Triggers on \"/digital-marketing-pro:connect\", \"connect Google Ads\", \"hook up Slack to the plugin\", \"set up the HubSpot integration\", \"how do I add Mailchimp\". Guidance and status checks only — the user adds credentials themselves; unknown or custom servers route to /digital-marketing-pro:add-integration.

Create a production-ready content brief a writer can execute without extra context — keyword map (primary, secondary, related questions), H2/H3 outline with key points and word-count targets, brand voice guidance, on-page SEO checklist, visual/media spec with AI-generation and C2PA provenance notes, and success metrics. Triggers on \"/digital-marketing-pro:content-brief\", \"write a brief for this topic\", \"brief a blog post on X\", \"what should this article cover\", \"outline and SEO requirements for a pillar page\". Preferred upstream of /digital-marketing-pro:content-engine. Reads the brand profile, guidelines, and compliance rules.

Generate a structured content calendar for a month, quarter, or custom range — topics mapped to platforms and publish dates, content-pillar and funnel-stage tags, repurposing chains from each core piece, SEO keyword targets, and owner assignments when team capacity is given. Triggers on \"/digital-marketing-pro:content-calendar\", \"plan next month's content\", \"build a quarterly editorial calendar\", \"what should we publish in March\", \"map our content pillars to a schedule\". Planning output only — it does not schedule or publish posts. Reads the brand profile, guidelines, and compliance rules for pillars and voice.

Scan the content library for decay — declining traffic, falling keyword positions, stale content, broken links, lost AI citations — scoring each URL 0-100 via creative-fatigue-predictor.py and ranking refreshes by recoverable revenue, with actionable refresh briefs and traffic-recovery estimates for top-priority tiers. Triggers on \"/digital-marketing-pro:content-decay-scan\", \"which content is losing traffic\", \"find stale content to refresh\", \"our blog traffic keeps dropping\", \"prioritize content refreshes\". Pulls performance data from Google Analytics/Search Console MCPs or exported CSVs; cross-reference causes with /digital-marketing-pro:seo-drift. Reads the brand profile for content strategy context.

Implement a Linear ticket end-to-end - fetch context, branch, code, tests, draft PR, update the ticket. Use when given a ticket ID like EVL-86 or DEP-123.

Generate the "How to test" section for the current branch's changes - exact commands, flows to exercise, expected results. Use before opening/updating a PR or when asked how to validate a change.

Use when the user wants a less default, more original solution for code, design, writing, naming, architecture, debugging, or strategy, especially when progress is stuck.

Use when the user wants a distinctive, production-ready frontend interface with clear visual direction, strong hierarchy, and polished interaction details.

When you want to pressure-test a potential new business, product, or side project against the serial-founder filter. Not \"marketing ideas for a product\" (that's marketing-skills:marketing-ideas) — this is \"should this business exist + can you win it.\" Runs the idea through a structured framework (problem, audience, wedge, monetization, moat, portfolio fit, distribution, energy fit, opportunity cost), checks domain availability via /domain, optionally triggers /deep-research for market validation, and outputs a viability brief: build / sleep on it / pass. Archives every idea to ~/.config/makerskills/business-brainstorm/archive/ so past work is searchable. Triggers on \"/business-brainstorm,\" \"/brainstorm,\" \"new business idea,\" \"should I build X,\" \"pressure test this idea,\" \"validate this idea,\" \"is X a good business,\" \"what about a [type] for [audience].\"

Your team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so Claude can answer questions on your team's behalf. Team-scope sibling to second-brain (which is personal-scope). Seven modes — capture (drop something into the right structured dir), compile (process into wiki pages, update INDEX.md), query (answer from the corpus with trust weighting, save to outputs/), review (triage queue — verify / deprecate / supersede unreviewed and stale captures so wrong info never becomes context), lint (orphans / stale / contradictions / gaps), connect (suggest new wikilinks), search (quick lookup). Structured raw dirs (people/, companies/, meetings/, sops/, decisions/, customer-language/, recurring-questions/, sales-objections/) instead of second-brain's flat type-prefixed raw/. Multi-author aware — every capture stamps author + timestamp + trust status. Optional auto-sync from Fathom/Gong/Granola call transcripts, Slack/email exports, CRM. Defaults to a vault at ${COMPANY_BRAIN_VAULT:-$HOME/Documents/CompanyBrain}/. Triggers on "/company-brain," "/cb," "capture this into the team brain," "log this meeting," "add this person to the team brain," "save this SOP," "compile the company wiki," "query the team brain," "what does the team know about X," "review the company brain," "cull the team brain," "lint the company brain," "who's the internal expert on X.

Monthly CFO workflow for a company or agency — pull raw data from bank + payment processor + payroll + expense management, categorize and reconcile, compute end-of-month cash via transaction-sum method, update a scenario projector for forward forecasting, write the monthly snapshot report, surface decisions to leadership. Modes — monthly (default; the standing report), weekly (thin cash pulse), scenario (ad-hoc modeling in the projector), pickup (resume where the prior run left off). Anonymized team-scope sibling to personal-cfo (which handles personal household finances). Composes with company-brain (report gets stored + wiki-indexed there), toolify (wire company-specific data sources), loopify (schedule the monthly + weekly runs). Triggers on "/company-cfo," "/cfo," "monthly cash report," "do the CFO snapshot," "CFO monthly," "let's run CFO," "cash projection," "runway forecast," "monthly financials," "cash pulse.

When you have a decision to make and want a structured workflow that picks the load-bearing questions, walks through them, reaches a call (or "wait"), and archives the rationale for future reference. Based on the 37signals Guide to Making Decisions (38 questions) plus house additions like Q39 opportunity cost ("what does saying yes displace?"). Triages to 6–8 relevant questions per decision instead of forcing the full set. Archives every decision to ~/.config/makerskills/decide/archive/ with a revisit date so you can check later whether the call was right. Triggers on "/decide," "help me decide," "should I [X]," "I need to make a decision about," "stuck on a decision," "deciding between," "go/no-go on," "what should I do about." This is both the decision-making workflow AND the decision log — making the decision is the act of logging it.

When you want multi-source, multi-step research on a topic — competitor research before a sales call, market research for a new business idea, positioning angles, due diligence on a partnership or podcast guest, tech decision research (which DB, which auth), or any \"I need to actually understand X.\" Combines WebSearch, WebFetch, agent-browser, /last30days (Reddit/X/YouTube/HN/web recency), memory, and Notion. Outputs a structured brief with citations, contradictions, gaps, and recommended next steps. Archives every research run to ~/.config/makerskills/deep-research/archive/ so past work is searchable. Triggers on \"/deep-research,\" \"research X,\" \"investigate X,\" \"do a deep dive on X,\" \"look into X,\" \"what's actually happening with X,\" \"due diligence on X,\" \"validate this market.\" Differs from a one-shot WebSearch: this is multi-pass with verification.

When you want to brainstorm and check available .com domains for a new project — brand naming, aftermarket pricing (HugeDomains / Afternic / Sedo / Dan), USPTO trademark screening, and social handle availability. Built on Laura Roeder's \"work backwards from availability, not from a name you fell in love with\" methodology. Uses Vercel CLI + whois + Domainr API + Namecheap API + agent-browser for the pieces each tool actually reliably supports (multi-tool ensemble because no single tool covers everything cleanly). 11-step workflow: budget → brainstorm → primary availability check → whois cross-check → Domainr aggregation → Namecheap price → aftermarket sweep (+ liveness probe for parked/dead domains, drop-watch for expiring ones) → bucket → negotiate → NAME research (trademark + socials) → buy. Triggers on \"/domain,\" \"find a domain,\" \"check domain availability,\" \"brainstorm a domain,\" \"what .com is available for X,\" \"domain hunt,\" \"name my project,\" \"is X.com available,\" \"aftermarket price on X.com,\" \"trademark check for X.\"

Gary Vaynerchuk's jab-jab-jab-right-hook framework applied to a personal portfolio rotation on X and LinkedIn. Jabs = build-in-public + educational (value). Hooks = promo (the ask). Each property in the user's configured portfolio (see `~/.config/makerskills/jab-hook/properties.yaml`) gets a hook at least once every ~3 weeks; jabs fill the rest. Drafts go into the user's Typefully workspace via MCP. Modes — plan (7-day plan), pick-next (single post), audit (coverage report), draft (specific post). Triggers on "/jab-hook," "what should I post," "plan my socials," "next promo," "next jab," "next hook," "social rotation," "promote [property]," "BIP post," "audit my socials," "what haven't I posted about.

When you want to set up an agent loop, cron-scheduled task, or recurring workflow that runs autonomously in Claude Code. Judgment layer on top of ScheduleWakeup, CronCreate, and the /loop skill — decides whether to use dynamic pacing (self-scheduling wake-ups), cron scheduling (fixed intervals), or a one-shot loop; tunes delay to avoid the 5-minute cache-miss cliff; designs idempotent loop bodies; sets bail-out conditions so loops don't run forever. Examples of loops to loopify — weekly review pulse, daily brief generation, hourly monitoring of a metric, periodic vault compilation, upstream-check for an adapted skill, sponsorship-pipeline refresh, YouTube-transcript-batch-download, morning startup routine. Triggers on "/loopify," "set up a loop," "schedule this task," "run this daily," "run this weekly," "cron this," "make this recurring," "automate this on a schedule," "keep this running until X." Part of the -ify trifecta (skillify / toolify / loopify) for extending Claude Code. NOT for authoring a new skill — that's skillify. NOT for adding a tool/integration — that's toolify.

When you want multiple expert perspectives on a founder/operator question — a simulated personal board of advisors staffed by legendary founders, CEOs, and operators (Jason Fried, Elon Musk, Jeff Bezos, Jensen Huang, Bob Iger, Paul Graham, Naval Ravikant, Sara Blakely). Bring a real decision — \"should I raise prices?\", \"hire my first employee?\", \"raise or bootstrap?\", \"kill this project?\" — and the council weighs in through their documented frameworks, surfaces where they disagree, and synthesizes a recommendation. Also use when the user mentions 'maker council,' 'board of advisors,' 'what would Bezos do,' 'what would Jason Fried say,' 'channel Naval,' 'ask the council,' 'get multiple perspectives on this decision,' or asks how a famous founder would approach their problem. Optional live-research pass grounds takes in what each member has actually said (via deep-research / watch-video / last30days). Sibling of marketing-skills' marketing-council (marketing questions go there; company-building and operator questions come here). Archives sessions to ~/.config/makerskills/maker-council/archive/. For committing to one of the surfaced directions, hand off to decide.

When you want to clean and reformat content (usually from your terminal) for pasting into Slack, Notion, Twitter/X, LinkedIn, email, GitHub, or plain text. Strips ANSI codes, box-drawing chars, terminal prompt artifacts, and applies destination-specific formatting. Default input is the clipboard (read via `pbpaste`); default output is both the clipboard (`pbcopy`) and a chat preview. Triggers on "/paste", "/paste [destination]", "clean this for X," "format for slack/notion/twitter/linkedin/email/github," "render as markdown," "paste-ready," "strip formatting," "make this copy-pastable." Default destination is plain. Also scans for secrets (API keys, tokens, .env values) and warns before copying anything sensitive.

When you want to model personal financial scenarios — house purchase + rental income (ADU, bedroom rentals, house-hacking), renovation budgets, monthly cash flow forecasts, big-purchase decisions, savings/investment what-ifs. For personal life: a household (you + partner), household budgets, real-estate decisions. v0.1 ships with the house scenario template (purchase + rental scenarios) as the first use case. Architected so other personal-finance scenarios (refi, car, education, retirement, side income) slot in as additional templates. Outputs scenario comparison tables in markdown. Saves every scenario to ~/Documents/personal-cfo/ with an index at ~/.config/makerskills/personal-cfo/archive/ for revisit + comparison. Composes with decide (formalize the call after modeling), deep-research (rental comps, mortgage rates, market data), business-brainstorm (when the scenario is a small business / side hustle), second-brain (capture the analysis to outputs/). Triggers on \"/personal-cfo,\" \"model this scenario,\" \"house math,\" \"rental forecast,\" \"monthly cash flow,\" \"what if I rent out the ADU,\" \"compare these housing scenarios,\" \"should we buy this house,\" \"house-hack math,\" \"renovation budget.\"

When you want to manage projects across your businesses using a kanban + Eisenhower methodology. One kanban per business (whatever portfolio of businesses, projects, or initiatives you run). Tool-agnostic — connects via API/MCP to whatever PM tool each business uses (Notion, GitHub Projects, Plane, Linear, Obsidian file-based, or manual mode). Async-first output. Six modes — setup (scaffold a new board for a business), triage (Eisenhower-sort the backlog), next (pick the next thing to work on, single board or across all), status (paste-ready async snapshot), unblock (diagnose Review/Blocked column), weekly (Friday pulse + week planning). Triggers on "/pm," "/pm setup," "/pm triage," "/pm next," "/pm status," "/pm unblock," "/pm weekly," "what should I work on next," "kanban status," "Eisenhower this," "triage my backlog," "what's blocked.

When you want to read and extract structured notes from a book — PDF, EPUB, MOBI, markdown, .txt, pasted text, or URL to a public-domain work. Reads in chunks (by chapter when a TOC exists, by 50-page blocks otherwise), extracts per-chapter TL;DR + key concepts + quotes + action items + frameworks, and offers to capture to second-brain raw/ as a highlights- file. Four modes — notes (default, chapter-by-chapter), summary (whole-book TL;DR + 3–5 takeaways), quotes (pull-quote highlights only), study (notes + Q&A spaced-rep prep). Triggers on "/read-book," "read this book," "extract notes from this PDF," "what's in this book," "summarize this ebook," "pull quotes from this." Sibling to watch-video (same content-consumption pattern, different medium).

When you want to capture into, compile, query, lint, or connect your personal Second Brain. Wraps the Karpathy LLM Wiki schema (Obsidian or any markdown vault) — raw/ (unprocessed sources), wiki/ (AI-compiled interlinked topic pages), outputs/ (generated artifacts). Tool-agnostic in design but defaults to a vault at ${SECOND_BRAIN_VAULT:-$HOME/Documents/SecondBrain}/. Six modes — capture (drop something into raw/), compile (process unprocessed raw files into wiki pages, update INDEX.md), query (answer a question from the wiki, save to outputs/), lint (orphans / contradictions / stale / unprocessed raw / topic gaps), connect (suggest new wikilinks between pages), search (quick lookup). Triggers on "/second-brain," "/sb," "capture this," "save this to my brain," "compile the wiki," "process raw notes," "query my wiki," "ask my brain," "lint the wiki," "find connections," "search my notes." Complements deep-research (external corpus) — this is the internal corpus.