Digital Marketing Pro

Your agency just signed a 50-brand client. The previous agency left no playbook. Three brands are bleeding budget, two have stale positioning, one is launching in a regulated jurisdiction next month. Where do you start?

Run /digital-marketing-pro:engagement against each brand. Same 12-Part Strategy Flow, same Four Core Documents, same 61-step structure — auditable across the entire portfolio in ~60 minutes per brand on Claude Opus-class models (measured on Opus 4.8; Opus 5 is the current equivalent at the same price). No more inconsistent depth between brands. No more "what did the last agency do?" mysteries. No more compliance gaps in regulated jurisdictions.

Open-source AI marketing plugin — 163 skills, 24 specialist agents, EU AI Act Article 50 ready, Cowork team-persistent. Built for marketing agencies, in-house teams running 50–200 brands, and consultancies. Installs on Claude Code (CLI + IDE), Anthropic Cowork, OpenAI Codex, Cursor 2.5+, GitHub Copilot CLI, Google Antigravity 2.0, Hermes Agent, and OpenClaw + 35+ Agent Skills platforms. Created by Indranil Banerjee · LinkedIn · X.

Version License Stars Forks Issues Last commit Tests Platforms Cowork EU AI Act Sponsor

🆕 Just shipped — v3.31.0 (August 17, 2026): Grok (xAI Build CLI) becomes the ninth native platform. A first-class .grok-plugin/ manifest pair — plugin.json with the "skills" pointer Grok's loader reads, plus a single-plugin marketplace.json — makes grok plugin install indranilbanerjee/digital-marketing-pro work directly (Grok Build also reads the Claude Code manifests for compatibility; the native pair is what an official xAI marketplace listing points at). Both files are version-locked into the release-consistency suite. The same pass also caught and fixed four stale counts that had escaped the doc-count guard through new phrasings — "158 `SKILL.md` files" hidden by backticks, "158 marketing skills" and "158 DMP skill names" hidden by qualifier words, and an "All 209 tests" claim that was 170 stale — and taught the guard each phrasing, plant-checked, with "N tests" now a derived-truth noun. 163 skills, 381 tests. Previously — v3.30.2 (August 16, 2026): the documentation truth pass. A from-zero audit found the doc-count guard pattern-blind: the comparison table said "Skills count 158" against 163 shipped, five documents quoted "86 Python scripts" against 93, and AGENTS.md — the file every non-Claude runtime auto-loads — pinned v3.17.0, thirteen releases stale. Every number is now re-derived from the filesystem and the guard grew the exact patterns that escaped it (script counts, SKILL.md-file counts, table rows, AGENTS.md currency), each plant-checked against the phrasing it previously missed. 163 skills, 379 tests. Previously — v3.30.1 (August 16, 2026): richer Agent Plugins listing metadata + the directory submission bundle (docs/distribution/). And — v3.30.0: the content-engine run auditor — “status: ready” is now re-derived, never trusted. New scripts/run-audit.py re-checks a finished run from its artifacts: every numbered artifact present, the humanize verdict re-measured with a fresh ai-tell-scan.py run instead of read off the scorecard, no scan JSON embedded in the file authorship.py measures (the corruption class that once flipped may_claim_authored and denied an author credit for work they did), the authorship record matching a fresh measurement, recorded voice distances actually inside the 0.15 gate, and publish-ready copy free of production placeholders. A scorecard declaring ready past its own recorded gate is a FAIL with the number quoted; a missing input is reported-N/A, never silent-pass. The content-engine contract now runs the audit before status: ready may be declared, and the verdict lands in run-audit.json beside the artifacts so the next reader sees the run was verified rather than believed. 163 skills, 376 tests. Previously — Just shipped — v3.29.0 (August 16, 2026): Digital Marketing Pro travels in Agent Plugins 1.0. OpenAI's vendor-neutral plugin standard (announced Aug 6; adopted by ChatGPT, Codex, Cursor, GitHub Copilot, VS Code, Kiro) reads a root plugin.json on a closed schema and defines ${PLUGIN_DATA} as the persistent-data name — and a compliant non-Claude host previously resolved no data directory here at all, because every resolver read only the CLAUDE_* spellings. Shipped: the root manifest (version-synced with the Claude manifest and guarded by tests — closed-schema check, name rules, all 163 skills verified in the standard's layout), and ${PLUGIN_DATA} accepted as the fallback wherever CLAUDE_PLUGIN_DATA was read. One listing in the shared ChatGPT + Codex directory is now a packaging step away rather than a port. 163 skills, 364 tests. Previously — Just shipped — v3.28.0 (August 15, 2026): a brand-setup → content-engine run, following the instructions literally, found five defects no unit test could see. brand_voice_match was unfailable: it asked for "≤ 1.5 point deviation" while the scorer emits distance bounded at 1.0 — a hollow gate that had been passing everything. Now stated in the scorer's own 0–1 unit at distance ≤ 0.15, the threshold the scorer already used internally, with a test that fails if the two diverge. seo_complete held one impossible criterion and one vacuous one: a pre-launch brand's first article cannot make 3 internal links, and "all images have alt text" passed at zero images — both now take an N/A that must name its reason, because a bare N/A is a FAIL. brand-setup produced a profile its own validate-profile rejected on BLOCKER items it never creates; the generator is the source of truth, so the validator was the outlier and now accepts its keys. The voice remediation inverted its own diagnosis — copy "too serious" was told the brand "calls for more serious tone", advice that moves the score further out of tolerance. And creating a brand silently repointed every skill at it with no history and no notice; it now announces the change, records the previous slug, and prints the way back. 163 skills, 358 tests. Previously — Just shipped — v3.27.0 (August 15, 2026): the humanize gate got measured against writing that predates ChatGPT, and lost a signal that was pointing the wrong way. A calibration corpus of 39 documents published before 2022-11-01 — before ChatGPT was public, so human authorship is guaranteed by publication date rather than assumed — across four registers, cut into 272 chunks of ~1000 words so both classes are compared at equal length, against 18 documents of default model prose. Of the 45 words in the LLM-favored lexicon, 23 fired — every one only on the human class, none on the model class. "robust", "facilitate" and "leverage" are ordinary technical English while current models have largely been trained off them, so as a gating signal it could only ever produce false positives; it is now advisory. And the gate now says what it actually proves: it fails 0 of 39 published human documents and catches 0 of 18 unedited model documents — a density floor, not evidence a piece was humanized. Also fixed: the content-engine told you to append scan output to 05-humanize.md while authorship.py measured that same file — on a real run that moved author_word_share 0.253 → 0.206 and flipped may_claim_authored to false, denying an author credit for work they did. violations stayed clean throughout, which is why nothing caught it. 163 skills, 358 tests. Previously — Just shipped — v3.26.2 (August 14, 2026): the humanize gate stops being a vibe, and the author stays in the piece. The content-engine's humanize_passed gate asked for "AI-pattern density below the brand threshold (under 10% of paragraphs flagged)" while nothing in the repo defined what a flag was — no catalog, no agent, no script. A gate whose measurement is undefined does not fail; it passes on impression. scripts/ai-tell-scan.py is the missing definition: deterministic surface tells (LLM-favored vocabulary, significance markers like "here's the thing" / "that's the part that got me" — which are DELETED, never reworded — and soft-adverb clusters) with a real per-paragraph flag rate. Crucially it gates on only the tells precise enough to gate on: measured against hand-written copy, the short-declarative heuristic alone flagged half the paragraphs of a good piece, so connective openers, participial openers and ungrounded one-liners stay advisory — a gate that fails human writing is worse than the undefined one it replaced. Bring your own words (--source-draft): hand the pipeline your own rough draft and it builds the piece around your sentences — carried through verbatim, typos and all, exempt from every tell, with scripts/authorship.py verifying afterwards that nothing of yours was paraphrased or dropped. That check BLOCKS rather than advises, because a detector signal is a probabilistic opinion but "the author wrote this and it is gone" is a fact. When the record earns it (25% floor, zero violations) the disclosure becomes provenance-accurate — read off the record, never requested, so it can only ever understate human authorship. Plus entity development in the structural scan: specifics name-dropped once and abandoned, fixed by developing an existing verified fact — never by deleting specifics, never by inventing a mention. 163 skills, 358 tests. Previously — v3.24.0 (August 12): the timing ladder. Posting-time and send-time recommendations no longer come from static best-times tables. Both scripts now put the brand's OWN data first (--history: ranked windows with sample sizes and statistical floors — the only path to high confidence), keep the population tables only as dated, medium-capped test starting points that refuse when stale, and carry 2026 mechanics in every output (interest-ranked feeds reward early velocity but content strength dominates timing; per-recipient ESP send-time optimization beats any global window). Earlier today — v3.23.0: capability-first translation. The last hardcoded-vendor surface is gone: the language router no longer ships a "language family → product" table. It names what each family requires from a translation service (native script-aware models for Indic, formality registers for European, segmentation and variant control for CJK) and resolves a concrete service at run time — from the brand's recorded preference or the MCP servers you have actually connected — refusing with a resolution ladder rather than naming a product from memory. A vendor-neutrality guard keeps the instruction surface clean permanently. Also today — v3.22.0: the Routing Layer. All 163 skill descriptions rewritten to the trigger-dense house pattern — what the skill does and produces, the trigger phrases a user would actually type (slash alias first), and what it reads or pairs with — because the description is the only thing the model reads when routing a request across 163 skills. The rewrite doubled as an honesty audit: plan-only skills now say "it publishes nothing", every approval-gated execution skill names its gate, and overclaims in the old copy (a "launching, managing" orchestrator that only plans; a promised SLA document that is a terms outline) were corrected. A new density guard pins the pattern per skill plus a median floor so the surface cannot thin back out. Same day — v3.21.x: the Flagship Contract. Every market-priced figure in the repo now carries provenance: 28 benchmark docs live-verified and stamped, backed by a new benchmark_book.py (numbers enter only with a source URL + as-of date; stale quotes are refused, never reused) — the pass that caught WhatsApp's retired conversation-based billing and repriced tool tables. A machine-verified depth contract (skills-index.json) publishes what each of the 163 skills guarantees — 108 execute real scripts, 12 route through quality gates, 43 are structured guidance — with drift tests that fail if a skill's promises ever diverge from its machinery. Model resolution gains an execution ladder (--for-execution: provenance and registry age travel with every id; unknowns are refused, never guessed), /digital-marketing-pro:help --intent routes goals to skill chains instead of alphabetical lists, and a new end-to-end engagement smoke test proves the executable spine — intake → benchmarks → media math → campaign store — preserves provenance at every joint, on every release. Previously — v3.18.0–v3.20.0 (August 12): the creator-craft wave. Four strategy skills mined from creator-economy practice (/goal-filter, /story-mine, /signal-mine, /lead-magnet-ideas), /video-packaging with the title/thumbnail pairing principle, and video ad scripting wired into the house quality machinery (6s/15s/30s/UGC ad-format physics, campaign-context inheritance, every script through /check). Read what's new → · Full changelog →

Previously — v3.15.0 (July 7, 2026): The Reliability & Truth release — a full-repo audit fixed ~200 findings in one pass. Connectors are honestly opt-in (the shipped .mcp.json is empty; fictional npm packages purged), all 18 execution skills carry a uniform typed-approval gate (closes issue #6), the Tessl review workflow moved to the tessl review CLI (closes issue #8), agents consolidated 25 → 24, C2PA gained the Article 50 --ai-disclosure assertion, and a new doc-vs-argparse contract linter + state-layer tests grew the suite from 123 to 207 passing. Read what's new → · Full changelog →

# Install — one line
/plugin marketplace add indranilbanerjee/neels-plugins
/plugin install digital-marketing-pro@neels-plugins

If this saves you time, give it a star ⭐ — it's the single thing that helps other marketers find it.

Who this is for

If you're a... Run this What you get
🏢 Marketing agency managing 50–200 brands /digital-marketing-pro:engagement per brand, then /digital-marketing-pro:cowork-setup for team Drive persistence Same 12-Part Strategy Flow audited across every brand. New-hire onboarding goes from 6 weeks to 6 hours. Per-brand AI cost rollup via :agency-dashboard.
👔 In-house marketing team (B2B SaaS · e-commerce · fintech · healthtech) /digital-marketing-pro:engagement once to anchor strategy, then :content-engine + :campaign-plan for ongoing work A single canonical strategy doc, monthly stakeholder reports via :performance-report, content + campaigns that tie back to the strategy instead of drifting.
🚀 Marketing automation builder (n8n · Zapier · Make · Pipedream · custom) /digital-marketing-pro:doctor to see what's wired, :execute-action to fire real API calls 8 verified HTTP connectors executing end-to-end (Slack · HubSpot · Klaviyo · SendGrid · Brevo · Customer.io · Mailchimp · Ahrefs); 25 OAuth connectors via MCP manifest. Stdlib only, no third-party deps.
💼 Solo consultant or freelance marketer /digital-marketing-pro:engagement per client 50–60 canonical files per client engagement in ~60 minutes for $15–40 of API spend. Same depth on every project. Installs on Codex / Cursor / Copilot CLI / Antigravity if you don't live in Claude.
📈 Growth team / product marketer :funnel-architect → :analytics-insights → :attribution-model → :churn-risk → :cohort-analysis Journey design + measurement + retention + churn — all aligned to the strategy document, not isolated outputs. MMM + incrementality testing baked in.
🛡 Compliance-led marketer (EU · UK · India · Brazil · California) /digital-marketing-pro:check before publishing anything C2PA content provenance, EU AI Act Article 50 disclosure, GDPR + CCPA + DPDPA + LGPD across 16 jurisdictions, deepfake disclosure clauses on every AI creative brief.

How does this compare?

Digital Marketing Pro Anthropic Marketing (official) Composio Marketing claude-seo (community)
Skills count 163 ~7 ~12 25 SEO-only sub-skills
Specialist agents 24 0 0 18 SEO-only
Has a methodology Yes — 12-Part Strategy Flow (61 explicit steps) No No No
Multi-brand / agency support Yes — per-brand state, brand-switch, agency-dashboard No No No
EU AI Act Article 50 ready Yes — C2PA + deepfake disclosure + 16 jurisdictions No No Partial
Cowork team persistence Yes — Drive MCP routing (v3.12.0) Cowork-native Composio cloud n/a
Real API execution Yes — 8 connectors live, 25 manifest-ready OAuth via plugin OAuth via Composio Optional DataForSEO / Firecrawl
6-platform AEO/GEO audit Yes — incl. Google AI Mode (May 2026) No No Yes (AEO + GEO)
Cross-platform install 9 native — CC + Cowork + Codex + Cursor + Copilot CLI + Antigravity + Hermes + OpenClaw + Grok Cowork only Cowork + Codex CC + Codex
Tests 209 stdlib unittest unknown unknown 271 incl. SSRF/DNS coverage
License MIT — no telemetry, no seats Proprietary Proprietary MIT
Maintainer responsiveness Direct via @askneelnow Anthropic queue Composio queue Community

Get started in 5 minutes (non-developer path)

Are you a marketer, agency owner, or content lead who doesn't live in a terminal? Here's the fastest path:

  1. Open Anthropic Cowork in your browser (no installation, no terminal, no command line). Sign up free if you don't have an account.
  2. Click your profile menu → Settings → Plugins → Add Marketplace. Paste: indranilbanerjee/neels-plugins
  3. Find "Digital Marketing Pro" in the list → click Install.
  4. Type in chat: "Let's set up a brand for ACME Corp" — Claude will walk you through brand setup (voice, audience, jurisdiction, competitors).
  5. Then ask: "Run a full marketing engagement for ACME" — and watch ~50–60 strategy documents get produced over the next ~60 minutes.

That's it. You never touched a command line. Your team Drive will hold the outputs. Re-open Cowork tomorrow and pick up where you left off.

If you're more technical, see Quick start below for the Claude Code CLI install (one terminal command).

For team usage (agencies running 50+ brands), also run /digital-marketing-pro:cowork-setup once so brand state persists across Cowork sessions via your team's Google Drive.

Why Digital Marketing Pro

Most AI marketing tools generate isolated outputs — a campaign brief here, an email there. No canonical sequence, no shared state, no enforced structure. Result: inconsistent depth, missed dependencies, outputs that don't compound.

DM Pro runs every brand through the same 12 parts, producing the same files in the same order, with explicit dependency rules between them. That's the whole product. Everything else — the 163 skills, 24 agents, May–June 2026 compliance updates, Cowork persistence — exists to make that 12-Part Flow ship cleanly across real marketing operations.

What this gives you that ad-hoc prompts don't Why it matters
Canonical 12-Part Strategy Flow producing the Four Core Documents (61 explicit steps) Every engagement looks the same, so handoffs work and quality is auditable
Two-Views Model (v1 unbiased + v2 client-validated) You never lose the original market view when the client pushes back
Decision Matrix — maps validation responses to re-runs Stops over-running (wasted hours) and under-running (broken strategy)
Living Project Instruction File — single source of truth per engagement All skills read it first; corrections propagate automatically
EU AI Act Article 50 readiness built in C2PA provenance signing, deepfake disclosure, final Article 50 Guidelines + Code of Practice (10 June 2026) in compliance
6-platform AEO/GEO audit (incl. Google AI Mode) The first marketing plugin to treat AI Mode as a distinct surface from AI Overviews

What you get in 60 minutes

Run /digital-marketing-pro:engagement and the plugin produces a full brand-strategy engagement in roughly 60 minutes on Opus 4.8/Opus 5-class models — ~50–60 canonical files organized by part:

  • Part 1 — Stone-vs-Opinion intake (what the client knows for certain vs what they believe)
  • Part 2 — External market research (unbiased, no client docs)
  • Part 3 — Four Core Documents — 61 explicit steps across Business & SBU Analysis, Segmentation Framework, Brand Positioning & Communications, DMFlow
  • Part 4 — Competitive + Customer + Market analysis (4 unbiased docs)
  • Part 5 — Client Validation Document — the one true stop
  • Part 6 — Selective v2 re-runs per Decision Matrix
  • Part 7 — Preparation documents (campaign architecture, KPI tree, content pillars, approval chains)
  • Part 8 — Growth Plan + 12-month Yearly Planner (the flagship deliverable)
  • Part 9 — Channel-strategy fan-out (up to 17 channel docs in 7 families)
  • Part 10 — Execution artefacts (ad copy, post copy, headlines, CTAs)
  • Part 11 — AI creative briefs (with Nano Banana Pro / Veo 3.1 / Gemini Omni model guidance and C2PA + deepfake-disclosure clauses)
  • Part 12 — Continuous improvement loop

Cost: roughly $15–40 in Claude API spend for a full 12-part engagement using Opus 4.8 or Opus 5 (same $5/$25 per-MTok pricing). The plugin itself is MIT-licensed and free.

Quick start

1. Install on Claude Code (canonical)

/plugin marketplace add indranilbanerjee/neels-plugins
/plugin install digital-marketing-pro@neels-plugins

/plugin commands work in Claude Code (CLI + IDE at claude.com/code) and Anthropic Cowork. In the standard Claude chat app (browser claude.ai OR the installed Claude Desktop app) plugins still install and run, but management is via the Plugins UI button at the bottom of the chat — not via /plugin slash commands. See the Updating section for the recovery procedure if you accidentally try a slash command in the chat UI.

2. Turn on auto-update (recommended)

Third-party marketplaces have auto-update OFF by default in Claude Code — no banner tells you when a new version ships. Fix it once:

Open /plugin → Marketplaces tab → find neels-plugins → toggle Enable auto-update. Done — future releases pull at session start; /reload-plugins applies mid-session without restart.

3. Set up your first brand

/digital-marketing-pro:brand-setup

Interactive brand profiling — voice, audience, channels, industry, target jurisdictions, competitors, goals. Quick mode (5 questions) or full mode (17 questions). Optional: /digital-marketing-pro:import-guidelines to bulk-load existing brand guidelines, SOPs, or templates.

4. Run a full engagement, or jump straight to a workflow

/digital-marketing-pro:engagement           # full 12-Part Strategy Flow (~60 min)

Or jump straight to one workflow:

/digital-marketing-pro:campaign-plan        # multi-channel campaign with budget, timeline, KPIs
/digital-marketing-pro:seo-audit            # technical + content + E-E-A-T + AI visibility audit
/digital-marketing-pro:content-engine       # blog / ad / email / social / landing / video drafts
/digital-marketing-pro:competitor-analysis  # multi-dimensional deep-dive
/digital-marketing-pro:performance-report   # trends + anomalies + recommendations
/digital-marketing-pro:email-sequence       # subject lines, copy, timing, segmentation
/digital-marketing-pro:check                # pre-publish quality gate (hallucination + voice + claims)
/digital-marketing-pro:status               # unified brand snapshot
/digital-marketing-pro:resume               # resume an interrupted long workflow (engagement / campaign-plan / etc.)
/digital-marketing-pro:output-folder        # open the user-visible ~/Documents/DigitalMarketingPro/ folder

5. Find your output

~/.claude-marketing/<brand-slug>/
├── brand-profile.json           ← brand voice, audience, guardrails, jurisdictions
├── engagements/
│   └── <engagement-slug>/
│       ├── 01-client-inputs/    ← Part 1 Stone-vs-Opinion intake
│       ├── 02-research/         ← Part 2 external market research
│       ├── 03-four-core/        ← Part 3 Four Core Documents (61 steps)
│       ├── 04-analysis/         ← Part 4 competitive / customer / market
│       ├── 05-validation/       ← Part 5 Client Validation Document
│       ├── 06-v2-reruns/        ← Part 6 selective v2 re-runs
│       ├── 07-prep/             ← Part 7 internal operating layer
│       ├── 08-growth-plan/      ← Part 8 Growth Plan + Yearly Planner
│       ├── 09-channels/         ← Part 9 channel-strategy fan-out
│       ├── 10-execution/        ← Part 10 ad copy / post copy / headlines / CTAs
│       ├── 11-creative-briefs/  ← Part 11 AI creative instructions
│       ├── 12-improvement/      ← Part 12 continuous improvement loop
│       └── PROJECT_INSTRUCTIONS.md  ← Living Project Instruction File
└── insights/                    ← cross-engagement learnings

See the Multi-Brand & Agency Guide for the multi-client switching workflow.

Real workflows you'd actually run

🆕 New-client onboarding (agency, week 1)

/digital-marketing-pro:brand-setup "ACME Corp"        # interactive: voice, audience, channels, jurisdiction
/digital-marketing-pro:competitor-analysis            # multi-dimensional deep-dive on top 5 competitors
/digital-marketing-pro:engagement                     # full 12-Part Strategy Flow (~60 min on Opus-class)
/digital-marketing-pro:check  engagements/.../03-four-core/*.md   # pre-publish gate before client review

Output: ~50–60 canonical files. Cost: $15–40 in API spend. Time saved: ~3 weeks of senior-strategist labor.

📊 Quarterly business review (in-house, last week of quarter)

/digital-marketing-pro:performance-report   --period=Q2-2026
/digital-marketing-pro:attribution-report   --period=Q2-2026 --model=data-driven
/digital-marketing-pro:competitor-monitor   --since=2026-04-01
/digital-marketing-pro:continuous-improvement-loop --quarter=Q2-2026

Output: stakeholder-ready Q2 review with anomalies, attribution shift, competitor moves, and next-quarter recommendations.

🎯 SEO sprint (any audience, 1 week)

/digital-marketing-pro:seo-plan                       # 4-pillar scorecard; weakest pillar drives the theme
/digital-marketing-pro:keyword-cluster  seeds.csv     # SERP-overlap clustering into pillar+spokes
/digital-marketing-pro:backlink-gap  acme.com competitor1.com competitor2.com
/digital-marketing-pro:content-engine                 # draft the top 3 pillar pages
/digital-marketing-pro:check  drafts/*.md             # hallucination + brand voice + claims gate
/digital-marketing-pro:seo-drift  baseline.csv current.csv     # 30 days later, what moved

🤖 Marketing automation flow (builders)

/digital-marketing-pro:doctor                         # which actions are live vs need connector setup
/digital-marketing-pro:execute-action --action diagnostic --execute            # GA4 + GSC pull
/digital-marketing-pro:execute-action --action audit-current --execute         # workflow state check
/digital-marketing-pro:execute-action --action enable-automation --confirm     # Klaviyo flow activate

Output: real API calls fired against your stack with audit logging at ~/.claude-marketing/{brand}/executions/. Combine with n8n / Make / Zapier for human-in-the-loop approval gates.

🛡 Pre-publish compliance gate (every campaign)

/digital-marketing-pro:check  campaign.md --full      # hallucination + voice + claims + jurisdictions
/digital-marketing-pro:c2pa-metadata  hero.png        # sign image with provenance for EU Article 50

🎨 AI creative brief with EU disclosure (every AI-generated asset)

/digital-marketing-pro:ad-creative                # ad concepts + copy with EU/FTC disclosure clauses
/digital-marketing-pro:influencer-creator         # FTC + EU deepfake clauses baked in

Supported surfaces (v3.31.0)

Platform Install command Manifest path Status
Claude Code CLI + IDE extensions /plugin install digital-marketing-pro@neels-plugins .claude-plugin/plugin.json Full support (canonical)
Anthropic Cowork Plugins UI → Add marketplace → indranilbanerjee/neels-plugins → Install same .claude-plugin/ files Full support — no /plugin slash commands in Cowork (UI-only)
OpenAI Codex CLI + IDE + App codex plugin marketplace add indranilbanerjee/neels-plugins then codex plugin install digital-marketing-pro@neels-plugins .codex-plugin/plugin.json (published OpenAI schema) Full skills + MCP support
Cursor 2.5+ In any Cursor Agent chat: /add-plugin digital-marketing-pro@https://github.com/indranilbanerjee/digital-marketing-pro .cursor-plugin/plugin.json (published Cursor JSON Schema) Full skills + agents + commands support
GitHub Copilot CLI copilot plugin marketplace add indranilbanerjee/neels-plugins then copilot plugin install digital-marketing-pro@neels-plugins .github/plugin/plugin.json (Copilot CLI also recognizes .claude-plugin/plugin.json as fallback) Full skills + MCP support; subagents need .agent.md extension (open issue); custom slash commands not yet supported in Copilot CLI
Google Antigravity 2.0 CLI + IDE agy plugin install https://github.com/indranilbanerjee/digital-marketing-pro gemini-extension.json (at repo root, per Google's reference pattern) Full skills + hooks support; subagents need /agent CLI spawning; slash commands fold into skills via agy plugin import gemini
Hermes Agent (Nous Research) — Desktop + CLI on macOS / Windows / Linux hermes plugins install indranilbanerjee/digital-marketing-pro plugin.yaml + __init__.py at repo root (Hermes native spec) Native plugin — adapter walks skills/ at register time and exposes all 163 skills via ctx.register_skill(). Targets Hermes Desktop v0.15.2+ (public preview June 2 2026).
OpenClaw (formerly Clawdbot / Moltbot) openclaw plugins install git:github.com/indranilbanerjee/digital-marketing-pro openclaw.plugin.json at repo root (also auto-detects .claude-plugin/plugin.json as Claude-compatible bundle) Native plugin via openclaw.plugin.json; skills field points at ./skills. Also installable via ClawHub marketplace (submission pending).
Grok (xAI Build CLI) grok plugin install indranilbanerjee/digital-marketing-pro — or grok plugin marketplace add indranilbanerjee/neels-plugins then grok plugin install digital-marketing-pro (append --trust to skip the install confirmation) .grok-plugin/plugin.json + .grok-plugin/marketplace.json (Grok Build also reads the Claude Code manifests for compatibility; the native pair is the first-class lane) Full skills support

Why this works: Agent Skills became an open standard in December 2025 (donated to the Agentic AI Foundation; adopted by 41+ agent products by June 2026 — see "Works on 40+ agent harnesses" below). All 163 SKILL.md files in DM Pro are platform-portable as written. The sibling manifests are thin platform-specific wrappers around the same skills/ directory — no skill duplication, no maintenance fork. The pattern is borrowed from Google's reference repo gemini-cli-extensions/data-agent-kit-starter-pack.

Minimum Claude Code version: 2.1.157 (declared via requiredMinimumVersion in plugin.json — landed in Claude Code v2.1.163, June 4 2026). Older Claude Code builds will be told to upgrade rather than load DMP with missing features.

Works on 40+ agent harnesses (via the Agent Skills open standard)

Beyond the 9 surfaces above where we ship a native manifest, DMP's 163 SKILL.md files work out-of-the-box on any agent that adopted the Agent Skills open standard (Anthropic-published Dec 2025, 41+ adopters as of June 2026). On each platform below, point it at our skills/ folder and all 163 marketing skills are immediately discoverable. No platform-specific manifest needed.

Tier 1 — verified-compatible platforms with explicit Agent Skills install paths:

Platform Vendor Install hint
Goose Block (Square) goose skills install github.com/indranilbanerjee/digital-marketing-pro/skills
OpenHands Open Hands (cloud agents) Mount this repo's skills/ via the OpenHands skills config
OpenCode sst opencode skills import github:indranilbanerjee/digital-marketing-pro
Junie JetBrains Drop skills/ into your project; Junie auto-discovers
Gemini CLI Google gemini skills add github:indranilbanerjee/digital-marketing-pro
Roo Code Roo Code Inc. VS Code → Roo settings → Skills → import from URL
Cline / Windsurf open-source VS Code agents Same Agent Skills import flow as Roo
Kiro Kiro Spec-driven dev with Agent Skills support
Amp Sourcegraph amp skills add github:indranilbanerjee/digital-marketing-pro
Letta Letta Stateful-agents platform — skills load via the Letta SDK
Mux Coder Browser-based parallel cloud agents
Factory Factory "Droid" agents read Agent Skills bundles
Workshop Workshop Multi-LLM cross-platform agent
Tabnine Tabnine Enterprise context-aware AI agent
Emdash General Action Parallel git-worktree agents
Superconductor Superconductor Multiplayer cloud agents
Ona Ona Background cloud-agent fleet
Mistral Vibe Mistral AI mistral-vibe skills install ...
VT Code open-source LLM-native code agent
Qodo Qodo Code integrity agent
Piebald Piebald Desktop agentic dev
Autohand Code CLI Autohand ReAct terminal agent
pi open-source Minimal terminal harness
Command Code Command Code Coding-taste-learning agent
TRAE ByteDance Adaptive AI IDE
Firebender Firebender Android-native agent
bub Bub Channel-native agent framework
fast-agent evalstate ACPX + Skills development
nanobot HKUDS Ultra-light personal agent (Slack / Discord / Telegram / WeChat)
Vita Vita Virtual-desktop autonomous workers
Snowflake Cortex Code Snowflake Data-platform agent
Databricks Genie Code Databricks Data-engineering agent
Laravel Boost Laravel Laravel-specific agent skills layer
Spring AI Spring Java/Spring AI applications
Agentman Agentman Healthcare revenue-cycle agents
Google AI Edge Gallery Google On-device mobile LLM agent

Quick test on any Tier-1 platform:

# 1. Clone the skills folder (or point your platform at the GitHub raw URL)
git clone --depth=1 https://github.com/indranilbanerjee/digital-marketing-pro.git
# 2. Point your agent's skills-config at ./digital-marketing-pro/skills
# 3. Try: "Run a competitor analysis on stripe.com"
# Your agent picks /digital-marketing-pro:competitor-analysis automatically.

Why we don't ship per-platform manifests for these: the Agent Skills standard says agents discover by walking a directory tree for SKILL.md files — no manifest required. Shipping 35 extra wrapper manifests would create maintenance overhead with zero added value.

If you run into a platform-specific install snag, file a GitHub issue — we'll add platform-specific docs as users report patterns.

The 12-Part Engagement Methodology

Part Name Output
1 Client Inputs Stone vs Opinion intake (what client knows for certain vs what they believe)
2 External Research Unbiased market research (no client docs used)
3 Four Core Documents 61 explicit steps — Business & SBU (18), Segmentation (15), Brand Positioning (19), DMFlow (9)
4 Competitive + Customer + Market 4 unbiased analysis documents (4.1–4.4)
5 Client Validation Document The one true stop — client accepts/rejects/edits each finding
6 Selective v2 Re-runs Subset of Part 3 + Part 4 docs re-run per the Decision Matrix
7 Preparation Documents Internal operating layer (campaign architecture, KPI tree, content pillars, asset inventory, approval chains)
8