dark-funnel

dark-funnel is a skill for Claude Code, Codex from indranilbanerjee/neels-plugins. It costs 28 tokens per session (1,344 once invoked), scanned A, a copy of dark-funnel, MIT.

A research and measurement guide for the parts of a buyer's journey that normal analytics cannot directly track. It looks at places such as Reddit, private communities, podcasts, AI chatbots, and word of mouth.

In plain words
What is it for?
Use it to map community discussions, podcast effects, AI-related mentions, private sharing, and other hidden sources of demand.
Why use it?
It helps explain interest and brand mentions that may not appear in website analytics or standard attribution reports. This gives teams a way to investigate awareness and consideration outside trackable advertising channels.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: reads .claude/ paths.

Good fit Use it to map community discussions, podcast effects, AI-related mentions, private sharing, and other hidden sources of demand.

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Install with agentmods
npx agentmods add skills/indranilbanerjee/neels-plugins/dark-funnel
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add indranilbanerjee/neels-plugins --skill dark-funnel
Clone the repo
git clone --depth 1 https://github.com/indranilbanerjee/neels-plugins

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for dark-funnel

README.md
[![agentmods](https://agentmods.dev/badge/skills/indranilbanerjee/neels-plugins/dark-funnel/github.svg)](https://agentmods.dev/skills/indranilbanerjee/neels-plugins/dark-funnel)
Your own site
<a href="https://agentmods.dev/skills/indranilbanerjee/neels-plugins/dark-funnel"><img src="https://agentmods.dev/badge/skills/indranilbanerjee/neels-plugins/dark-funnel/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for dark-funnel

Your own site · 80×15
<a href="https://agentmods.dev/skills/indranilbanerjee/neels-plugins/dark-funnel"><img src="https://agentmods.dev/badge/skills/indranilbanerjee/neels-plugins/dark-funnel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,344 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 91% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00028 $0.01344
Opus 5.5 $0.00011 $0.00538
Sonnet 5.5 $0.00006 $0.00269
Haiku 4.5 $0.00003 $0.00134

Measured 2d ago against content hash df27cb48df7b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

dark-funnel scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

91% identical to dark-funnel — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

hermes/digital-marketing-pro/strategy/skills/dark-funnel/SKILL.md · 48 lines

How it starts

The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/digital-marketing-pro:dark-funnel

Purpose

Map and illuminate the dark funnel — buyer journey activities invisible to traditional attribution. Identify where prospects are encountering the brand outside of trackable channels (Reddit discussions, AI chatbot queries, podcast mentions, community forums, word-of-mouth, dark social sharing) and surface intent signals that reveal the true scope of brand awareness and consideration happening beyond what analytics platforms can measure.

Input Required

The user must provide (or will be prompted for):

  • Brand name and product names: The brand and specific products or services to track across dark funnel channels — used to define search terms, mention patterns, and signal queries
  • Known community presence: Subreddits, Slack communities, Discord servers, industry forums, or niche platforms where the brand has an official or organic presence — these are primary dark funnel listening posts
  • Podcast appearances: Episodes, shows, or sponsorships the brand has participated in — used to correlate vanity URL visits, promo code redemptions, and branded search spikes with specific air dates
  • AI visibility data (optional): Output from /digital-marketing-pro:geo-monitor showing how AI chatbots (ChatGPT, Perplexity, Gemini) reference or recommend the brand — a growing dark funnel channel
  • Branded search volume trends: Google Search Console or third-party keyword data showing branded search volume over time — the strongest proxy signal for offline and untracked brand exposure
  • "How did you hear about us" survey data (optional): Self-reported attribution from lead forms, onboarding flows, or post-purchase surveys — direct evidence of dark funnel touchpoints that customers themselves identify

Process

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice, industry context, and known competitive landscape. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
  2. Aggregate dark funnel signals: Collect and normalize data from all available dark funnel sources — branded search volume trends over time (the primary proxy for untracked exposure), Reddit and community mention frequency and sentiment, AI chatbot citation data from the geo-tracker, direct traffic anomalies that correlate with offline activities, podcast attribution data (vanity URL visits, promo code usage, post-episode traffic spikes), and self-reported survey response data categorized by source.
  3. Correlate signals with marketing activities: Cross-reference dark funnel signal spikes against a timeline of known marketing activities — do branded search spikes follow podcast episodes? Do community mentions surge after product launches or PR coverage? Does direct traffic increase after conference appearances? Identify which activities are generating the strongest dark funnel response and which have no measurable dark signal. Note: GA4's new "AI Assistant" default channel now makes some previously-dark AI-assistant referral traffic (ChatGPT, Gemini, Copilot, Perplexity, etc.) directly measurable — pull it from GA4 and treat it as lit, subtracting it from your estimated "dark" AI influence so the two are not double-counted.
  4. Map the invisible buyer journey: Construct a dark funnel map identifying each untracked touchpoint, its position in the buyer journey (awareness, consideration, decision), estimated audience size, and growth trajectory. Classify touchpoints by channel type — community (Reddit, forums, Discord), media (podcasts, YouTube mentions), AI (chatbot citations), social (dark social sharing via DMs and private groups), and word-of-mouth (survey-reported).
  5. Score dark funnel health per channel: Rate each dark funnel channel on signal strength (volume and reliability of data), growth trend (expanding, stable, or declining), brand sentiment within the channel, and conversion proximity (how close the channel is to purchase intent). Produce a composite dark funnel health score.
  6. Recommend dark funnel investment opportunities: Based on scoring and correlation analysis, identify the highest-ROI dark funnel investment opportunities — channels with strong signals but no intentional brand investment, emerging channels showing growth, and underperforming channels that could be amplified with targeted effort.

Read the full file on GitHub · 48 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 48 lines · 28 tokens per session scan A df27cb48df7b

Subscribe to this mod's changes

dark-funnel is a skill published in the GitHub repository indranilbanerjee/neels-plugins (6 stars, last pushed 2d ago), licensed MIT. It adds 28 tokens to every session and 1,344 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to dark-funnel, differing in 0 lines, and is treated as a copy.