Agent skill

Enrichment Audience

by acogood in acogood/diffmode_free

Advanced JTBD + audience segmentation for a founder's product (AI-CMO enrichment ENR-001).

Apache-2.0Auto-check passedProduct & Project Management

Install Enrichment Audience

skills CLI
$ npx skills add acogood/diffmode_free --skill enrichment-audience -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install acogood/diffmode_free enrichment-audience --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/acogood/diffmode_free.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/enrichment-audience .claude/skills/enrichment-audience && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
enrichment-audience
GitHub stars
163
Token cost
~2k tokens
SKILL.md length
738 words
Files
1
Skills in repo
13
Repo updated
First seen
Licence
Apache-2.0

At a glance

Advanced JTBD + audience segmentation for a founder's product (AI-CMO enrichment ENR-001).

  • Works in 3 steps: Identify 3-4 customer segments → Evaluate ALL segments against criteria… → 5 — Channel-fit analysis framework (per…
  • Running the enrichment stages audience dimension
  • SKILL.md covers Inputs & Output, NO WEB RESEARCH (structural…, Scope (CRITICAL) and Analysis framework, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Enrichment Audience is an agent skill from acogood/diffmode_free. Advanced JTBD + audience segmentation for a founder's product (AI-CMO enrichment ENR-001). Identify 3-4 distinct segments with full Jobs-To-Be-Done structure, score each on 6 criteria, and document per-segment channel-fit — ANALYSIS ONLY, NO web research. Use when running the enrichment stage's audience dimension or when asked to segment a product's customers with JTBD.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Product & Project Management, covering User stories. The repository describes itself as: Free guerrilla growth tactics for startups, the kind your competitors won't come up with on their own. Runs in Claude Code or Codex: competitor read, buyer map, and 7 to 9… The licence is Apache-2.0.

When your agent uses it

  • Running the enrichment stages audience dimension
  • Asked to segment a products customers with JTBD

Example prompts

  • “s audience dimension or when asked to segment a product”
  • “/enrichment-audience”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Identify 3-4 customer segments
  2. Evaluate ALL segments against criteria (1-10)
  3. 5 — Channel-fit analysis framework (per segment)

What it can do on your machine

Read from SKILL.md and the folder at commit c175a4d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Enrichment Audience loads about 2k tokens when it runs. Until then it costs about 98 tokens; SKILL.md has 738 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~98
When it runs · the whole SKILL.md, loaded when a task matches
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from acogood/diffmode_free at commit c175a4d, republished under its Apache-2.0 licence (© acogood). 738 words, ~1,996 tokens.

Download SKILL.mdSave it as .claude/skills/enrichment-audience/SKILL.md (or your agent's skills folder).
name
enrichment-audience
description
Advanced JTBD + audience segmentation for a founder's product (AI-CMO enrichment ENR-001). Identify 3-4 distinct segments with full Jobs-To-Be-Done structure, score each on 6 criteria, and document per-segment channel-fit — ANALYSIS ONLY, NO web research. Use when running the enrichment stage's audience dimension or when asked to segment a product's customers with JTBD.
metadata.version
1.0.0

Enrichment — Advanced Audience & JTBD Analysis (ENR-001)

You are a senior product marketing strategist with deep expertise in Advanced Jobs To Be Done (JTBD). You analyze potential customer segments and document segment characteristics with evaluation criteria.

Distilled from the Diffmode AI-CMO enrichment methodology (ENR-001). This is the logic; an orchestrator/worker supplies file paths and control flow.

Inputs & Output

  • INPUT — founder context (required): 01-diagnostics/founder-input.md.
  • INPUT — competitive intelligence (required): 02-enrichment/competitors-analysis.md — use for the Competitive Channel Matrix, channel strategies, market context.
  • INPUT — channel taxonomy (required): the bundled channel menu at ${CLAUDE_PLUGIN_ROOT}/reference/Marketing-Channel-Menu-2026.md. NOTE: the legacy Python pipeline omitted this input even though the analysis below depends on it (Step 2.5 channel-fit). This skill declares it required — a deliberate fix, not the latent bug.
  • OUTPUT: 02-enrichment/audience-jtbd.md (path supplied by invoker).

NO WEB RESEARCH (structural rule)

Work ONLY with the information in the input files. You may reference general market knowledge, but do NOT conduct new web searches or external research. This dimension is intentionally run by a worker that has no web-research tool, so the rule is enforced structurally — honor it.

Scope (CRITICAL)

✅ DO: Identify/document 3-4 distinct segments with JTBD; document 6-criteria scores; document per-segment channel fit. ❌ DON'T: select or rank "top" segments (→ Strategic Prioritization); make channel "recommendations" (document fit analysis only); prioritize. Present all segments neutrally.

Analysis framework

Step 1 — Identify 3-4 customer segments

For each segment, use the Advanced JTBD structure:

  • Segment Name & Portrait — 2-3 sentences (role, context, daily challenges).
  • Core Job:
    • when → context (1-2 sentences) · trigger (1 sentence — the activation moment) · activating knowledge (1 sentence — what they believe) · emotions at point A (3-5 specific emotions, not generic "frustrated").
    • I want to [clear desired outcome in one sentence — not a solution].
    • success criteria: 3 measurable/specific points.
    • so that [Big Job] AND feel [emotional outcome].
    • Job frequency (daily/weekly/monthly/…).
    • Current alternatives & why they fail: 3 alternatives (include "do nothing"), one sentence each on why it fails.
    • Switching Costs Analysis: data/content migration · learning curve · workflow disruption · team buy-in · sunk-cost psychology · overall friction (Low/Med/High).

Each segment must have a distinct Core Job — not variations of the same job, not the same person at different times.

Step 2 — Evaluate ALL segments against criteria (1-10)

Score every segment neutrally on: Pain Intensity · Market Size · Willingness to Pay · Accessibility (use competitors-analysis.md) · Product Fit · Frequency. For each: the 6 scores with brief evidence (from input files, not invented), overall assessment confidence (High/Medium/Low) with reasoning, and potential channel fit (Step 2.5).

Show full SKILL.md (340 more words)Show less
Step 2.5 — Channel-fit analysis framework (per segment)
  1. Audience behavior (from JTBD): where they spend time (from context + activating knowledge); information-seeking behavior (research-heavy vs impulsive); triggers (from trigger + emotions); trust-building process (community vs authority).
  2. Channel evaluation using the Marketing Channel Menu:
    • A — Filter by founder constraints: eliminate Cost:High if budget <$2K/mo; prioritize Measurability:High; match Impact stage to journey position (awareness/ consideration/decision).
    • B — Cross-reference the Competitive Channel Matrix (from competitors-analysis): Saturated (6+ competitors) → avoid unless strong differentiation; Moderate (3-5) → viable with unique angle; Open (0-2) → PRIORITIZE.
    • C — Channel-segment fit matrix: B2C (young/digital-native → TikTok/IG/Discord/ Reddit; professional → LinkedIn/podcasts/newsletter; hobbyist → YouTube/forums/FB groups; price-sensitive → organic social/SEO/community). B2B (technical → dev communities/Discord/Reddit/technical SEO; business → LinkedIn/webinars/ partnerships/events; small business → FB groups/local/SEO; enterprise → LinkedIn/ sponsored events/partnerships/sales outreach). Behavior patterns (high-research → SEO/long-form/webinars; community-driven → Discord/Reddit/Slack; visual-first → TikTok/IG/YouTube; authority-trust → podcasts/speaking/PR).
    • D — Unconventional channel fit: micro-community (authenticity-valuing segments); guerrilla/stealth (early adopters); hyperlocal (geo-concentrated); psychological/FOMO (community-driven).
  3. Channel-fit output (per segment): 2 high-fit options (each: specific channel name; fit analysis [behavior match w/ JTBD evidence · cost/impact alignment · competitive adoption X/Y]; characteristics [Cost · Impact Stage · Measurability · Competitive Adoption]; 1-2 documented tactics from competitors-analysis.md) + 2+ medium-fit options (brief).

Output language

Body copy follows ${CLAUDE_PLUGIN_ROOT}/reference/writing-style.md (the invoker may also pass it as an input): plain English a busy founder reads fast — grade 6–8, short sentences, the banned-jargon table respected ("use" not "leverage"). The template's required section headings and field labels stay exactly as written.

Output template

Target length 1,500-2,000 words. Write to the supplied output path:

markdown
# Audience & JTBD Analysis

## Customer Segments (3-4 total)
### Segment N: [Name]
**Portrait:** …
**Core Job:** when (context/trigger/activating knowledge/emotions at point A) · I want
to … · success criteria (3) · so that … AND feel … · Job frequency
**Current alternatives & why they fail:** 3 (incl. do-nothing)
**Switching Costs:** data migration / learning curve / workflow disruption / overall
friction + **Implication for acquisition**
[repeat per segment]

## Segment Evaluation Summary
### Segment N: [Name]
**Evaluation Criteria Scores (1-10):** Pain Intensity · Market Size · Willingness to Pay
· Accessibility · Product Fit · Frequency (each with brief evidence)
**Overall Assessment Confidence:** High/Medium/Low — reasoning
**Potential Channel Fit Analysis:** High-Fit Option 1 (characteristics + fit analysis +
documented tactics) · High-Fit Option 2 · Medium-Fit Options (2+)
[repeat per segment]

DON'T add Executive Summary, Founder's Hypothesis vs Reality, Strategic Recommendations, or Key Insights sections. No web sources list. No generic "social media"/"content marketing" — name exact channels.

Calibration

Strong: 3-4 truly distinct Core Jobs; complete when (all 4 components, specific emotions); measurable success criteria; realistic alternatives incl. do-nothing; all 6 criteria scored with input-file evidence; channel fit references JTBD behavior + names specific channels + documents competitive adoption (X/Y) + cites tactics from competitors-analysis.md; neutral; ~1,500-2,000 words. Weak: 1-2 segments or non-distinct; incomplete JTBD; generic emotions; "I want to" describes a solution; missing competitive-adoption data; generic channel categories; any recommendations/prioritization (scope violation); >2,500 words.

© acogood, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugin/skills/enrichment-audience of acogood/diffmode_free.

Open the folder on GitHubat commit c175a4d

Compare with similar skills

Enrichment Audience next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Ralph Tui Create Beads Rustsubsy/ralph-tui2.5k1 repos~2.8kAutomated safety check: PassMIT
To Specbestofjs/bestofjs3.1k21 repos~757Automated safety check: PassMIT

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Questions about Enrichment Audience

What does Enrichment Audience do?

Advanced JTBD + audience segmentation for a founder's product (AI-CMO enrichment ENR-001). Enrichment Audience is an agent skill from acogood/diffmode_free. Advanced JTBD + audience segmentation for a founder's product (AI-CMO enrichment ENR-001).

When should I use Enrichment Audience?

Enrichment Audience fits situations like: running the enrichment stages audience dimension; asked to segment a products customers with JTBD.

How do I install Enrichment Audience in Claude Code?

Run `npx skills add acogood/diffmode_free --skill enrichment-audience -a claude-code`. Or copy the skill folder (plugin/skills/enrichment-audience in acogood/diffmode_free) into .claude/skills/enrichment-audience in your project. Claude Code loads it when a task matches its description.

How do I install Enrichment Audience in Codex?

Run `npx skills add acogood/diffmode_free --skill enrichment-audience -a codex`. Or copy the skill folder (plugin/skills/enrichment-audience in acogood/diffmode_free) into .agents/skills/enrichment-audience in your project. Codex loads it when a task matches its description.

Can I use Enrichment Audience in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add acogood/diffmode_free --skill enrichment-audience -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/enrichment-audience, .gemini/skills/enrichment-audience, .github/skills/enrichment-audience and .opencode/skills/enrichment-audience in your project.

What does Enrichment Audience need to run?

SKILL.md names no scripts, command-line tools or credentials: Enrichment Audience is instructions for the agent only. Our summary lists: Python 3.

Does Enrichment Audience access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Enrichment Audience safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Enrichment Audience use?

Enrichment Audience is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Enrichment Audience use?

About 2k tokens (SKILL.md is roughly 8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Enrichment Audience?

Skills that share tags, products or a category with Enrichment Audience: User Story Writer (deanpeters/Product-Manager-Skills, 7.2k stars), Ralph Tui Create Beads (subsy/ralph-tui, 2.5k stars), Agile Product Owner (alirezarezvani/claude-skills, 28k stars) and Ralph Tui Create Beads Rust (subsy/ralph-tui, 2.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Enrichment Audience?

acogood (a GitHub user) maintains it in acogood/diffmode_free, which has 163 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on August 10, 2026.

Source: acogood/diffmode_free on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.