enrichment-audience
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.
Profile →npx skills add acogood/diffmode_free --skill enrichment-audience --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 refer
- Inputs & Output
- NO WEB RESEARCH (structural rule)
- Scope (CRITICAL)
- Analysis framework
- Step 1 — Identify 3-4 customer segments
- Step 2 — Evaluate ALL segments against criteria (1-10)
- Step 2.5 — Channel-fit analysis framework (per segment)
- Output language
- Output template
- Calibration
What does the enrichment-audience skill do?
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.
How do I install it?
Run `npx skills add acogood/diffmode_free --skill enrichment-audience --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From acogood/diffmode_free, a repository with 153 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.