Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add acogood/diffmode_free/plugin install diffmode-growth-tacticsWrote 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.
[](https://agentmods.dev/skills/acogood/diffmode_free/enrichment-audience)<a href="https://agentmods.dev/skills/acogood/diffmode_free/enrichment-audience"><img src="https://agentmods.dev/badge/skills/acogood/diffmode_free/enrichment-audience.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00087 | $0.01900 |
| Opus 5 | $0.00044 | $0.00950 |
| Sonnet 5 | $0.00017 | $0.00380 |
| Haiku 4.5 | $0.00009 | $0.00190 |
Grade A, and why
enrichment-audience 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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).
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.
- 8d ago First seen · 150 lines · 87 tokens per session scan A 12633422412c
enrichment-audience is a skill published in the GitHub repository acogood/diffmode_free (161 stars, last pushed 27d ago), licensed Apache-2.0. It adds 87 tokens to every session and 1,900 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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