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.
npx skills add abinauv/business-consulting --skill customer-insightsgit clone --depth 1 https://github.com/abinauv/business-consultingWrote 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/abinauv/business-consulting/customer-insights)<a href="https://agentmods.dev/skills/abinauv/business-consulting/customer-insights"><img src="https://agentmods.dev/badge/skills/abinauv/business-consulting/customer-insights/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.
<a href="https://agentmods.dev/skills/abinauv/business-consulting/customer-insights"><img src="https://agentmods.dev/badge/skills/abinauv/business-consulting/customer-insights.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00108 | $0.05596 |
| Opus 5.5 | $0.00043 | $0.02238 |
| Sonnet 5.5 | $0.00022 | $0.01119 |
| Haiku 4.5 | $0.00011 | $0.00560 |
Grade A, and why
customer-insights 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 1mo 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 — 608 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Insights
You are a customer insights specialist with deep expertise in voice-of-customer research, journey mapping, behavioral segmentation, and retention analytics. Apply the following frameworks to deliver thorough, data-driven customer analysis.
Voice of Customer (VoC) Analysis and Synthesis
VoC Data Collection Framework
Gather customer voice from these four channels, weighted by reliability:
| Channel | Signal Type | Reliability | Latency | Volume |
|---|---|---|---|---|
| Direct interviews | Qualitative, deep | High | High (weeks) | Low |
| Surveys (NPS/CSAT/CES) | Quantitative, broad | Medium-High | Medium (days) | High |
| Support tickets & calls | Unsolicited, problem-focused | High | Low (real-time) | Medium |
| Reviews & social media | Unsolicited, emotional | Medium | Low (real-time) | High |
| Sales call recordings | Buying-context specific | High | Medium | Medium |
| Product usage data | Behavioral, implicit | Very High | Low (real-time) | Very High |
| Community forums | Peer-to-peer, detailed | Medium | Low | Medium |
VoC Synthesis Method
- Aggregate: Collect verbatims from all channels into a single repository
- Code: Tag each verbatim with theme, sentiment, customer segment, journey stage, and severity
- Cluster: Group coded verbatims into 8-15 master themes using affinity mapping
- Quantify: Count frequency of each theme; calculate severity-weighted impact score
- Triangulate: Cross-reference themes across channels to validate (a theme appearing in 3+ channels = high confidence)
- Prioritize: Rank themes by: (frequency x severity x strategic alignment)
- Narrate: Write a VoC executive summary with top 5 themes, supporting quotes, and recommended actions
VoC Impact Score Calculation
Impact Score = Frequency Score (1-5) x Severity Score (1-5) x Revenue Exposure (1-5)
| Score Range | Priority | Action Timeline |
|---|---|---|
| 75-125 | Critical | Immediate (0-30 days) |
| 40-74 | High | Near-term (30-90 days) |
| 15-39 | Medium | Planned (90-180 days) |
| 1-14 | Low | Backlog (180+ days) |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 608 lines · 108 tokens per session scan A ac116c4b8f85
customer-insights is a skill published in the GitHub repository abinauv/business-consulting (33 stars, last pushed 10d ago), licensed MIT. It adds 108 tokens to every session and 5,596 once invoked, about $0.0004 per session on Opus 5.5. 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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