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 agentmods add skills/tupe12334/instinct/kano-modelnpx skills add tupe12334/instinct --skill kano-modelgit clone --depth 1 https://github.com/tupe12334/instinctWrote 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/tupe12334/instinct/kano-model)<a href="https://agentmods.dev/skills/tupe12334/instinct/kano-model"><img src="https://agentmods.dev/badge/skills/tupe12334/instinct/kano-model.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 | $0.00021 | $0.01980 |
| Opus 5 | $0.00010 | $0.00990 |
| Sonnet 5 | $0.00004 | $0.00396 |
| Haiku 4.5 | $0.00002 | $0.00198 |
Grade A, and why
kano-model 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 4d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kano Model
Overview
Framework for classifying product features by how they affect customer satisfaction. Developed by Noriaki Kano (1984). Core insight: not all features contribute equally — some are expected, some scale linearly, and some surprise and delight.
SATISFACTION
▲
│ ╭─── DELIGHT (Excitement)
│ ╭───╯
│ ╭──────╯ ╭── PERFORMANCE (Linear)
│ ╭───╯ ╭───╯
─────┼─────────────────────────────────► FEATURE PRESENT/ABSENT
│ ╭─────╯ BASIC (Must-be)
│╭──╯
▼
DISSATISFACTION
Feature Categories
Basic (Must-be / Threshold)
Expected by default. Absent = dissatisfied. Present = neutral. Customers never ask for these — they simply assume them.
- Example: Login works, data saves correctly, app does not crash on launch
Performance (One-dimensional / Linear)
Satisfaction scales directly with execution quality. More = better, less = worse. Customers benchmark these against competitors.
- Example: Page load speed, battery life, search accuracy, storage capacity
Delight (Excitement / Attractive)
Unexpected features that create positive surprise when present; no dissatisfaction when absent. High ROI until competitors copy them.
- Example: Proactive suggestions, smart defaults, surprising personalization, one-tap undo
Indifferent
Customers do not care either way. Common with internal engineering features accidentally exposed as UI.
Reverse
Presence actively annoys a segment of users. Often surfaces in power-user vs. casual-user splits (e.g., auto-play, onboarding modals).
How to Apply
Step 1 — List candidate features
Enumerate the features to evaluate: backlog items, proposed roadmap, or existing features under investment review.
Step 2 — Design the Kano survey
For each feature, ask exactly two questions:
- Functional: "How would you feel if this feature WERE present?"
- Dysfunctional: "How would you feel if this feature were NOT present?"
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.
- 4d ago First seen · 159 lines · 21 tokens per session scan A 918f88b7a698
kano-model is a skill published in the GitHub repository tupe12334/instinct (1 stars, last pushed 18d ago), licensed MIT. It adds 21 tokens to every session and 1,980 once invoked, about $0.0001 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 2026-08-31.
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