kano-model

kano-model is a skill for Claude Code, Codex from fzfclee/consulting-skills. It costs 48 tokens per session (753 once invoked), scanned A, original, Apache-2.0.

A method for classifying customer needs or service features by how they affect satisfaction. Categories include basic expectations, performance improvements, pleasant surprises, irrelevant features, and features that can reduce satisfaction.

In plain words
What is it for?
Use it to assess feature ideas or service attributes against customer satisfaction and dissatisfaction evidence.
Why use it?
It separates features customers simply expect from those that improve satisfaction or create differentiation. It also records that these effects can change over time.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to assess feature ideas or service attributes against customer satisfaction and dissatisfaction evidence.

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Install with agentmods
npx agentmods add skills/fzfclee/consulting-skills/kano-model
Install

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.

Any agent
npx skills add fzfclee/consulting-skills --skill kano-model
Clone the repo
git clone --depth 1 https://github.com/fzfclee/consulting-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for kano-model

README.md
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Your own site
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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.

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Your own site · 80×15
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Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 753 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00048 $0.00753
Opus 5 $0.00024 $0.00377
Sonnet 5 $0.00010 $0.00151
Haiku 4.5 $0.00005 $0.00075

Measured 10d ago against content hash b26f5e02ddfb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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 10d 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.

skills/kano-model/SKILL.md · 86 lines

How it starts

The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Kano Model

Use this skill to run Kano Model as a practical consulting method, not as a generic framework explanation.

Method Notes

  • Classify features as must-be, performance, delighter, indifferent, or reverse.
  • Kano categories shift over time; mark evidence age.

Required Inputs

Collect or infer these inputs before execution:

  • features or service attributes
  • customer segment
  • satisfaction evidence
  • dissatisfaction evidence

If an input is missing, do not block automatically. Mark it as missing, state the assumption used, and add a validation action.

When Not To Use

Do not use without a defined customer context and evidence of how feature presence and absence affect satisfaction. Kano classification alone does not set roadmap priority, cost, or sequence.

Adjacent Methods

  • jobs-to-be-done: understand the progress the customer is trying to make.
  • user-journey-mapping: locate attributes and pain points across a chronological experience.

Step-by-Step Execution

Step Required input How to execute Output
Define feature set Features/service attributes and target segment. List attributes in customer language. Kano item list.
Collect satisfaction evidence Survey/interview/VOC data for presence and absence of each item. Capture how customers react if the attribute exists or is missing. Functional/dysfunctional response data.
Classify categories Response data. Classify must-be, performance, delighter, indifferent, or reverse. Kano classification.
Interpret investment Categories, segment, maturity, cost. Decide baseline requirements, performance investments, and differentiating delighters. Feature priority implications.
Refresh assumptions Evidence age and market expectations. Mark which classifications need revalidation over time. Kano review plan.

Output Template

### 1. Scope And Evidence
Customer segment:
Decision:
Attributes tested:
Research basis:

### 2. Classification
| Attribute | Functional response | Dysfunctional response | Kano class | Confidence |
|---|---|---|---|---|
|  |  |  |  |  |

### 3. Investment Logic
| Attribute | Current performance | Priority | Reason |
|---|---|---|---|
|  |  |  |  |

### 4. Refresh Plan
| Assumption | Validation | Owner | Timing | Reclassification trigger |
|---|---|---|---|---|
|  |  |  |  |  |

### Evidence And Next Decision
- Confirmed facts:
- Assumptions:
- Missing evidence:
- Next action, owner, and timing:
- Expected signal and decision threshold:

Read the full file on GitHub · 86 lines

Changes

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.

  1. 10d ago First seen · 86 lines · 48 tokens per session scan A b26f5e02ddfb

Subscribe to this mod's changes

kano-model is a skill published in the GitHub repository fzfclee/consulting-skills (4 stars, last pushed 21d ago), licensed Apache-2.0. It adds 48 tokens to every session and 753 once invoked, about $0.0002 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.