"algo-price-conjoint"

"algo-price-conjoint" is a skill for Claude Code from charlieviettq/awesome-agent-skill. It costs 80 tokens per session (989 once invoked), scanned A, a copy of algo-price-conjoint, MIT.

A survey-based method for measuring how product features affect customer choices and willingness to pay. People compare hypothetical product versions, producing estimates of the relative value of each tested feature option.

In plain words
What is it for?
Use it to estimate the value of specific features, calculate willingness to pay, and choose a product configuration for a target customer group.
Why use it?
It helps reveal which features drive buying decisions and the trade-offs customers make between them. Results should only be used within the feature and price ranges tested in the study.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to estimate the value of specific features, calculate willingness to pay, and choose a product configuration for a target customer group.

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Install with agentmods
npx agentmods add skills/charlieviettq/awesome-agent-skill/algo-price-conjoint
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 charlieviettq/awesome-agent-skill --skill algo-price-conjoint
Clone the repo
git clone --depth 1 https://github.com/charlieviettq/awesome-agent-skill

Made for: Claude Code.

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 "algo-price-conjoint"

README.md
[![agentmods](https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-price-conjoint/github.svg)](https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-price-conjoint)
Your own site
<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-price-conjoint"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-price-conjoint/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.

agentmods 80×15 button for "algo-price-conjoint"

Your own site · 80×15
<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-price-conjoint"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-price-conjoint.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 989 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 92% copy Near-identical to another mod 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 $0.00080 $0.00989
Opus 5 $0.00040 $0.00495
Sonnet 5 $0.00016 $0.00198
Haiku 4.5 $0.00008 $0.00099

Measured 12d ago against content hash 4049c102954d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

"algo-price-conjoint" 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 12d 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.

Origin

This is a copy

92% identical to algo-price-conjoint — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.claude/skills/algo-price-conjoint/SKILL.md · 87 lines

How it starts

The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Conjoint Analysis

Overview

Conjoint analysis estimates the relative value consumers place on product attributes by analyzing their choices among hypothetical product profiles. Choice-Based Conjoint (CBC) is the most common variant. Produces part-worth utilities per attribute level and derived willingness-to-pay estimates.

When to Use

Trigger conditions:

  • Determining which features drive purchase decisions and how much they're worth
  • Estimating willingness to pay for specific product features
  • Optimizing product configuration for a target segment

When NOT to use:

  • When you only need an acceptable price range (use Van Westendorp — simpler)
  • When attributes can't be varied independently (natural constraints)

Algorithm

IRON LAW: Conjoint Results Are Valid ONLY for Tested Attribute Levels
Extrapolating beyond tested ranges is unreliable. If you tested
prices $10-$50, you cannot predict preference at $100. The utility
function is only defined within the experimental design space.

Phase 1: Input Validation

Define: attributes (3-7), levels per attribute (2-5 each), design type (full factorial if small, fractional/D-optimal if large). Survey 200+ respondents minimum. Gate: Attributes independent, levels realistic, sample size sufficient.

Phase 2: Core Algorithm

  1. Generate choice sets using experimental design (D-optimal or balanced overlap)
  2. Present respondents with sets of 3-4 product profiles, ask to choose preferred
  3. Estimate part-worth utilities using multinomial logit (MNL) or hierarchical Bayes (HB)
  4. Compute: attribute importance = range of part-worths within attribute / sum of all ranges
  5. Derive WTP: utility-to-price conversion using the price attribute coefficient

Phase 3: Verification

Check: holdout task prediction accuracy (hit rate > 60%), signs of part-worths are logical (higher price → lower utility). Gate: Holdout hit rate acceptable, utilities directionally correct.

Phase 4: Output

Return part-worth utilities, attribute importance, and WTP estimates.

Read the full file on GitHub · 87 lines

Files

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.

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. 12d ago First seen · 87 lines · 80 tokens per session scan A 4049c102954d

Subscribe to this mod's changes

"algo-price-conjoint" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 989 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to algo-price-conjoint, differing in 8 lines, and is treated as a copy.

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