Pricing Strategy
freekmurze/dotfiles
When the user wants help with pricing decisions, packaging, or monetization strategy.
Run conjoint analysis to measure how product attributes drive consumer preferences and willingness to pay.
Install Algo Price Conjoint
$ npx skills add asgard-ai-platform/skills --skill algo-price-conjoint -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-price-conjoint --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-price-conjoint .claude/skills/algo-price-conjoint && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "algo-price-conjoint" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-price-conjoint into .claude/skills/algo-price-conjoint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-price-conjoint", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/asgard-ai-platform/skills/tree/main/algo-price-conjointType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add asgard-ai-platform/skills --skill algo-price-conjoint -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-price-conjoint --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-price-conjoint .agents/skills/algo-price-conjoint && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "algo-price-conjoint" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-price-conjoint into .agents/skills/algo-price-conjoint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-price-conjoint", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-price-conjoint -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-price-conjoint --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-price-conjoint .cursor/skills/algo-price-conjoint && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "algo-price-conjoint" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-price-conjoint into .cursor/skills/algo-price-conjoint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-price-conjoint", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/asgard-ai-platform/skills.git --path algo-price-conjoint--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add asgard-ai-platform/skills --skill algo-price-conjoint -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-price-conjoint --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-price-conjoint .gemini/skills/algo-price-conjoint && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "algo-price-conjoint" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-price-conjoint into .gemini/skills/algo-price-conjoint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-price-conjoint", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install asgard-ai-platform/skills algo-price-conjointInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add asgard-ai-platform/skills --skill algo-price-conjoint -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-price-conjoint .github/skills/algo-price-conjoint && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "algo-price-conjoint" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-price-conjoint into .github/skills/algo-price-conjoint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-price-conjoint", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-price-conjoint -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills algo-price-conjoint --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-price-conjoint .opencode/skills/algo-price-conjoint && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "algo-price-conjoint" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-price-conjoint into .opencode/skills/algo-price-conjoint/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-price-conjoint", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
algo-price-conjoint- GitHub stars
- 242
- Token cost
- ~1.1k tokens
- SKILL.md length
- 403 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Run conjoint analysis to measure how product attributes drive consumer preferences and willingness to pay.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to quantify feature value trade-offs
- SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Algo Price Conjoint is an agent skill from asgard-ai-platform/skills. Run conjoint analysis to measure how product attributes drive consumer preferences and willingness to pay. Use this skill when the user needs to quantify feature value trade-offs, estimate willingness to pay for specific features, or optimize product configuration — even if they say 'which features do customers value most', 'willingness to pay for feature X', or 'product attribute trade-offs'.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/experimental-design.md` and `references/hb-estimation.md`).
It sits in Sales & Support, covering Pricing strategy. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.
When your agent uses it
- The user needs to quantify feature value trade-offs
- Estimate willingness to pay for specific features
- Optimize product configuration — even if they say which features do customers value most
- Willingness to pay for feature X
Example prompts
- “which features do customers value most”
- “willingness to pay for feature X”
- “product attribute trade-offs”
- “/algo-price-conjoint”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Algo Price Conjoint loads about 1.1k tokens when it runs, and up to ~8.2k if it reads all its reference files. Until then it costs about 104 tokens; SKILL.md has 403 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 403 words, ~1,140 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-price-conjoint/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-price-conjoint
- description
- Run conjoint analysis to measure how product attributes drive consumer preferences and willingness to pay. Use this skill when the user needs to quantify feature value trade-offs, estimate willingness to pay for specific features, or optimize product configuration — even if they say 'which features do customers value most', 'willingness to pay for feature X', or 'product attribute trade-offs'.
- metadata.category
- WP-39 定價演算法
- metadata.tags
- pricing, conjoint-analysis, market-research, preference
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
- Generate choice sets using experimental design (D-optimal or balanced overlap)
- Present respondents with sets of 3-4 product profiles, ask to choose preferred
- Estimate part-worth utilities using multinomial logit (MNL) or hierarchical Bayes (HB)
- Compute: attribute importance = range of part-worths within attribute / sum of all ranges
- 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.
Output Format
{
"attribute_importance": [{"attribute": "price", "importance_pct": 35}, {"attribute": "brand", "importance_pct": 28}],
"part_worths": {"price": {"$10": 2.1, "$30": 0.5, "$50": -1.8}},
"wtp": {"feature_x": 12.50, "brand_premium": 8.00},
"metadata": {"respondents": 300, "model": "hierarchical_bayes", "holdout_hit_rate": 0.72}
}Examples
Show full SKILL.md (169 more words)Show less
Sample I/O
Input: Laptop with attributes: Brand(Apple/Dell/Lenovo), RAM(8/16/32GB), Price($800/$1200/$1600) Expected: Apple has highest brand utility, 32GB RAM preferred, price negative utility. WTP for Apple brand premium ≈ $200.
Edge Cases
| Input | Expected | Why |
|---|---|---|
| All attributes equally important | No clear driver | Product is commodity-like |
| Price dominates (>60%) | Highly price-sensitive market | Features don't differentiate enough |
| One level never chosen | Extreme negative utility | That level is a deal-breaker |
Gotchas
- Hypothetical bias: Respondents making hypothetical choices may not reflect real purchase behavior. Incentive-compatible designs (real choices) are better but expensive.
- Number of attributes: More than 6-7 attributes overwhelms respondents, leading to simplification strategies (ignore some attributes). Keep designs manageable.
- Interaction effects: Standard analysis assumes attributes are independent. If brand affects price sensitivity (brand×price interaction), you need interaction terms.
- Segment heterogeneity: Average part-worths mask segments with opposite preferences. Use latent class or HB models to uncover segments.
- Design efficiency: Poor experimental designs (unbalanced, correlated attributes) produce imprecise estimates. Use proper design software.
References
- For experimental design generation, see
references/experimental-design.md - For hierarchical Bayes estimation, see
references/hb-estimation.md
© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in algo-price-conjoint of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/experimental-design.md
- references/hb-estimation.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Price Conjoint next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Algo Price Conjoint this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Pricing Strategyfreekmurze/dotfiles | 1k | 18 repos | ~1.6k | Automated safety check: Pass | None | |
| Profit Margin Calculator Amazonnexscope-ai/eCommerce-Skills | 1.1k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Niche Opportunity Finderzanecole10/software-tailor-skills | 106 | — | ~4.2k | Automated safety check: Pass | None | |
| Pricing Strategyalirezarezvani/claude-skills | 28k | 1 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Software Pricing Calculatorzanecole10/software-tailor-skills | 106 | — | ~3.7k | Automated safety check: Pass | None |
Similar skills
Profit Margin Calculator Amazon
nexscope-ai/eCommerce-Skills
Amazon profit margin calculator for sellers. An agent skill from nexscope-ai/eCommerce-Skills.
Niche Opportunity Finder
zanecole10/software-tailor-skills
Discover untapped B2B software opportunities by analyzing specific industries for boring business problems, pain points, willingness to pay, competition levels, and where to find these businesses.
Pricing Strategy
alirezarezvani/claude-skills
Design, optimize, and communicate SaaS pricing — tier structure, value metrics, pricing pages, and price increase strategy.
Software Pricing Calculator
zanecole10/software-tailor-skills
Calculate the right price for custom software projects ($8K-$50K+) based on complexity, value delivered, and client ROI.
Pricing And Wtp
MaxKmet/idea-validation-agents
Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and marketinsights monetization signals.
More from asgard-ai-platform/skills
All 207 skills in this repoAlgo Ecom Bm25
asgard-ai-platform/skills
Implement BM25 ranking function for e-commerce product search relevance scoring.
Algo Mfg Cpk
asgard-ai-platform/skills
Calculate Cpk process capability index to assess whether a process meets specification requirements.
Algo Price Elasticity
asgard-ai-platform/skills
Calculate price elasticity of demand to quantify how price changes affect sales volume.
Algo Rank Bayesian
asgard-ai-platform/skills
Apply Bayesian averaging to rank items by combining observed ratings with prior expectations.
Algo Rank Elo
asgard-ai-platform/skills
Implement Elo rating system to rank items or players from pairwise comparison outcomes.
Algo Rank Wilson
asgard-ai-platform/skills
Calculate Wilson Score confidence intervals for ranking items by positive proportion with sample size correction.
Related
Categories
Questions about Algo Price Conjoint
What does Algo Price Conjoint do?
Run conjoint analysis to measure how product attributes drive consumer preferences and willingness to pay. Algo Price Conjoint is an agent skill from asgard-ai-platform/skills. Run conjoint analysis to measure how product attributes drive consumer preferences and willingness to pay.
When should I use Algo Price Conjoint?
Algo Price Conjoint fits situations like: the user needs to quantify feature value trade-offs; estimate willingness to pay for specific features; optimize product configuration — even if they say which features do customers value most; willingness to pay for feature X.
How do I install Algo Price Conjoint in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-price-conjoint -a claude-code`. Or copy the skill folder (algo-price-conjoint in asgard-ai-platform/skills) into .claude/skills/algo-price-conjoint in your project. Claude Code loads it when a task matches its description.
How do I install Algo Price Conjoint in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-price-conjoint -a codex`. Or copy the skill folder (algo-price-conjoint in asgard-ai-platform/skills) into .agents/skills/algo-price-conjoint in your project. Codex loads it when a task matches its description.
Can I use Algo Price Conjoint in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill algo-price-conjoint -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-price-conjoint, .gemini/skills/algo-price-conjoint, .github/skills/algo-price-conjoint and .opencode/skills/algo-price-conjoint in your project.
What does Algo Price Conjoint need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Price Conjoint is instructions for the agent only.
Does Algo Price Conjoint access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Algo Price Conjoint safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Algo Price Conjoint use?
Algo Price Conjoint is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Algo Price Conjoint use?
About 1.1k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.1k tokens, read only when the agent opens those files.
What are the alternatives to Algo Price Conjoint?
Skills that share tags, products or a category with Algo Price Conjoint: Pricing Strategy (freekmurze/dotfiles, 1k stars), Profit Margin Calculator Amazon (nexscope-ai/eCommerce-Skills, 1.1k stars), Niche Opportunity Finder (zanecole10/software-tailor-skills, 106 stars) and Pricing Strategy (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Price Conjoint?
asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.
Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.