Scikit Learn
zLanqing/codex-claude-academic-skills
Machine learning in Python with scikit-learn. An agent skill from zLanqing/codex-claude-academic-skills.
Define custom distance/similarity metrics for clustering and ML algorithms.
Install Custom Distance Metrics
$ npx skills add benchflow-ai/skillsbench --skill custom-distance-metrics -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench custom-distance-metrics --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/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics .claude/skills/custom-distance-metrics && 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 "custom-distance-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics into .claude/skills/custom-distance-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "custom-distance-metrics", 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/benchflow-ai/skillsbench/tree/main/tasks/mars-clouds-clustering/environment/skills/custom-distance-metricsType 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 benchflow-ai/skillsbench --skill custom-distance-metrics -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench custom-distance-metrics --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics .agents/skills/custom-distance-metrics && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "custom-distance-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics into .agents/skills/custom-distance-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "custom-distance-metrics", 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 benchflow-ai/skillsbench --skill custom-distance-metrics -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench custom-distance-metrics --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics .cursor/skills/custom-distance-metrics && 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 "custom-distance-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics into .cursor/skills/custom-distance-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "custom-distance-metrics", 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/benchflow-ai/skillsbench.git --path tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics--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 benchflow-ai/skillsbench --skill custom-distance-metrics -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench custom-distance-metrics --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics .gemini/skills/custom-distance-metrics && 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 "custom-distance-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics into .gemini/skills/custom-distance-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "custom-distance-metrics", 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 benchflow-ai/skillsbench custom-distance-metricsInstalls 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 benchflow-ai/skillsbench --skill custom-distance-metrics -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics .github/skills/custom-distance-metrics && 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 "custom-distance-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics into .github/skills/custom-distance-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "custom-distance-metrics", 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 benchflow-ai/skillsbench --skill custom-distance-metrics -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install benchflow-ai/skillsbench custom-distance-metrics --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics .opencode/skills/custom-distance-metrics && 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 "custom-distance-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics into .opencode/skills/custom-distance-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "custom-distance-metrics", 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
custom-distance-metrics- GitHub stars
- 1.8k
- Token cost
- ~674 tokens
- SKILL.md length
- 99 words
- Files
- 1
- Skills in repo
- 189
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Define custom distance/similarity metrics for clustering and ML algorithms.
- Working with DBSCAN
- SKILL.md covers Defining Custom Metrics for…, Parameterized Distance Functions, Example: Manhattan Distance… and Using scipy.spatial.distance, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Scipy distance functions with application-specific metrics
What it does
Custom Distance Metrics is an agent skill from benchflow-ai/skillsbench. Define custom distance/similarity metrics for clustering and ML algorithms. Use when working with DBSCAN, sklearn, or scipy distance functions with application-specific metrics.
Its SKILL.md is about 670 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Machine learning. It works with scikit-learn. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.
When your agent uses it
- Working with DBSCAN
- Scipy distance functions with application-specific metrics
Example prompts
- “/custom-distance-metrics”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 9a1f4dd. 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 python).
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
Custom Distance Metrics loads about 674 tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 99 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 benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 99 words, ~674 tokens.
Download SKILL.mdSave it as
.claude/skills/custom-distance-metrics/SKILL.md (or your agent's skills folder).- name
- custom-distance-metrics
- description
- Define custom distance/similarity metrics for clustering and ML algorithms. Use when working with DBSCAN, sklearn, or scipy distance functions with application-specific metrics.
Custom Distance Metrics
Custom distance metrics allow you to define application-specific notions of similarity or distance between data points.
Defining Custom Metrics for sklearn
sklearn's DBSCAN accepts a callable as the metric parameter:
from sklearn.cluster import DBSCAN
def my_distance(point_a, point_b):
"""Custom distance between two points."""
# point_a and point_b are 1D arrays
return some_calculation(point_a, point_b)
db = DBSCAN(eps=5, min_samples=3, metric=my_distance)Parameterized Distance Functions
To use a distance function with configurable parameters, use a closure or factory function:
def create_weighted_distance(weight_x, weight_y):
"""Create a distance function with specific weights."""
def distance(a, b):
dx = a[0] - b[0]
dy = a[1] - b[1]
return np.sqrt((weight_x * dx)**2 + (weight_y * dy)**2)
return distance
# Create distances with different weights
dist_equal = create_weighted_distance(1.0, 1.0)
dist_x_heavy = create_weighted_distance(2.0, 0.5)
# Use with DBSCAN
db = DBSCAN(eps=10, min_samples=3, metric=dist_x_heavy)Example: Manhattan Distance with Parameter
As an example, Manhattan distance (L1 norm) can be parameterized with a scale factor:
def create_manhattan_distance(scale=1.0):
"""
Manhattan distance with optional scaling.
Measures distance as sum of absolute differences.
This is just one example - you can design custom metrics for your specific needs.
"""
def distance(a, b):
return scale * (abs(a[0] - b[0]) + abs(a[1] - b[1]))
return distance
# Use with DBSCAN
manhattan_metric = create_manhattan_distance(scale=1.5)
db = DBSCAN(eps=10, min_samples=3, metric=manhattan_metric)Using scipy.spatial.distance
For computing distance matrices efficiently:
from scipy.spatial.distance import cdist, pdist, squareform
# Custom distance for cdist
def custom_metric(u, v):
return np.sqrt(np.sum((u - v)**2))
# Distance matrix between two sets of points
dist_matrix = cdist(points_a, points_b, metric=custom_metric)
# Pairwise distances within one set
pairwise = pdist(points, metric=custom_metric)
dist_matrix = squareform(pairwise)Performance Considerations
- Custom Python functions are slower than built-in metrics
- For large datasets, consider vectorizing operations
- Pre-compute distance matrices when doing multiple lookups
© benchflow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Custom Distance Metrics 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 |
|---|---|---|---|---|---|---|
| Custom Distance Metrics this skillbenchflow-ai/skillsbench | 1.8k | — | ~674 | Automated safety check: Pass | Apache-2.0 | |
| Scikit LearnzLanqing/codex-claude-academic-skills | 4.7k | 16 repos | ~3.9k | Automated safety check: Pass | BSD-3-Clause | |
| Senior Data ScientistRaidriar7170/hermes-skilleval | 125 | 5 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Time Series Analytics Useropen-edge-platform/edge-ai-libraries | 171 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Estimate Online Covariancemicroprediction/precise | 337 | — | ~535 | Automated safety check: Pass | MIT | |
| Precisemicroprediction/precise | 337 | — | ~782 | Automated safety check: Pass | MIT |
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Questions about Custom Distance Metrics
What does Custom Distance Metrics do?
Define custom distance/similarity metrics for clustering and ML algorithms. Custom Distance Metrics is an agent skill from benchflow-ai/skillsbench. Define custom distance/similarity metrics for clustering and ML algorithms.
When should I use Custom Distance Metrics?
Custom Distance Metrics fits situations like: working with DBSCAN; scipy distance functions with application-specific metrics.
How do I install Custom Distance Metrics in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill custom-distance-metrics -a claude-code`. Or copy the skill folder (tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics in benchflow-ai/skillsbench) into .claude/skills/custom-distance-metrics in your project. Claude Code loads it when a task matches its description.
How do I install Custom Distance Metrics in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill custom-distance-metrics -a codex`. Or copy the skill folder (tasks/mars-clouds-clustering/environment/skills/custom-distance-metrics in benchflow-ai/skillsbench) into .agents/skills/custom-distance-metrics in your project. Codex loads it when a task matches its description.
Can I use Custom Distance Metrics 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 benchflow-ai/skillsbench --skill custom-distance-metrics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/custom-distance-metrics, .gemini/skills/custom-distance-metrics, .github/skills/custom-distance-metrics and .opencode/skills/custom-distance-metrics in your project.
What does Custom Distance Metrics need to run?
SKILL.md names no scripts, command-line tools or credentials: Custom Distance Metrics is instructions for the agent only. Our summary lists: Python 3.
Does Custom Distance Metrics 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 Custom Distance Metrics 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 Custom Distance Metrics use?
Custom Distance Metrics is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Custom Distance Metrics use?
About 674 tokens (SKILL.md is roughly 2.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Custom Distance Metrics?
Skills that share tags, products or a category with Custom Distance Metrics: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Time Series Analytics User (open-edge-platform/edge-ai-libraries, 171 stars) and Estimate Online Covariance (microprediction/precise, 337 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Custom Distance Metrics?
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,835 GitHub stars. The repository holds 189 skills in this directory. The repository was last updated on July 23, 2026.
Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.