Performance Attribution
HKUDS/Vibe-Trading
Explains why a portfolio beat or lagged its benchmark with Brinson sector attribution, factor alpha and beta decomposition, timing evaluation and benchmark comparison.
Classify environmental and meteorological variables into driver categories for attribution analysis.
MITAuto-check passed
Install Meteorology Driver Classification
$ npx skills add benchflow-ai/skillsbench --skill meteorology-driver-classification -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench meteorology-driver-classification --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/lake-warming-attribution/environment/skills/meteorology-driver-classification .claude/skills/meteorology-driver-classification && 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 "meteorology-driver-classification" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/meteorology-driver-classification into .claude/skills/meteorology-driver-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meteorology-driver-classification", 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/lake-warming-attribution/environment/skills/meteorology-driver-classificationType 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 meteorology-driver-classification -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench meteorology-driver-classification --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/lake-warming-attribution/environment/skills/meteorology-driver-classification .agents/skills/meteorology-driver-classification && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "meteorology-driver-classification" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/meteorology-driver-classification into .agents/skills/meteorology-driver-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meteorology-driver-classification", 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 meteorology-driver-classification -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench meteorology-driver-classification --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/lake-warming-attribution/environment/skills/meteorology-driver-classification .cursor/skills/meteorology-driver-classification && 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 "meteorology-driver-classification" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/meteorology-driver-classification into .cursor/skills/meteorology-driver-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meteorology-driver-classification", 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/lake-warming-attribution/environment/skills/meteorology-driver-classification--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 meteorology-driver-classification -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench meteorology-driver-classification --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/lake-warming-attribution/environment/skills/meteorology-driver-classification .gemini/skills/meteorology-driver-classification && 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 "meteorology-driver-classification" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/meteorology-driver-classification into .gemini/skills/meteorology-driver-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meteorology-driver-classification", 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 meteorology-driver-classificationInstalls 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 meteorology-driver-classification -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/lake-warming-attribution/environment/skills/meteorology-driver-classification .github/skills/meteorology-driver-classification && 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 "meteorology-driver-classification" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/meteorology-driver-classification into .github/skills/meteorology-driver-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meteorology-driver-classification", 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 meteorology-driver-classification -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 meteorology-driver-classification --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/lake-warming-attribution/environment/skills/meteorology-driver-classification .opencode/skills/meteorology-driver-classification && 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 "meteorology-driver-classification" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/meteorology-driver-classification into .opencode/skills/meteorology-driver-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meteorology-driver-classification", 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
meteorology-driver-classification- GitHub stars
- 1.8k
- Token cost
- ~487 tokens
- SKILL.md length
- 192 words
- Files
- 1
- Skills in repo
- 189
- Repo updated
- First seen
- Licence
- MIT
At a glance
Classify environmental and meteorological variables into driver categories for attribution analysis.
- Works in 4 steps: Identify all available variables in your… → Assign each variable to a category based… → Create derived variables if needed → …
- You need to group multiple variables into meaningful factor categories
- SKILL.md covers Overview, Common Driver Categories, Derived Variables and Grouping Strategy, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Meteorology Driver Classification is an agent skill from benchflow-ai/skillsbench. Classify environmental and meteorological variables into driver categories for attribution analysis. Use when you need to group multiple variables into meaningful factor categories.
Its SKILL.md is about 490 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is MIT.
When your agent uses it
- You need to group multiple variables into meaningful factor categories
Example prompts
- “/meteorology-driver-classification”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Identify all available variables in your dataset
- Assign each variable to a category based on physical meaning
- Create derived variables if needed
- Variables in the same category should be correlated
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
Meteorology Driver Classification loads about 487 tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 192 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 MIT licence (© benchflow-ai). 192 words, ~487 tokens.
Download SKILL.mdSave it as
.claude/skills/meteorology-driver-classification/SKILL.md (or your agent's skills folder).- name
- meteorology-driver-classification
- description
- Classify environmental and meteorological variables into driver categories for attribution analysis. Use when you need to group multiple variables into meaningful factor categories.
- license
- MIT
Driver Classification Guide
Overview
When analyzing what drives changes in an environmental system, it is useful to group individual variables into broader categories based on their physical meaning.
Common Driver Categories
Heat
Variables related to thermal energy and radiation:
- Air temperature
- Shortwave radiation
- Longwave radiation
- Net radiation (shortwave + longwave)
- Surface temperature
- Humidity
- Cloud cover
Flow
Variables related to water movement:
- Precipitation
- Inflow
- Outflow
- Streamflow
- Evaporation
- Runoff
- Groundwater flux
Wind
Variables related to atmospheric circulation:
- Wind speed
- Wind direction
- Gust speed
- Atmospheric pressure
Human
Variables related to anthropogenic activities:
- Developed area
- Agriculture area
- Impervious surface
- Population density
- Industrial output
- Land use change rate
Derived Variables
Sometimes raw variables need to be combined before analysis:
# Combine radiation components into net radiation
df['NetRadiation'] = df['Longwave'] + df['Shortwave']Grouping Strategy
- Identify all available variables in your dataset
- Assign each variable to a category based on physical meaning
- Create derived variables if needed
- Variables in the same category should be correlated
Validation
After statistical grouping, verify that:
- Variables load on expected components
- Groupings make physical sense
- Categories are mutually exclusive
Best Practices
- Use domain knowledge to define categories
- Combine related sub-variables before analysis
- Keep number of categories manageable (3-5 typically)
- Document your classification decisions
© benchflow-ai, MIT. 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/lake-warming-attribution/environment/skills/meteorology-driver-classification of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Meteorology Driver Classification 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 |
|---|---|---|---|---|---|---|
| Meteorology Driver Classification this skillbenchflow-ai/skillsbench | 1.8k | — | ~487 | Automated safety check: Pass | MIT | |
| Performance AttributionHKUDS/Vibe-Trading | 35k | — | ~3.1k | Automated safety check: Pass | MIT | |
| Changelog PR Classifierwarpdotdev/warp | 65k | 1 repos | ~1.1k | Automated safety check: Pass | AGPL-3.0 | |
| Direction Attributethedaviddias/Front-End-Checklist | 74k | — | ~534 | Automated safety check: Pass | MIT | |
| Fetchpriority Attributethedaviddias/Front-End-Checklist | 74k | — | ~535 | Automated safety check: Pass | MIT | |
| Lang Attributethedaviddias/Front-End-Checklist | 74k | — | ~995 | Automated safety check: Pass | MIT |
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Changelog PR Classifier
warpdotdev/warp
Rules for deciding whether a pull request without an explicit changelog marker belongs in the release changelog, and under which category.
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A skill your agent uses when reviewing templates, rendered HTML, or shared components related to Set text direction for RTL languages.
Fetchpriority Attribute
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Lang Attribute
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A skill your agent uses when applies to all HTML documents. An agent skill from thedaviddias/Front-End-Checklist.
Cv Classification
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Best practices for image classification tasks. An agent skill from aiming-lab/AutoResearchClaw.
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Questions about Meteorology Driver Classification
What does Meteorology Driver Classification do?
Classify environmental and meteorological variables into driver categories for attribution analysis. Meteorology Driver Classification is an agent skill from benchflow-ai/skillsbench. Classify environmental and meteorological variables into driver categories for attribution analysis.
When should I use Meteorology Driver Classification?
Meteorology Driver Classification fits situations like: you need to group multiple variables into meaningful factor categories.
How do I install Meteorology Driver Classification in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill meteorology-driver-classification -a claude-code`. Or copy the skill folder (tasks/lake-warming-attribution/environment/skills/meteorology-driver-classification in benchflow-ai/skillsbench) into .claude/skills/meteorology-driver-classification in your project. Claude Code loads it when a task matches its description.
How do I install Meteorology Driver Classification in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill meteorology-driver-classification -a codex`. Or copy the skill folder (tasks/lake-warming-attribution/environment/skills/meteorology-driver-classification in benchflow-ai/skillsbench) into .agents/skills/meteorology-driver-classification in your project. Codex loads it when a task matches its description.
Can I use Meteorology Driver Classification 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 meteorology-driver-classification -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/meteorology-driver-classification, .gemini/skills/meteorology-driver-classification, .github/skills/meteorology-driver-classification and .opencode/skills/meteorology-driver-classification in your project.
What does Meteorology Driver Classification need to run?
SKILL.md names no scripts, command-line tools or credentials: Meteorology Driver Classification is instructions for the agent only. Our summary lists: Python 3.
Does Meteorology Driver Classification 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 Meteorology Driver Classification 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 Meteorology Driver Classification use?
Meteorology Driver Classification is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Meteorology Driver Classification use?
About 487 tokens (SKILL.md is roughly 1.9k 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 Meteorology Driver Classification?
Skills that share tags, products or a category with Meteorology Driver Classification: Performance Attribution (HKUDS/Vibe-Trading, 35k stars), Changelog PR Classifier (warpdotdev/warp, 65k stars), Direction Attribute (thedaviddias/Front-End-Checklist, 74k stars) and Fetchpriority Attribute (thedaviddias/Front-End-Checklist, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Meteorology Driver Classification?
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,834 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.