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Agent skill

Weather Data Lifecycle Management

by sickn33 in sickn33/agentic-awesome-skills

Manage ownership, retention, and cleanup of downloaded weather data across one-shot jobs, interactive viewers, caches, failures, and cancellation.

MITAuto-check passedResearch & Science

Install Weather Data Lifecycle Management

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill weather-data-lifecycle-management -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills weather-data-lifecycle-management --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/weather-data-lifecycle-management .claude/skills/weather-data-lifecycle-management && rm -rf skills-src

Use ~/.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/

Facts

Skill name
weather-data-lifecycle-management
GitHub stars
47k
Used in
1 other repo
Token cost
~1.7k tokens
SKILL.md length
820 words
Files
1
Skills in repo
1,493
Repo updated
First seen
Licence
MIT

At a glance

Manage ownership, retention, and cleanup of downloaded weather data across one-shot jobs, interactive viewers, caches, failures, and cancellation.

  • Works in 5 steps: The worker owns the request workspace… → If acquisition or display creation… → On successful display, attach one… → …
  • Tasks that involve Physical and earth sciences
  • SKILL.md covers Overview, When to Use This Skill, Classify Every Path and One-Shot Workflow, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Weather Data Lifecycle Management is an agent skill from sickn33/agentic-awesome-skills. Manage ownership, retention, and cleanup of downloaded weather data across one-shot jobs, interactive viewers, caches, failures, and cancellation.

Its SKILL.md is about 1.7k 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 Research & Science, covering Physical and earth sciences. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Physical and earth sciences

Example prompts

  • “/weather-data-lifecycle-management”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. The worker owns the request workspace during acquisition.
  2. If acquisition or display creation fails, the worker cleans it immediately.
  3. On successful display, attach one idempotent cleanup callback to the real
  4. The consumer becomes the owner; the worker must not also delete the data in
  5. If multiple consumers share the same data, release it only after the final

What it can do on your machine

Read from SKILL.md and the folder at commit 680176d. 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

Weather Data Lifecycle Management loads about 1.7k tokens when it runs. Until then it costs about 45 tokens; SKILL.md has 820 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~45
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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 sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 820 words, ~1,687 tokens.

Download SKILL.mdSave it as .claude/skills/weather-data-lifecycle-management/SKILL.md (or your agent's skills folder).
name
weather-data-lifecycle-management
description
Manage ownership, retention, and cleanup of downloaded weather data across one-shot jobs, interactive viewers, caches, failures, and cancellation.
category
data
risk
safe
source
self
source_type
self
date_added
2026-09-24
author
ShianMike
tags
weather, data-lifecycle, cleanup, caching, desktop-apps
tools
claude, cursor, gemini, codex

Weather Data Lifecycle Management

Overview

Keep downloaded and derived weather data for exactly as long as its consumer needs it. Make ownership explicit so one-shot jobs clean promptly, interactive views retain their inputs until close, and shared caches are never deleted as request cleanup.

This skill governs lifetime and ownership. It does not choose a provider, decode scientific data, or define a cache eviction policy by itself.

When to Use This Skill

  • A completed render leaves large temporary files behind.
  • An interactive view fails because its backing data was deleted too early.
  • Cancellation or exceptions leak partial downloads and derived files.
  • Request-owned files and a shared user cache are mixed together.
  • Background workers transfer ownership of data to a UI or later processing stage.

Classify Every Path

Assign each file or directory one owner and one lifetime:

ClassOwnerDelete when
Request workspaceOne operationOperation finishes or fails after durable outputs are saved
Interactive-session dataViewer or sessionFinal consumer closes or display creation fails
Shared cacheCache managerExplicit eviction policy permits it
User exportUserNever as automatic request cleanup
Partial fileActive writerFailure, cancellation, or successful atomic promotion

If ownership cannot be stated, do not delete the path. Resolve ownership first.

One-Shot Workflow

Use a request-specific temporary directory, keep all transient downloads and intermediate products inside it, and copy or atomically move only the requested artifact to its durable destination.

python
from pathlib import Path
from tempfile import TemporaryDirectory


def render_once(destination):
    destination = Path(destination)
    destination.parent.mkdir(parents=True, exist_ok=True)

    with TemporaryDirectory(prefix="weather-job-") as workspace:
        workspace = Path(workspace)
        source = fetch_into(workspace)
        temporary_output = render(source, workspace / "result.png")
        temporary_output.replace(destination)

    return destination

The final artifact must live outside the temporary directory. If replacement across filesystems is not atomic, copy to a temporary file beside the final destination, verify it, then replace locally.

Interactive Workflow

An interactive consumer outlives the worker that created its data. Transfer ownership only after the viewer or session is successfully created:

  1. The worker owns the request workspace during acquisition.
  2. If acquisition or display creation fails, the worker cleans it immediately.
  3. On successful display, attach one idempotent cleanup callback to the real consumer close or destroyed event.
  4. The consumer becomes the owner; the worker must not also delete the data in its ordinary finally path.
  5. If multiple consumers share the same data, release it only after the final lease closes.

Do not tie cleanup to a temporary dialog, local variable, or signal that can fire before the actual consumer is finished.

Failure and Cancellation Rules

  • Keep incomplete downloads under a distinct suffix or directory so they cannot be mistaken for valid data.
  • Close datasets, file handles, and memory maps before removing their paths.
  • Put request-owned cleanup in finally, but exclude resources whose ownership was successfully transferred.
  • Make cleanup idempotent; a cancellation signal and a close event may race.
  • Preserve a durable partial-result manifest when the user can retry remaining work.
  • On cleanup failure, report the exact owned path without deleting broader parent directories.
Show full SKILL.md (356 more words)Show less

Shared Cache Boundary

  • A request may read or populate a shared cache but does not own the cache root.
  • Promote validated entries atomically; never expose a partial file as a hit.
  • Use cache identity and eviction metadata rather than deleting files by age or filename guesswork inside request code.
  • Do not place user exports inside a directory that normal cache cleanup owns.
  • Test that two concurrent consumers do not remove data still leased by the other.

Verification Checklist

  • Every created path has a named owner and deletion event.
  • One-shot operations retain only requested durable outputs.
  • Interactive data remains available until the final consumer closes.
  • Display-creation failure cleans immediately.
  • Exceptions and cancellation remove partial request-owned files.
  • Cleanup is idempotent and closes open handles first.
  • Shared caches and user exports are outside request cleanup scope.
  • Tests exercise success, failure, cancellation, and close-event paths.

Security & Safety Notes

  • Resolve and verify cleanup targets before recursive deletion.
  • Never delete a home directory, workspace root, shared cache root, or user- selected directory as a computed fallback.
  • Avoid globs and unresolved environment variables for destructive cleanup.
  • Do not follow untrusted symlinks or junctions outside the owned workspace.
  • Keep sensitive temporary data in an access-controlled location and remove it when its approved lifetime ends.
  • Report material cleanup failures instead of silently leaving private data.

Common Pitfalls

  • The image renders but source data leaks: Cleanup is absent from the one-shot finally path or data was written outside the request workspace.
  • The viewer opens and then breaks: The worker deleted data immediately after emitting the viewer. Transfer ownership to the viewer close event.
  • Cancellation deletes useful completed work: Durable artifacts and request-owned intermediates share a directory. Separate them.
  • A cache disappears: Request cleanup treated a shared cache as owned data. Delegate eviction to the cache manager.
  • Windows refuses deletion: A dataset or mapped file is still open. Close every consumer before cleanup.

Limitations

  • Multi-process leases require coordination beyond an in-memory reference count.
  • Some decoders create files outside their requested directory; configure or inventory those outputs before promising complete cleanup.
  • Lifecycle correctness prevents leaks and premature deletion but does not validate the scientific contents of retained data.

© sickn33, 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 skills/weather-data-lifecycle-management of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit 680176d

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Weather Data Lifecycle Management 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.

Weather Data Lifecycle Management compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Weather Data Lifecycle Management this skillsickn33/agentic-awesome-skills47k1 repos~1.7kAutomated safety check: PassMIT
AstropyzLanqing/codex-claude-academic-skills4.7k13 repos~2.9kAutomated safety check: PassBSD-3-Clause
PymatgenzLanqing/codex-claude-academic-skills4.7k11 repos~5kAutomated safety check: PassMIT
Cantera Ignition DelayK-Dense-AI/scientific-agent-skills48k1 repos~2.2kAutomated safety check: PassMIT
Weathertrpc-group/trpc-agent-go1.9k8 repos~591Automated safety check: PassApache-2.0
Pymol VisualizationChatMol/ChatMol373—~1.2kAutomated safety check: PassMIT

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Categories

Questions about Weather Data Lifecycle Management

What does Weather Data Lifecycle Management do?

Manage ownership, retention, and cleanup of downloaded weather data across one-shot jobs, interactive viewers, caches, failures, and cancellation. Weather Data Lifecycle Management is an agent skill from sickn33/agentic-awesome-skills. Manage ownership, retention, and cleanup of downloaded weather data across one-shot jobs, interactive viewers, caches, failures, and cancellation.

When should I use Weather Data Lifecycle Management?

Weather Data Lifecycle Management fits situations like: tasks that involve Physical and earth sciences.

How do I install Weather Data Lifecycle Management in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill weather-data-lifecycle-management -a claude-code`. Or copy the skill folder (skills/weather-data-lifecycle-management in sickn33/agentic-awesome-skills) into .claude/skills/weather-data-lifecycle-management in your project. Claude Code loads it when a task matches its description.

How do I install Weather Data Lifecycle Management in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill weather-data-lifecycle-management -a codex`. Or copy the skill folder (skills/weather-data-lifecycle-management in sickn33/agentic-awesome-skills) into .agents/skills/weather-data-lifecycle-management in your project. Codex loads it when a task matches its description.

Can I use Weather Data Lifecycle Management 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 sickn33/agentic-awesome-skills --skill weather-data-lifecycle-management -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/weather-data-lifecycle-management, .gemini/skills/weather-data-lifecycle-management, .github/skills/weather-data-lifecycle-management and .opencode/skills/weather-data-lifecycle-management in your project.

What does Weather Data Lifecycle Management need to run?

SKILL.md names no scripts, command-line tools or credentials: Weather Data Lifecycle Management is instructions for the agent only. Our summary lists: Python 3.

Does Weather Data Lifecycle Management 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 Weather Data Lifecycle Management 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 Weather Data Lifecycle Management use?

Weather Data Lifecycle Management 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 Weather Data Lifecycle Management use?

About 1.7k tokens (SKILL.md is roughly 6.7k 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 Weather Data Lifecycle Management?

Skills that share tags, products or a category with Weather Data Lifecycle Management: Astropy (zLanqing/codex-claude-academic-skills, 4.7k stars), Pymatgen (zLanqing/codex-claude-academic-skills, 4.7k stars), Cantera Ignition Delay (K-Dense-AI/scientific-agent-skills, 48k stars) and Weather (trpc-group/trpc-agent-go, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Weather Data Lifecycle Management?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.