Stripe Projects
fossasia/eventyay
A skill your agent uses when the user wants to provision infrastructure or third-party services using Stripe Projects.
Retrieve numerical weather prediction data from public AWS S3 and HTTP archives using GRIB2 inventories, byte ranges, Herbie, provider fallbacks, and verified caching.
$ npx skills add sickn33/agentic-awesome-skills --skill weather-model-data-fetching -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sickn33/agentic-awesome-skills weather-model-data-fetching --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/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/weather-model-data-fetching .claude/skills/weather-model-data-fetching && 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 "weather-model-data-fetching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/weather-model-data-fetching into .claude/skills/weather-model-data-fetching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weather-model-data-fetching", 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/sickn33/agentic-awesome-skills/tree/main/skills/weather-model-data-fetchingType 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 sickn33/agentic-awesome-skills --skill weather-model-data-fetching -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sickn33/agentic-awesome-skills weather-model-data-fetching --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/weather-model-data-fetching .agents/skills/weather-model-data-fetching && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "weather-model-data-fetching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/weather-model-data-fetching into .agents/skills/weather-model-data-fetching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weather-model-data-fetching", 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 sickn33/agentic-awesome-skills --skill weather-model-data-fetching -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sickn33/agentic-awesome-skills weather-model-data-fetching --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/weather-model-data-fetching .cursor/skills/weather-model-data-fetching && 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 "weather-model-data-fetching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/weather-model-data-fetching into .cursor/skills/weather-model-data-fetching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weather-model-data-fetching", 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/sickn33/agentic-awesome-skills.git --path skills/weather-model-data-fetching--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 sickn33/agentic-awesome-skills --skill weather-model-data-fetching -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sickn33/agentic-awesome-skills weather-model-data-fetching --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/weather-model-data-fetching .gemini/skills/weather-model-data-fetching && 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 "weather-model-data-fetching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/weather-model-data-fetching into .gemini/skills/weather-model-data-fetching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weather-model-data-fetching", 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 sickn33/agentic-awesome-skills weather-model-data-fetchingInstalls 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 sickn33/agentic-awesome-skills --skill weather-model-data-fetching -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/weather-model-data-fetching .github/skills/weather-model-data-fetching && 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 "weather-model-data-fetching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/weather-model-data-fetching into .github/skills/weather-model-data-fetching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weather-model-data-fetching", 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 sickn33/agentic-awesome-skills --skill weather-model-data-fetching -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sickn33/agentic-awesome-skills weather-model-data-fetching --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/weather-model-data-fetching .opencode/skills/weather-model-data-fetching && 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 "weather-model-data-fetching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/weather-model-data-fetching into .opencode/skills/weather-model-data-fetching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weather-model-data-fetching", 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.
weather-model-data-fetchingRetrieve numerical weather prediction data from public AWS S3 and HTTP archives using GRIB2 inventories, byte ranges, Herbie, provider fallbacks, and verified caching.
Weather Model Data Fetching is an agent skill from sickn33/agentic-awesome-skills. Retrieve numerical weather prediction data from public AWS S3 and HTTP archives using GRIB2 inventories, byte ranges, Herbie, provider fallbacks, and verified caching.
Its SKILL.md is about 3k 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 Backend & APIs, covering Caching. It works with Amazon S3. 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.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
awsFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
registry.opendata.awsherbie.readthedocs.iogithub.comnomads.ncep.noaa.govdocs.aws.amazon.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Weather Model Data Fetching loads about 3k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 1,410 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.
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.
The full file from sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 1,410 words, ~2,981 tokens.
.claude/skills/weather-model-data-fetching/SKILL.md (or your agent's skills folder).Fetch numerical weather prediction data without treating a multi-gigabyte GRIB2 file as one indivisible download. Prefer an existing project adapter or Herbie; use direct object-store byte ranges only when the supported path cannot express the request.
This skill covers transport, inventory selection, caching, and verification. It does not interpret the forecast or decide whether a model is meteorologically appropriate.
Do not activate this skill for ordinary weather-forecast questions that do not require model files.
Resolve these values before downloading:
valid = initialization + lead);Confirm that the cycle is complete, the forecast hour exists for that cycle,
and the requested location is inside the model domain. A recent 404 often
means the cycle is not published yet; step back to a completed cycle instead of
retrying indefinitely.
Do not recursively list a large public bucket to discover one run. Build the documented prefix for the model, cycle, product, forecast hour, and member, then probe that exact object and its inventory.
Use an explicit provider priority and record the provider that succeeded. A fallback must refer to the same model run, product, member, and forecast hour; never silently substitute a different forecast.
GRIB2 files contain consecutive messages. A companion inventory such as
.idx, .grib2.idx, or .grb2.inv records each message's starting byte.
Range: bytes=START-END request per range. S3 does not support
multiple ranges in one GetObject request.206 Partial Content and a matching Content-Range. If a server
answers 200, do not append the whole object as though it were a fragment.ETag and/or Last-Modified) while
downloading. Discard fragments if the object changes.A GRIB message contains one field over its grid. Message-range subsetting saves variables and levels, not geography. A point request still downloads the full grid for every selected message unless the provider offers a point, regional, Zarr, or other chunked endpoint.
Use the current search argument; searchString is deprecated. Start from the
inventory, fail on an empty match, and keep the download directory explicit.
from pathlib import Path
from herbie import Herbie
PRESSURE_FIELDS = (
r":(?:HGT|TMP|RH|SPFH|UGRD|VGRD):\d+(?:\.\d+)? mb:"
)
def fetch_hrrr_pressure_run(initialization, forecast_hour, cache_dir):
cache_dir = Path(cache_dir)
h = Herbie(
initialization,
model="hrrr",
product="prs",
fxx=forecast_hour,
priority=["aws", "nomads", "google", "azure"],
save_dir=cache_dir,
verbose=False,
)
selected = h.inventory(PRESSURE_FIELDS)
if selected.empty:
raise RuntimeError("inventory matched no pressure-level fields")
downloaded = h.download(PRESSURE_FIELDS, errors="raise")
path = Path(downloaded) if downloaded is not None else None
if path is None or not path.is_file() or path.stat().st_size == 0:
raise RuntimeError("GRIB2 subset was not materialized")
return path, {
"model": h.model,
"product": h.product,
"initialization": h.date.isoformat(),
"forecast_hour": h.fxx,
"valid_time": h.valid_date.isoformat(),
"provider": h.grib_source,
"remote_object": str(h.grib),
"messages": len(selected),
}For xarray output, call h.xarray(search, ...) and handle either one
xarray.Dataset or a list of incompatible GRIB hypercubes. Merge only groups
whose coordinates and dimensions are compatible, and close every dataset when
finished.
NOAA Open Data buckets allow unsigned reads. --no-sign-request prevents the
AWS CLI from loading credentials; it does not disable TLS verification.
aws s3 ls --no-sign-request s3://noaa-hrrr-bdp-pds/hrrr.YYYYMMDD/conus/
aws s3api get-object --no-sign-request \
--bucket noaa-hrrr-bdp-pds \
--key "hrrr.YYYYMMDD/conus/hrrr.tHHz.wrfprsfFF.grib2" \
--range "bytes=START-END" fragment.grib2Use these commands to inspect a documented public object or reproduce one known range. For normal multi-message assembly, reuse Herbie or the project's tested downloader instead of scripting binary concatenation in shell.
A pressure-level file alone may not contain a usable surface row. Before building a vertical profile, require:
Some providers split pressure and surface fields into separate products. Fetch and join the companion product from the same run, or reject the request with a list of missing fields. Do not fabricate a ground row or silently reduce the profile to a short mandatory-level list.
After decoding, sort pressure monotonically, remove duplicate levels, normalize units and longitude conventions, and run the consuming project's profile QC.
408, 429, and transient 5xx responses with bounded
exponential backoff and jitter; honor Retry-After.finally after the derived artifact is durable.Measure discovery, inventory, transfer, decode, point extraction, and rendering separately. A slow end-to-end request is not evidence that GRIB decoding is the bottleneck.
--no-verify-ssl.Range.© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/weather-model-data-fetching of sickn33/agentic-awesome-skills.
Open the folder on GitHubat commit 680176d
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.
Weather Model Data Fetching 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 |
|---|---|---|---|---|---|---|
| Weather Model Data Fetching this skillsickn33/agentic-awesome-skills | 47k | 1 repos | ~3k | Automated safety check: Pass | MIT | |
| Stripe Projectsfossasia/eventyay | 1.7k | 5 repos | ~2k | Automated safety check: Notes | Apache-2.0 | |
| FoundatioFoundatioFx/Foundatio | 2.1k | — | ~3.9k | Automated safety check: Pass | Apache-2.0 | |
| Wp Block Themesgambitph/Stackable | 351 | 3 repos | ~985 | Automated safety check: Pass | GPL-3.0 | |
| Wp Performancegambitph/Stackable | 351 | 3 repos | ~1.5k | Automated safety check: Pass | GPL-3.0 | |
| Effect Portable Patternsmillionco/expect | 3.6k | — | ~3.7k | Automated safety check: Pass | Custom licence |
fossasia/eventyay
A skill your agent uses when the user wants to provision infrastructure or third-party services using Stripe Projects.
FoundatioFx/Foundatio
A skill your agent uses when working with Foundatio infrastructure abstractions for .NET -- caching, queuing, messaging, file storage, distributed locking, or background jobs.
gambitph/Stackable
A skill your agent uses when developing WordPress block themes: theme.json (global settings/styles), templates and template parts, patterns, style variations, and Site Editor troubleshooting (style…
gambitph/Stackable
A skill your agent uses when investigating or improving WordPress performance (backend-only agent): profiling and measurement (WP-CLI profile/doctor, Server-Timing, Query Monitor via REST headers)…
millionco/expect
Portable Effect patterns for robust promise execution. An agent skill from millionco/expect.
redis/fastapi-redis-sdk
Guides development on the fastapi-redis-sdk library itself - its connection lifecycle, dependency-injected caching, and async/sync bridging.
sickn33/agentic-awesome-skills
Implements an interface in one of two named color modes, iridescent white or colorful black, from a parameterized starter that reports measured color intensity.
sickn33/agentic-awesome-skills
Saves a user's project decisions, rules and preferences into a project-local mdbase so later sessions and other agents can recover the intent.
sickn33/agentic-awesome-skills
Keeps project decisions, research and verified results available across coding-agent sessions through LWC memory, a document Wiki graph and a CodeGraph code index.
sickn33/agentic-awesome-skills
Guides an agent through assessing its own owner for cofounder fit, publishing an approved profile, and ranking complementary profiles other agents published for their owners.
sickn33/agentic-awesome-skills
Integracao com WhatsApp Business Cloud API (Meta). An agent skill from sickn33/agentic-awesome-skills.
sickn33/agentic-awesome-skills
Acts as a proxy for the Cline CLI, dispatching coding tasks one at a time, monitoring runs by hard evidence, relaying decisions to you and learning per-project preferences.
Works with
Categories
Retrieve numerical weather prediction data from public AWS S3 and HTTP archives using GRIB2 inventories, byte ranges, Herbie, provider fallbacks, and verified caching. Weather Model Data Fetching is an agent skill from sickn33/agentic-awesome-skills. Retrieve numerical weather prediction data from public AWS S3 and HTTP archives using GRIB2 inventories, byte ranges, Herbie, provider fallbacks, and verified caching.
Weather Model Data Fetching fits situations like: tasks that involve Caching.
Run `npx skills add sickn33/agentic-awesome-skills --skill weather-model-data-fetching -a claude-code`. Or copy the skill folder (skills/weather-model-data-fetching in sickn33/agentic-awesome-skills) into .claude/skills/weather-model-data-fetching in your project. Claude Code loads it when a task matches its description.
Run `npx skills add sickn33/agentic-awesome-skills --skill weather-model-data-fetching -a codex`. Or copy the skill folder (skills/weather-model-data-fetching in sickn33/agentic-awesome-skills) into .agents/skills/weather-model-data-fetching in your project. Codex loads it when a task matches its description.
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-model-data-fetching -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-model-data-fetching, .gemini/skills/weather-model-data-fetching, .github/skills/weather-model-data-fetching and .opencode/skills/weather-model-data-fetching in your project.
Going by SKILL.md and its folder, Weather Model Data Fetching needs the command-line tools its instructions call (aws). Our summary lists: Python 3.
SKILL.md names 5 domains. As links in the text: registry.opendata.aws, herbie.readthedocs.io, github.com, nomads.ncep.noaa.gov and docs.aws.amazon.com. This is read from the text; nothing was executed.
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.
Weather Model Data Fetching is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Weather Model Data Fetching: Stripe Projects (fossasia/eventyay, 1.7k stars), Foundatio (FoundatioFx/Foundatio, 2.1k stars), Wp Block Themes (gambitph/Stackable, 351 stars) and Wp Performance (gambitph/Stackable, 351 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.