Weather
openclaw/openclaw
Current weather and forecasts with webfetch, falling back to wttr.in curl for locations, rain, temperature, travel planning.
Interpret weather radar and satellite observations by validating product metadata and geometry, deriving storm and cloud structures, tracking evolution, and quantifying uncertainty.
$ npx skills add sickn33/agentic-awesome-skills --skill radar-satellite-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sickn33/agentic-awesome-skills radar-satellite-analysis --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/radar-satellite-analysis .claude/skills/radar-satellite-analysis && 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 "radar-satellite-analysis" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/radar-satellite-analysis into .claude/skills/radar-satellite-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-satellite-analysis", 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/radar-satellite-analysisType 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 radar-satellite-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sickn33/agentic-awesome-skills radar-satellite-analysis --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/radar-satellite-analysis .agents/skills/radar-satellite-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "radar-satellite-analysis" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/radar-satellite-analysis into .agents/skills/radar-satellite-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-satellite-analysis", 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 radar-satellite-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sickn33/agentic-awesome-skills radar-satellite-analysis --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/radar-satellite-analysis .cursor/skills/radar-satellite-analysis && 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 "radar-satellite-analysis" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/radar-satellite-analysis into .cursor/skills/radar-satellite-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-satellite-analysis", 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/radar-satellite-analysis--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 radar-satellite-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sickn33/agentic-awesome-skills radar-satellite-analysis --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/radar-satellite-analysis .gemini/skills/radar-satellite-analysis && 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 "radar-satellite-analysis" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/radar-satellite-analysis into .gemini/skills/radar-satellite-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-satellite-analysis", 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 radar-satellite-analysisInstalls 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 radar-satellite-analysis -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/radar-satellite-analysis .github/skills/radar-satellite-analysis && 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 "radar-satellite-analysis" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/radar-satellite-analysis into .github/skills/radar-satellite-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-satellite-analysis", 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 radar-satellite-analysis -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 radar-satellite-analysis --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/radar-satellite-analysis .opencode/skills/radar-satellite-analysis && 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 "radar-satellite-analysis" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/radar-satellite-analysis into .opencode/skills/radar-satellite-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-satellite-analysis", 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.
radar-satellite-analysisInterpret weather radar and satellite observations by validating product metadata and geometry, deriving storm and cloud structures, tracking evolution, and quantifying uncertainty.
Radar Satellite Analysis is an agent skill from sickn33/agentic-awesome-skills. Interpret weather radar and satellite observations by validating product metadata and geometry, deriving storm and cloud structures, tracking evolution, and quantifying uncertainty.
Its SKILL.md is about 3.9k 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: 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.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit b84d35a. 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.
No scripts in the folder and no shell commands in SKILL.md.
From 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.awsroc.noaa.govospo.noaa.govFrom 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.
Radar Satellite Analysis loads about 3.9k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 2,001 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 b84d35a, republished under its MIT licence (© sickn33). 2,001 words, ~3,887 tokens.
.claude/skills/radar-satellite-analysis/SKILL.md (or your agent's skills folder).Analyze radar and satellite weather products as remote-sensing observations, not as ground truth. Validate scan time, geometry, calibration, quality flags, and coverage before interpreting a feature. Then separate what the instrument directly measures from what a meteorological inference suggests.
This skill consumes already retrieved and decoded products. Use it after
noaa-radar-satellite-fetching or an equivalent source-specific skill. It does
not discover buckets, download files, repair outages, or make a product
scientifically suitable by naming it radar or satellite data.
Do not infer a surface hail size, wind speed, rainfall rate, or lightning count from a single proxy without stating the retrieval assumptions. Do not use a single still image to claim a storm's future track or intensity.
Before interpreting pixels, beams, or profiles, record:
Keep observation time, scan start/end time, file creation time, retrieval time, and analysis time separate. A file created quickly after a scan is not a newer measurement.
x and y as latitude and
longitude.Use reflectivity to describe the location and organization of precipitation echoes, then qualify the interpretation with beam height, range, attenuation, and sampling. Look for features such as:
Do not call a reflectivity threshold hail, tornado, or destructive wind. Those are conditional interpretations requiring velocity, cloud-top, lightning, surface, or model context. Convert reflectivity to rain rate only with an explicit relation, sample assumptions, and uncertainty bounds; a reflectivity lookup is not a universal precipitation truth.
Analyze radial velocity as a radar-relative measurement. State whether the field is gate-to-gate shear, environmental shear, storm-relative radial flow, or a derived couplet. Consider dealiasing, range folding, noise, side lobes, velocity folding artifacts, and the fact that a velocity signature can have multiple meteorological explanations.
For rotation or mesocyclone interpretation, use the appropriate temporal, azimuthal, and vertical support and compare the result with neighboring scans. A single couplet is a candidate feature, not a confirmed tornado. Do not use radial velocity as a direct measurement of the environmental wind vector without accounting for viewing geometry.
When a volume supports it, align elevation angles and analyze vertical structure: low-level inflow, mid-level rotation, updraft organization, echo top, overhang, and bounded weak-echo regions. Account for increasing beam volume and decreasing resolution with height. A feature that appears vertically stacked may be a sampling or attenuation artifact; check adjacent sites or scans before asserting a continuous structure.
Use satellite data to describe cloud-top and environmental context, not to replace radar precipitation structure. Depending on the product, examine:
An infrared cold cloud top is not, by itself, proof of severe weather, hail, or a particular updraft strength. A very cold top can reflect a high cloud whose emission and viewing geometry differ from a nearby lower cloud. State the proxy, the threshold, and the alternative explanation.
For a time sequence, use a consistent feature-identification and tracking method. Track cell or cloud-system centers, echoes or anvils, growth and decay, merges and splits, and changes in shape or intensity proxy. Distinguish motion from the steering flow and from apparent motion caused by parallax, changing scan geometry, or inconsistent feature definitions.
Estimate motion over multiple scans and report the method, time interval, position uncertainty, and whether the feature was occluded or lost. A single displacement between scans is not a reliable nowcast. If a forecast window is short, say so and show the persistence or extrapolation assumption.
Align the products in time and space before joining them. Radar can provide precipitation structure, radial motion, and vertical echoes; satellite can provide broad cloud shield, cloud-top context, anvils, and environmental patterns. Their footprints, resolution, viewing geometry, and quality are not identical.
Use a joint interpretation only when the evidence is complementary. For example, a radar core beneath a rapidly developing cold cloud top can support a convective-growth diagnosis, but it still does not establish surface hail or a confirmed tornado. Preserve each source's quality mask and contribution in the final result instead of collapsing them into an opaque score.
For multi-radar mosaics, resolve overlapping beams using a documented rule. Never average dBZ values or velocity fields across sites without a physical and quality-aware rule. A mosaic should show coverage and the selected source for each pixel or cell.
Remote-sensing interpretation has several uncertainty sources: beam geometry, attenuation, parallax, threshold choice, temporal resolution, feature tracking, instrument calibration, cloud microphysics, and incomplete coverage. When the task is operational or safety-relevant, report the strongest alternative interpretation and the observations that would distinguish it.
Use a bounded nowcast horizon and show how the result changes with alternate motion, persistence, growth, or decay assumptions. Do not turn a qualitative feature label into a precise probability without a calibration method. If the images are too sparse, ambiguous, or poorly geolocated, report that the feature is unconfirmed rather than filling the gap with narrative certainty.
When asked whether a storm intensified over an hour, align successive radar volumes and satellite scans, verify time and geolocation, and track one consistent feature definition. Report measured reflectivity and cloud-top changes separately from inferred intensification, include data gaps and uncertainty, and do not infer surface hail from a cold cloud top alone.
For a suspected velocity couplet, inspect neighboring scans and available elevation angles, confirm radial-velocity convention and dealiasing quality, and call it a possible rotation signature unless the evidence supports a stronger conclusion.
Return a traceable analysis with:
Avoid a report that says only "severe storm," "heavy rain," or "rapidly developing convection" without a location, time, measured proxy, and evidence beneath the label.
© 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/radar-satellite-analysis of sickn33/agentic-awesome-skills.
Open the folder on GitHubat commit b84d35a
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.
Radar Satellite Analysis 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 |
|---|---|---|---|---|---|---|
| Radar Satellite Analysis this skillsickn33/agentic-awesome-skills | 47k | 1 repos | ~3.9k | Automated safety check: Pass | MIT | |
| Weatheropenclaw/openclaw | 392k | — | ~726 | Automated safety check: Pass | MIT | |
| Langsmith ObservabilityOrchestra-Research/AI-Research-SKILLs | 13k | 2 repos | ~2.4k | Automated safety check: Pass | MIT | |
| ObservabilityBuilderIO/agent-native | 7.1k | — | ~7.3k | Automated safety check: Pass | None | |
| Form Validationthedaviddias/Front-End-Checklist | 74k | — | ~633 | Automated safety check: Pass | MIT | |
| Python Observabilitywshobson/agents | 40k | — | ~1.8k | Automated safety check: Pass | MIT |
openclaw/openclaw
Current weather and forecasts with webfetch, falling back to wttr.in curl for locations, rain, temperature, travel planning.
Orchestra-Research/AI-Research-SKILLs
LLM observability platform for tracing, evaluation, and monitoring.
BuilderIO/agent-native
Agent observability, evals, feedback, and experiments. An agent skill from BuilderIO/agent-native.
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing templates, rendered HTML, or shared components related to Validate forms accessibly.
wshobson/agents
Python observability patterns including structured logging, metrics, and distributed tracing.
alirezarezvani/claude-skills
Design production-ready observability strategies combining metrics, logs, and traces.
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.
Interpret weather radar and satellite observations by validating product metadata and geometry, deriving storm and cloud structures, tracking evolution, and quantifying uncertainty. Radar Satellite Analysis is an agent skill from sickn33/agentic-awesome-skills. Interpret weather radar and satellite observations by validating product metadata and geometry, deriving storm and cloud structures, tracking evolution, and quantifying uncertainty.
Run `npx skills add sickn33/agentic-awesome-skills --skill radar-satellite-analysis -a claude-code`. Or copy the skill folder (skills/radar-satellite-analysis in sickn33/agentic-awesome-skills) into .claude/skills/radar-satellite-analysis in your project. Claude Code loads it when a task matches its description.
Run `npx skills add sickn33/agentic-awesome-skills --skill radar-satellite-analysis -a codex`. Or copy the skill folder (skills/radar-satellite-analysis in sickn33/agentic-awesome-skills) into .agents/skills/radar-satellite-analysis 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 radar-satellite-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/radar-satellite-analysis, .gemini/skills/radar-satellite-analysis, .github/skills/radar-satellite-analysis and .opencode/skills/radar-satellite-analysis in your project.
SKILL.md names no scripts, command-line tools or credentials: Radar Satellite Analysis is instructions for the agent only.
SKILL.md names 3 domains. As links in the text: registry.opendata.aws, roc.noaa.gov and ospo.noaa.gov. 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.
Radar Satellite Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.9k tokens (SKILL.md is roughly 16k 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 Radar Satellite Analysis: Weather (openclaw/openclaw, 392k stars), Langsmith Observability (Orchestra-Research/AI-Research-SKILLs, 13k stars), Observability (BuilderIO/agent-native, 7.1k stars) and Form Validation (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.
sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 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.