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Agent skill

Weather Pipeline Performance Diagnosis

by sickn33 in sickn33/agentic-awesome-skills

Diagnose slow weather-data workflows by measuring discovery, transfer, parsing, scientific processing, and rendering separately before changing code.

MITAuto-check passedResearch & Science

Install Weather Pipeline Performance Diagnosis

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill weather-pipeline-performance-diagnosis -a claude-code

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills weather-pipeline-performance-diagnosis --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-pipeline-performance-diagnosis .claude/skills/weather-pipeline-performance-diagnosis && 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-pipeline-performance-diagnosis
GitHub stars
47k
Used in
1 other repo
Token cost
~1.8k tokens
SKILL.md length
798 words
Files
1
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

Diagnose slow weather-data workflows by measuring discovery, transfer, parsing, scientific processing, and rendering separately before changing code.

  • Works in 8 steps: request validation and source discovery; → availability checks and fallback… → network transfer or cache read; → …
  • Tasks that involve Physical and earth sciences
  • SKILL.md covers Overview, When to Use This Skill, Reproduce the Real Path and Divide the Pipeline into Stages, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Weather Pipeline Performance Diagnosis is an agent skill from sickn33/agentic-awesome-skills. Diagnose slow weather-data workflows by measuring discovery, transfer, parsing, scientific processing, and rendering separately before changing code.

Its SKILL.md is about 1.8k 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-pipeline-performance-diagnosis”

Requirements

  • Python 3

Workflow steps

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

  1. request validation and source discovery;
  2. availability checks and fallback selection;
  3. network transfer or cache read;
  4. parsing or decoding;
  5. supplemental-data retrieval;
  6. profile, grid, or derived-value construction;
  7. visualization or export;
  8. cleanup and final publication.

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. 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 Pipeline Performance Diagnosis loads about 1.8k tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 798 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~47
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

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 b84d35a, republished under its MIT licence (© sickn33). 798 words, ~1,763 tokens.

Download SKILL.mdSave it as .claude/skills/weather-pipeline-performance-diagnosis/SKILL.md (or your agent's skills folder).
name
weather-pipeline-performance-diagnosis
description
Diagnose slow weather-data workflows by measuring discovery, transfer, parsing, scientific processing, and rendering separately before changing code.
category
data
risk
safe
source
self
source_type
self
date_added
2026-09-24
author
ShianMike
tags
weather, performance, profiling, diagnostics, pipelines
tools
claude, cursor, gemini, codex

Weather Pipeline Performance Diagnosis

Overview

Find the stage that actually makes a weather workflow slow before optimizing it. Measure the real runtime path from source discovery through the final artifact, including optional companion data and fallback work.

This skill diagnoses latency and throughput. It does not prescribe a particular data provider, file format, decoder, or optimization.

When to Use This Skill

  • A fetch, sounding, map, animation, or batch job feels slower than before.
  • A parser or native backend is blamed without stage-level timing evidence.
  • Local runs and packaged or hosted runs have different performance.
  • A cache, provider fallback, optional enrichment, or rendering step may hide the real cost.
  • A proposed performance fix needs a repeatable before-and-after comparison.

Do not activate this skill for a correctness bug unless performance is also part of the observed failure.

Reproduce the Real Path

Record one fixed workload before editing code:

  • requested source, time, location, variables, and output;
  • application commit and runtime version;
  • active implementation or backend, including fallback reason;
  • cold-cache or warm-cache state;
  • machine, operating system, worker count, and relevant resource limits;
  • bytes transferred and final artifact size;
  • whether optional guidance, overlays, or secondary sources were enabled.

Use the same workload for the baseline and candidate measurement. A faster run with fewer inputs or a warm cache is not evidence that the original path improved.

Divide the Pipeline into Stages

At minimum, time these boundaries independently:

  1. request validation and source discovery;
  2. availability checks and fallback selection;
  3. network transfer or cache read;
  4. parsing or decoding;
  5. supplemental-data retrieval;
  6. profile, grid, or derived-value construction;
  7. visualization or export;
  8. cleanup and final publication.

Add sub-stages only where the first pass shows meaningful time. Preserve the existing behavior while instrumenting; diagnostics must not silently disable expensive work.

Lightweight Timing Example

Use a monotonic clock and emit structured records that can be compared across runs:

python
import json
import time
from contextlib import contextmanager


@contextmanager
def timed_stage(name, report):
    started = time.perf_counter()
    outcome = "ok"
    try:
        yield
    except BaseException:
        outcome = "error"
        raise
    finally:
        report.append({
            "stage": name,
            "outcome": outcome,
            "seconds": round(time.perf_counter() - started, 6),
        })


timings = []
with timed_stage("source_discovery", timings):
    source = discover_source()
with timed_stage("transfer", timings):
    local_path = fetch_source(source)
with timed_stage("processing", timings):
    result = build_result(local_path)

print(json.dumps(timings, sort_keys=True))

Instrument production boundaries or the same public APIs used by production. Avoid a benchmark helper that bypasses the path users report as slow.

Interpret the Evidence

  • Long discovery with little transfer suggests broad listings, excessive retries, provider timeouts, or repeated availability probes.
  • Long transfer with expected parsing time suggests bandwidth, object size, throttling, or failure to reuse a valid cache.
  • Long parsing requires proof that the intended backend is active and that the input volume is comparable.
  • Long processing after parsing points to interpolation, secondary retrieval, profile construction, or repeated computation.
  • Long rendering can come from layout, rasterization, font loading, excessive redraws, or large output dimensions.
  • High variance across identical runs suggests external services, contention, cold starts, garbage collection, or uncontrolled parallelism.

Measure wall time, CPU time, bytes, item counts, cache state, and worker count where they explain the result. A single total duration cannot locate a bottleneck.

Show full SKILL.md (338 more words)Show less

Validate a Fix

  1. Preserve the baseline report and environment description.
  2. Change the smallest shared cause supported by the measurements.
  3. Rerun the identical workload several times in the same cache state.
  4. Compare the affected stage, total duration, output identity, and resource use.
  5. Run correctness tests for the changed path.
  6. Report both improvement and measurement variability.

Do not call a slowdown fixed when only a suspected backend, log message, or microbenchmark changed. Require an end-to-end result from the reported path.

Verification Checklist

  • The measured workload matches the user's slow workflow.
  • Active backend and fallback state are observed, not inferred.
  • Network, parsing, processing, rendering, and cleanup are separate timings.
  • Cold and warm cache results are labeled.
  • Optional or supplemental work remains visible.
  • Baseline and candidate use equivalent inputs, outputs, and worker settings.
  • The fix has correctness checks plus repeatable before-and-after evidence.

Security & Safety Notes

  • Remove credentials, signed URLs, private paths, and sensitive coordinates from timing reports before sharing them.
  • Bound benchmark repetitions, downloads, concurrency, and disk usage.
  • Do not disable certificate verification or safety checks to improve timing.
  • Avoid profiling production services in a way that increases load without authorization.
  • Keep diagnostic logs from capturing raw private datasets unnecessarily.

Common Pitfalls

  • The decoder is blamed first: Transfer or supplemental data dominates. Measure each boundary before changing the decoder.
  • The candidate looks faster: It used a warm cache or smaller request. Restore equivalent conditions.
  • A unit benchmark passes: The real application takes a fallback or render path the benchmark omits. Measure the application entry point.
  • A timing limit is raised: No stage-level regression analysis was done. Inspect evidence and rerun before changing a budget.
  • Parallelism increases latency: Workers contend for network, memory, or decoder resources. Measure throughput and resource saturation together.

Limitations

  • External-service latency and hosted-runner capacity can remain variable even with correct instrumentation.
  • Instrumentation has overhead; keep it lightweight and measure coarse stages before adding fine-grained probes.
  • This skill identifies bottlenecks but does not determine whether an expensive scientific operation is necessary or meteorologically appropriate.

© 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-pipeline-performance-diagnosis of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit b84d35a

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 Pipeline Performance Diagnosis 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 Pipeline Performance Diagnosis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Weather Pipeline Performance Diagnosis this skillsickn33/agentic-awesome-skills47k1 repos~1.8kAutomated 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 Pipeline Performance Diagnosis

What does Weather Pipeline Performance Diagnosis do?

Diagnose slow weather-data workflows by measuring discovery, transfer, parsing, scientific processing, and rendering separately before changing code. Weather Pipeline Performance Diagnosis is an agent skill from sickn33/agentic-awesome-skills. Diagnose slow weather-data workflows by measuring discovery, transfer, parsing, scientific processing, and rendering separately before changing code.

When should I use Weather Pipeline Performance Diagnosis?

Weather Pipeline Performance Diagnosis fits situations like: tasks that involve Physical and earth sciences.

How do I install Weather Pipeline Performance Diagnosis in Claude Code?

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

How do I install Weather Pipeline Performance Diagnosis in Codex?

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

Can I use Weather Pipeline Performance Diagnosis 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-pipeline-performance-diagnosis -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-pipeline-performance-diagnosis, .gemini/skills/weather-pipeline-performance-diagnosis, .github/skills/weather-pipeline-performance-diagnosis and .opencode/skills/weather-pipeline-performance-diagnosis in your project.

What does Weather Pipeline Performance Diagnosis need to run?

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

Does Weather Pipeline Performance Diagnosis 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 Pipeline Performance Diagnosis 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 Pipeline Performance Diagnosis use?

Weather Pipeline Performance Diagnosis 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 Pipeline Performance Diagnosis use?

About 1.8k tokens (SKILL.md is roughly 7.1k 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 Pipeline Performance Diagnosis?

Skills that share tags, products or a category with Weather Pipeline Performance Diagnosis: 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 Pipeline Performance Diagnosis?

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.