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AC geoskill-temperature-anomaly-mapping

计算温度距平（当期温度减多年同期气候态）与标准化距平，划分暖/冷异常等级，输出距平栅格、异常等级 GeoTIFF 与时序 JSON。Temperature anomaly mapping: current minus multi-year climatology, standardized anomalies, and warm/cold anomaly classes, outputting anomaly rasters, a class GeoTIFF, and a time-series JSON.

ClawHub Agent Skills author: ruiduobao v1.0.0 MIT-0 33 files body ≈ 1 465 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

计算温度距平（当期温度减多年同期气候态）与标准化距平，划分暖/冷异常等级，输出距平栅格、异常等级 GeoTIFF 与时序 JSON。Temperature anomaly mapping: current minus multi-year climatology, standardized anomalies…

As a process C 59/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

ReferenceSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 28. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 59/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 18 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1465 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +2Single-language instructions
  • +3Description length 256: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 18 items
  • +3Output format is stated explicitly
  • +4Has examples (14 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
The main mapper is mostly local, but the package also ships under-disclosed geocoding, download, and credential helpers including hardcoded Earthdata credentials.
LLM: suspicious (high) · 4 Aug 2026