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AB cold-chain-risk-calculator

Calculate temperature excursion risks for cold chain transport. Assesses route risk, packaging suitability, and monitoring requirements for biological samples and pharmaceuticals requiring controlled-temperature shipping.

ClawHub Agent Skills author: AIpoch v1.0.0 MIT-0 3 files · 1 script body ≈ 1 371 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

Calculate temperature excursion risks for cold chain transport.

As a process B 77/100 · Nearly there — weak spots: when it triggers, consistency

ProcedureCommerceWriting and documentstype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
B
77/100
Nearly there
When it triggers w 12
20
Consistency w 8
40
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

    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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "skill-author"
    • note frontmatter-key unknown frontmatter key "status"

    Process rating: all ten parameters 77/100

    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (cold-chain-risk-calculator) differs from the folder (cold-chain-risk-calculator-1)
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 31 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Execution cost. Instruction body is 1371 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • 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 221: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 31 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This skill is a simple local cold-chain risk calculator with no evidence of hidden access, persistence, network use, or data exfiltration.
    LLM: benign (high) · VirusTotal: · 16 Sept 2026