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BC cocktail-craft

鸡尾酒配方与研发技能——IBA 官方配方（精确用量）、经典鸡尾酒历史、比赛获奖配方、世界知名酒吧签名酒、配方设计方法论（六根配方/风味平衡）、进阶技法（奶洗/慢煮/浸泡/澄清/脂肪洗涤）、自制材料（bitters/tinctures/shrubs/cordials）、杯具装饰冰处理指南。

ClawHub Agent Skills author: touri v1.0.0 MIT-0 16 files body ≈ 515 tokens Open the sourceclawhub.ai↗ analyzed 29 h ago

鸡尾酒配方与研发技能——IBA 官方配方（精确用量）、经典鸡尾酒历史、比赛获奖配方、世界知名酒吧签名酒、配方设计方法论（六根配方/风味平衡）、进阶技法（奶洗/慢煮/浸泡/澄清/脂肪洗涤）、自制材料（bitters/tinctures/shrubs/cordials）、杯具装饰冰处理指南。

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData and analyticsMarketingtype 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
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 16. 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")
  • note frontmatter-key unknown frontmatter key "trigger"
  • note edit-residue the text marks something as outdated (lines 22): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 12 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 515 tokens
  • 100Running it twice. No mutating operations

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)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 144: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 12 items
  • +4Reference files are cited in the instructions (11 of 11)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: clean
This appears to be a cocktail reference skill with no evidence of hidden code execution, credential access, exfiltration, or destructive behavior, though users should review installation commands and food/alcohol safety gaps.
LLM: benign (medium) · VirusTotal: · 28 May 2026