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AB Automation Workflow Helper

Turns your weekly repetitive tasks into automated bots. Identifies high-value automation opportunities, picks the right tool (Dify / Coze / n8n workflow, or zero-Token RPA scripts), designs process templates, and calculates ROI. Triggered when the user mentions "automation", "repetitive tasks", "workflow", "efficiency", "save time", "bot", "RPA", "process automation", "automate", or asks to automate repetitive browser/file tasks.

ClawHub Agent Skills author: laziobird v1.0.1 MIT-0 2 files body ≈ 5 565 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 65/100 · Nearly there — weak spots: consistency, running it twice

ProcedureZapierInfrastructuretype 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%
75
Run on models
none yet
Process rating
B
65/100
Nearly there
Running it twice w 4
30
Consistency w 8
40
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning body-long SKILL.md body ≈ 5565 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 65/100

  • 30Running it twice. 13 mutating operations with no state check
  • 40Consistency. Frontmatter name (Automation Workflow Helper) differs from the folder (automation-workflow-helper)
  • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 60Failures and branches. 2 branches
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5565 tokens
  • 85Steps. 70 steps, 1 vague phrases
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 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

  • +4Description does not say when NOT to use the skill (false activations)
  • -231 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +3Description length 433: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 70 items
  • +3Output format is stated explicitly
  • +4Has examples (24 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
This is a coherent automation guide, but it under-scopes guidance for saved login sessions, credentials, local files, and high-impact browser automation.
LLM: suspicious (high) · VirusTotal: benign · 28 May 2026