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AF task-automation

Automate repetitive tasks with scripts, workflows, and schedules. Create efficient automation for file operations, data processing, API calls, and scheduled jobs. Use when automating repetitive work, creating workflows, or scheduling tasks. Triggers on "automate", "workflow", "schedule", "repetitive", "batch", "cron".

ClawHub Agent Skills author: engsathiago v1.0.0 MIT-0 2 files body ≈ 3 157 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 27/100 · Will not run — References files that are not bundled: context

ProcedureInfrastructuretype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
F
27/100
Will not run
References files that are not bundled: context
Tools and files w 18
0
Steps w 15
0
Result and completion w 14
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 missing-ref reference to a missing file: context

Process rating: all ten parameters 27/100

Will not run. References files that are not bundled: context
  • 0Tools and files. 1 referenced file(s) missing: context
  • 0Steps. Prose only: no discrete steps
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 40Consistency. Frontmatter name (task-automation) differs from the folder (task-automation-workflows)
  • 50Failures and branches. 0 branches, has a failure section
  • 70When it triggers. States when to use, but not when not to
  • 100Execution cost. Instruction body is 3157 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 319: enough signal without eating the budget
  • +4Structure: 26 headings
  • +4Has examples (26 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: clean
This is a plain automation guide with no executable install behavior, though its file, API, and scheduling examples should be used carefully.
LLM: benign (high) · VirusTotal: · 29 May 2026