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AB huawei-cloud-functiongraph-trigger-create

Create and configure scheduled TIMER triggers for Huawei Cloud FunctionGraph functions using Quartz Cron expressions. Use this skill when users ask to create triggers, schedule function execution, set up periodic tasks, or configure timers for functions. Triggered by keywords like "create trigger", "set up trigger", "schedule function", "periodic task", "timer", "cron", "创建云函数触发器", "配置云函数触发器", "云函数定时触发", "定时执行", "定时任务", "创建定时器", "FunctionGraph trigger", "schedule function execution".

ClawHub Agent Skills author: huaweicloud-skills-team v1.0.0 MIT-0 10 files body ≈ 1 781 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Create and configure scheduled TIMER triggers for Huawei Cloud FunctionGraph functions using Quartz Cron expressions.

As a process B 70/100 · Nearly there — weak spots: when it triggers, running it twice, progress reporting

GeneratorSoftware developmentInfrastructuretype 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
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
B
70/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
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: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 70/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 55 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1781 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 17 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)
    • -32 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 9 example trigger phrases
    • +3Description length 488: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 55 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    This skill mostly does what it says, but it needs review because its cloud-credential setup and troubleshooting guidance can expose secrets or weaken TLS while performing real Huawei Cloud changes.
    LLM: suspicious (high) · VirusTotal: · 21 Jul 2026