SKILLEMALL.ai
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Family household coordinator. Aggregates Google Calendar, runs a daily morning briefing (weather, schedule, kids meals, school snacks), watches school Gmail for flyers and PDFs, tracks restaurant visits and ratings from receipt photos, manages a per-store grocery list, drafts a weekly meal plan with a Sat/Sun approval cycle, logs sick-kid medication doses, and proactively flags upcoming trips. Optional Club Studio module (off by default) watches Gmail for fitness bookings and can scrape clubstudiofitness.com via Playwright for class recommendations — a real third-party site, disclosed here up front. All family-specific data (names, phones, calendar IDs, meal catalogs, WhatsApp routing) lives in config.json and is 100% config-driven, including Club Studio's WhatsApp targets.

Not recommendedcritical or high security findings · low grade D
ClawHub Agent Skills author: Harsh Chawla v0.5.1 MIT-0 63 files body ≈ 1 852 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process B 67/100 · Nearly there — weak spots: result and completion, running it twice

GeneratorWhatsAppGmailPlaywrightPersonal productivityInfrastructuretype 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
D
59/100
safety, quality, tests
Safety 60%
53
Quality 40%
69
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
When it triggers w 12
50
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. 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 · 5

  • high Dangerous commands cmd-persistence README.md:158
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    ~/Library/LaunchAgents/com.openclaw.daemon.plist
  • high Dangerous commands cmd-persistence README.md:160
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    launchctl load ~/Library/LaunchAgents/com.openclaw.daemon.plist
Medium and low: 3
  • medium Dangerous commands cmd-persistence SKILL.md:96
    Persistence mechanism (cron / launchd / scheduled task / autorun registry) (quoted — discussed, not commanded)
    `~/Library/LaunchAgents/com.openclaw.daemon.plist` and can be removed at
    quoted
  • medium Dangerous commands cmd-persistence tools.py:29
    Persistence mechanism (cron / launchd / scheduled task / autorun registry) (code comment)
    # (set by ~/Library/LaunchAgents/com.openclaw.daemon.plist) is
    comment
  • low Exfiltration read-dotenv setup_check.py:94
    Reads a .env file (quoted — discussed, not commanded)
    "Run: cp .env.example .env && edit .env")
    quoted

Files scanned: 44. 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")

Process rating: all ten parameters 67/100

  • 0Result and completion. Does not say what the result is
  • 30Running it twice. 4 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 28 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1852 tokens
  • 100Progress reporting. Reports progress
  • high The skill tells the model to perform an irreversible action with no human approval

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
  • +2Single-language instructions
  • +3Description length 784: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 28 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
The skill is mostly coherent and disclosed as a family assistant, but it needs Review because it can send private family data, change calendar entries, and read shared OpenClaw cron logs with limited scoping safeguards.
LLM: suspicious (medium) · 18 Jul 2026