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AC sol-email

Read and send emails using the SolEmail automation system. Use when asked to check emails, send files via email, respond to an email, or set up email automation.

ClawHub Agent Skills author: Pixelwitch v1.0.0 MIT-0 8 files body ≈ 835 tokens Open the sourceclawhub.ai↗ analyzed 21 h ago

Read and send emails using the SolEmail automation system.

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

ProcedureGitHubSoftware developmentAI and agentsData and analyticstype 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
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
87
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Exfiltration read-dotenv README.md:111
      Reads a .env file
      cp SolEmail/.env.example ~/.openclaw/workspace/.env
    • low Dangerous commands cmd-cron-mention README.md:244
      Mentions editing / listing crontab (quoted — discussed, not commanded)
      Run automated email checks on a schedule. Add to your crontab (`crontab -e`):
      quoted

    Files scanned: 8. 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 56/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 6 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 10 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 835 tokens

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 161: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 10 items
    • +4Has examples (10 code blocks)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    This email automation skill is disclosed, but it needs review because it can let an AI read personal mail, send messages and attachments, package local files, and run scheduled checks without strong confirmation guardrails.
    LLM: suspicious (high) · 24 Jun 2026