SKILLEMALL.ai

BC gigamail

Email and calendar for your OpenClaw agent through the GigaMail MCP server — read, search, draft, reply, schedule — with every destructive action (send, delete, calendar write) held for out-of-band human approval that the agent cannot grant itself. 给你的 OpenClaw 代理一个真实邮箱和日历:读信、搜索、起草自由,发送与删除必须由人带外批准,代理无法批准自己。

ClawHub Agent Skills author: adecubed v0.3.1 MIT-0 2 files body ≈ 2 617 tokens Open the sourceclawhub.ai analyzed 3 d ago

Email and calendar for your OpenClaw agent through the GigaMail MCP server — read, search, draft, reply, schedule — with every destructive action (send…

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorTelegramGitHubAI and agentsPersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 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 description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 24 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 34 steps
  • 100Failures and branches. 5 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2617 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (5 tags): a typed call is more reliable

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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 308: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 34 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: clean
The skill transparently connects an agent to GigaMail for email and calendar work, with disclosed human approval gates for destructive actions, but its unpinned pip install deserves caution.
LLM: benign (medium) · VirusTotal: · 8 Sept 2026