AC email-manager
Comprehensive AI email attendant for OpenClaw agents. Use this skill whenever the user asks about email, wants to check or sort their inbox, asks about important messages, wants to reply to an email, asks what emails need their attention, wants to set up email monitoring, wants to manage spam, or mentions anything about organizing their mailbox. Also triggers for scheduling email checks, managing email folders, reviewing pending or high-priority messages, or asking about the daily email report. This skill assumes an email MCP or equivalent tool is already connected to the agent — it does not set one up. Trigger this skill proactively whenever email management, inbox organization, or email-related workflows are mentioned.
Comprehensive AI email attendant for OpenClaw agents.
As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
How to improve
- 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: 3. 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 58/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 62 mutating operations with no state check
- 40Consistency. Frontmatter name (email-manager) differs from the folder (email-manager-pro)
- 65Failures and branches. 3 branches
- 70When it triggers. States when to use, but not when not to
- 85Steps. 69 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Execution cost. Instruction body is 3289 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 12 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
- +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 730: enough signal without eating the budget
- +4Structure: 25 headings
- +3Step-by-step instructions: 69 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.