AF gmail-inbox-zero
Gmail Inbox Zero Triage - Interactive inbox management using gog CLI with Telegram buttons. Use when the user wants to achieve inbox zero, triage their Gmail inbox interactively, process ALL inbox messages (read and unread) with AI summaries and batch actions (archive, filter, unsubscribe). OAuth-based, no passwords needed.
As a process F 52/100 · Will not run — References files that are not bundled: scripts/queue_manager.py
How to improve
- The text references files that are not there: add them or drop the references.
- 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: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: scripts/queue_manager.py
Process rating: all ten parameters 52/100
- 0Tools and files. 1 referenced file(s) missing: scripts/queue_manager.py
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 40Consistency. Frontmatter name (gmail-inbox-zero) differs from the folder (gmail-inbox-zero-triage)
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 48 steps, 1 vague phrases
- 100Failures and branches. 2 branches, has a failure section
- 100Execution cost. Instruction body is 1376 tokens
- 100Running it twice. Mutating operations check current state
- 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 325: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 48 items
- +4Has examples (13 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.