BC maybeai-sheet-cli
Use when the user works with MaybeAI spreadsheets through the mbs CLI for workbook inspection, local or remote-URL file import, native cross-workbook import/export, worksheet/range/table writes, worksheet calculation and error scans, complete table/SQL reads with frame export, SQL-to-Base materialization, full worksheet data refreshes that keep headers, formulas, worksheet styling, chart/image CRUD, dashboard validate/refresh/export-template flows, or sharing. Route dashboard design and chart composition to `sheet-dashboard`.
Use when the user works with MaybeAI spreadsheets through the mbs CLI for workbook inspection, local or remote-URL file import, native cross-workbook…
As a process C 64/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-requests-env-secretSKILL.md:1Skill asks the runtime to inject credential env vars into its sandbox: MAYBEAI_API_TOKEN — verify each one is needed for the stated purposerequired_environment_variables: MAYBEAI_API_TOKEN
Files scanned: 25. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6953 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "required_environment_variables" - note
edit-residuethe text marks something as outdated (lines 28, 129, 291, 333): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 64/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 18 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 6953 tokens
- 100Steps. 22 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- low The response is described with custom markup (6 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
- -32 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 531: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 22 items
- +4Has examples (25 code blocks)
- +4Reference files are cited in the instructions (13 of 16)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.