AC maybeai-sheet
Manages MaybeAI spreadsheets across upload, workbook profiling, read/write, worksheet operations, formulas, formula lineage tracing, formatting, and SQL result-table workflows. Use when working on Excel or spreadsheet tasks in MaybeAI, including file import, large row-count or large cell-count table imports that should route to SheetTable/PG, workbook semantic overview, worksheet inspection, cell or range updates, row or column changes, formula execution, cell dependency tracing, readable report sheets, sharing, or export. Use sheet-dashboard instead for chart-authoring or dashboard-first workflows.
As a process C 59/100 · Has gaps — weak spots: result and completion, consistency, running it twice
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 · 1
✓ No critical or high findings
Medium and low: 1
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low Obfuscation
obf-base64-blobscripts/07-charts-pictures.sh:11Long base64-looking blob (quoted — discussed, not commanded)PICT…E64="${PICT…WP4////fwAJ+wP9K…ggg==}"quoted
Files scanned: 26. 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 59/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 10 mutating operations with no state check
- 40Consistency. Frontmatter name (maybeai-sheet) differs from the folder (maybeai-sheet-skill)
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Failures and branches. 7 branches
- 100Steps. 122 steps
- 100Execution cost. Instruction body is 3326 tokens
- 100Progress reporting. Reports progress
- 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
- -31 of 12 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 606: enough signal without eating the budget
- +4Structure: 25 headings
- +3Step-by-step instructions: 122 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (9 of 10)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.