AF excel-to-word-grounded-fill
Grounded generation and precision filling of Word documents from Excel data while preserving a supplied Word template's structure and formatting. Use when the user supplies an authoritative .xlsx/.xls/.csv data file, a completed .docx reference example, and a blank or partially populated .docx target, and asks to intelligently draft and insert concise text without inventing facts or mixing the reference example's content into the result.
Grounded generation and precision filling of Word documents from Excel data while preserving a supplied Word template's structure and formatting.
As a process F 34/100 · Will not run — References files that are not bundled: references/grounding-and-qa.md
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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: references/grounding-and-qa.md
Process rating: all ten parameters 34/100
- 0Tools and files. 1 referenced file(s) missing: references/grounding-and-qa.md
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (excel-to-word-grounded-fill) differs from the folder (ipsossss)
- 70When it triggers. States when to use, but not when not to
- 100Steps. 29 steps
- 100Execution cost. Instruction body is 1366 tokens
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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 441: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 29 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.