Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add moonlight-lupin/agent-skills --skill fill-templategit clone --depth 1 https://github.com/moonlight-lupin/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/moonlight-lupin/agent-skills/fill-template)<a href="https://agentmods.dev/skills/moonlight-lupin/agent-skills/fill-template"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/fill-template/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/moonlight-lupin/agent-skills/fill-template"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/fill-template.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00235 | $0.02824 |
| Opus 5.5 | $0.00094 | $0.01130 |
| Sonnet 5.5 | $0.00047 | $0.00565 |
| Haiku 4.5 | $0.00023 | $0.00282 |
Grade A, and why
fill-template scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 20d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fill Template (mail-merge)
Turn one master template + a data table into many filled copies — one per row — swapping only the parts that vary and leaving the master's layout and styling untouched. Document generation only: nothing is sent, posted or signed.
Scope and routing
Use this skill when the user has one template and a list, and wants a filled copy per row;
or when they have a filled/example .docx and want a reusable tokenised template derived from it
(extract_template). Do not use it to produce a single bespoke letter (just edit one document),
or where the host environment mandates a different document pipeline. Extract of .xlsx forms is
out of scope this round.
Inputs the user provides
- The master template — a
.docxletter or form, or a.xlsxform. One master per run. This is the source of truth for layout and styling. - The data table — a
.xlsxor.csv, one row per output, with a header row. Columns supply the values that vary (names, amounts, dates, references…). - (Optional) a naming pattern — how each output file is named, e.g.
Letter_{Name}. Defaults to<template>_<row-number>.
Workflow (do this, in order)
If the user hands you a filled example rather than a master, run extract first (step 0), show
ext["mapping"], and wait for confirmation before generating. Otherwise start at step 1.
- Extract from a filled .docx (optional reverse path).
extract_template(src, out_dir, name=None)writes<name>_tokenised.docxand<name>_data.csv(name defaults to the source stem) and returns a report. Varying values become{{Token}}using the same run-aware replacement astokenise. The CSV has one row per repeating instance. Showreport["mapping"](literal → token → where) and anyreport["uncertain"]entries — those spans were left literal rather than guessed. Token names come only from five typed shapes: afterDear→Recipient;$…→Amount;INV-123→InvoiceRef; dates like12 Mar 2026→Date;ACME-0042→AccountRef. Collisions becomeAmount2, …. Untyped variation is left literal — a genericValue1/Value2fallback is not implemented; fall back to manualtokenisefor those fields. Then continue from step 3 with the extracted template and skeleton, or let the user edit the mapping and re-tokenise.from fill_template import ( read_content, tokenise, tokens_in, load_rows, generate, extract_template, ) ext = extract_template("filled_letters.docx", "extracted") print(ext["mapping"]) # confirm-before-reuse digest print(ext["uncertain"]) # left literal; do not invent
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 20d ago Changed · -14 lines · -1 tokens per session 2fb6863447e5
- 1mo ago Changed · +35 lines · +21 tokens per session 60fdc5b61888
- 1mo ago First seen · 169 lines · 215 tokens per session scan A 3e95648319a9
fill-template is a skill published in the GitHub repository moonlight-lupin/agent-skills (112 stars, last pushed 4d ago), licensed MIT. It adds 235 tokens to every session and 2,824 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
spreadsheet-analysis
Inspect, profile, clean, reconcile, analyze, visualize, and verify spreadsheet data while preserving formulas, formatting, types, and source files. Use when working with .xlsx, .xlsm, .xls, .ods, .csv, or .tsv files; answering questions from a workbook; auditing formulas or data quality; comparing sheets or versions…
document-converter-suite
Convert local PDF, Office, Markdown, HTML, text, CSV, TSV, and JSON files; batch-convert folders, extract tables, fill templates, and edit PDF pages.
xlsx
Create, inspect, edit, and verify Excel XLSX workbooks with openpyxl, including CSV import, formulas, styling, error checks, and optional LibreOffice rendering.
format-data-to-copy-paste-tables
Convert unstructured text, emails, or receipts into consistent CSV or TSV rows for a spreadsheet.
document-exports
Generate and TEST downloadable document exports (PDF/CSV) from a Node/Express backend — pdfkit streaming, CSV BOM, cents formatting, and the supertest/pdfkit pitfalls that silently break tests.
xlsx-data-wrangling
Query, validate, clean, and manipulate Excel (.xlsx/.xls) spreadsheets programmatically without GUI using pandas, openpyxl, and polars.