moonlight-lupin/agent-skills/fill-template

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

1. The master template — a .docx letter or form, or a .xlsx form. One master per run.

This is the source of truth for layout and styling.

1. The data table — a .xlsx or .csv, one row per output, with a header row. Columns supply

the values that vary (names, amounts, dates, references…).

1. (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.

1. Extract from a filled .docx (optional reverse path).

extract_template(src, out_dir, name=None) writes <name>_tokenised.docx and <name>_data.csv (name defaults to the source stem) and returns a report. Varying values become {{Token}} using the same run-aware replacement as tokenise. The CSV has one row per repeating instance. Show report["mapping"] (literal → token → where) and any report["uncertain"] entries — those spans were left literal rather than guessed. Token names come only from five typed shapes: after Dear → Recipient; $… → Amount; INV-123 → InvoiceRef; dates like 12 Mar 2026 → Date; ACME-0042 → AccountRef. Collisions become Amount2, …. Untyped variation is left literal — a generic Value1/Value2 fallback is not implemented; fall back to manual tokenise for those fields. Then continue from step 3 with the extracted template and skeleton, or let the user edit the mapping and re-tokenise. ```python 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 ```

1. Analyse the master. Read it with read_content(path) and identify the parts that vary row

to row — names, salutations, amounts, dates, reference numbers — versus the boilerplate that stays fixed. read_content shows repeated paragraph text once with a (×N) count — that's how many places tokenise will replace it (e.g. a name in both the body and the header). See references/tokenising-guide.md for what to tokenise and how to name tokens well.

1. Propose a tokenised template. Build a mapping of each varying phrase → a token name, and

present it to the user as a plain list ("Ms Jordan Lee → {{Name}}, $1,000,000 → {{Amount}}, …"). Token names should match the data file's column headers where possible — that makes the mapping automatic.

1. Confirm with the user. Show the proposed tokens (and, if helpful, save the tokenised template

and show its text) and wait for explicit confirmation before generating anything. This is the review gate — get the template right once, then fan it out. ```python from fill_template import ( read_content, tokenise, tokens_in, load_rows, generate, extract_template, )

print(read_content("Letter_master.docx")) # step 1

rep = tokenise("Letter_master.docx", # step 2 "Letter_tokenised.docx", [{"find": "Ms Jordan Lee", "token": "Name"}, {"find": "$1,000,000", "token": "Amount"}, {"find": "01 Jul 2026", "token": "EffectiveDate"}, {"find": "REF-0001", "token": "Reference"}]) # rep["not_found"] lists any phrase that wasn't located — fix those before generating. print(tokens_in("Letter_tokenised.docx")) `` tokenise finds each exact phrase and replaces it with {{Token}}, **preserving the formatting of the run/cell it sits in** (a bold figure stays bold). It reports a hit count per token and flags any phrase it could not find, so a typo in the find` text is caught before you generate 200 letters.

1. Load the data and check the mapping. headers, rows = load_rows("recipients.xlsx"). The

token→column map defaults to token name == column header (case-insensitive); override any that differ. Any template token with no column, or a mapped column blank for a given row, becomes a visible «MISSING: Token» flag — never a guess.

1. Generate one file per row.

``python report = generate( "Letter_tokenised.docx", rows, token_to_column={"Name": "Recipient"}, # only the ones whose token != column outdir="out", name_pattern="Letter_{Recipient}", ) ` report lists every file written, the rows skipped (e.g. a name-pattern column missing from the data), any unmapped_tokens, and per-file missing` tokens.

1. Report back honestly. Tell the user how many files were produced and where, and surface

every missing flag and unmapped_tokens entry so blanks are dealt with before the batch is used. Outputs are drafts for a person to review before they go out.

How tokenising and replacement work

• Token syntax is {{TokenName}} (spaces inside the braces are ignored).
• `.docx` — replacement is run-aware: the value lands in the run where the token begins (inheriting

its formatting) and any other runs the token spans are trimmed; runs outside the token are left exactly as they were. Body text, tables (including nested), and header/footer text are all covered.

• `.xlsx` — tokens inside any cell on any sheet are replaced; all other cell content, formulas and

formatting are preserved (openpyxl). Put a token anywhere the value should appear, e.g. cell B5 = {{Name}}.

• Dates in the data render as DD MMM YYYY by default; whole numbers drop a trailing .0. For

currency or specific number formats, format the column in the data file — the helper inserts the value as supplied.

• Never invent. A token with no value for a row is written as «MISSING: Token» and recorded in

the report. Fix the data (or the mapping) and re-run; don't hand-edit blanks into invented values.

Safety

• Files only. Generates documents; never sends, posts, emails, signs or files anything.
• One master per run. Don't mix two different templates in a single batch.
• Match the master. Preserve the supplied template's layout and styling; only swap tokens.
• Confirm before fan-out. Get explicit sign-off on the tokenised template and mapping before

generating the batch.

• Surface every blank. Report all «MISSING:…» flags and unmapped tokens; never paper over them.

Files

• scripts/fill_template.py — the engine: extract_template (filled .docx → template + skeleton) ·

read_content (analyse) · tokenise / tokens_in · load_rows (.xlsx/.csv) · generate (one file per row + report). generate accepts a list of row-dicts or the (headers, rows) tuple from load_rows.

• references/tokenising-guide.md — how to choose and name tokens, letters vs forms, the confirm

step, and the MISSING-flag rule.

• examples/example-run.md — a worked end-to-end run (bring your own master + data; no binaries are

shipped).

Principles

• Drafts, not advice — outputs are drafts for a person to review, not finished correspondence.
• Never invent — a missing value is a visible «MISSING: Token» flag, never a silent blank or a

guess.

• Deterministic where it counts — tokenising, mapping and generation are deterministic and

reported; the LLM only proposes the token list for the user to confirm.

• Honesty and calibration — surface every missing/unmapped token and where files were written.
• Workspace hygiene — write outputs to a clear folder; keep the master, the filled source, and

the data untouched. extract_template never writes back to its input file.

Data handling

Runs fully local — the template and data stay on your machine and nothing crosses to an external tool, so names, amounts and references are handled in place. If any step would send data to an external or third-party service, de-identify sensitive personal data first and confirm the egress with the user. This skill itself needs no network and no credentials.

Pitfalls

1. `find` text must be exact — copy the phrase verbatim (punctuation, currency symbols); check the

not_found list before generating.

1. Token == column header wherever possible — then the mapping is automatic; otherwise pass

token_to_column.

1. Confirm before fan-out — fixing the template once beats re-issuing a whole batch.
2. MISSING flags are not failures to hide — surface them; fix the data/mapping and re-run.
3. The data table is a separate file from an .xlsx form template — don't confuse the two.

Verification checklist

• [ ] Master analysed; varying parts identified vs boilerplate. Or extract_template run on a

filled .docx and mapping confirmed.

• [ ] Token list proposed and confirmed by the user before generating.
• [ ] not_found list from tokenise is empty (or resolved). uncertain from extract surfaced.
• [ ] Token→column mapping checked; defaults verified.
• [ ] Batch generated; file count and output folder reported.
• [ ] Every «MISSING:…» and unmapped_tokens entry surfaced to the user.

Requirements

• Python 3.8+
• pip install python-docx openpyxl
• No network access and no credentials — fully local file I/O.