brand-xlsx

brand-xlsx

Use this skill when the user wants reusable branded Excel/workbook generation from a company .xlsx template and variable user-provided data.

This is an AI-agent skill for Codex and Claude Code. The user should describe the workbook/model they want filled; the agent maps that request to named cells and named regions, invokes the internal Python engine, verifies the output, and returns the generated .xlsx.

The seven verbs: three deterministic + four model-assisted

Every brand skill (brand-docx, brand-pptx, brand-xlsx) implements the same contract. The deterministic core is extract / verify / generate; on top of it sit the optional learning verbs comprehend / learn / propose-overrides / refine, each fail-closed (the engine validates every proposal and authors every value).

Verb	Input	Output
extract	a company .xlsx template	a reusable Brand Profile
comprehend (optional, model-driven)	a saved profile + a model-authored comprehension.json	the profile with a validated, cached comprehension block
verify	a saved Brand Profile	QA findings + a verdict
generate	data (a GridDocument) + a profile	a new on-brand .xlsx
learn (deterministic distillation)	the profile's cross-run generation history	recurring QA findings distilled into shell-frozen overrides, advisory until --accept
propose-overrides (model-driven)	the recurring remainder learn could not bind + a model-authored proposal	shell-backed corrections through the same fail-closed sink, advisory until --accept
refine	end-of-generation user feedback (text or a screenshot) as a refinement.json delta	the existing comprehension overlaid for FUTURE generations, advisory until --accept

comprehend is optional: generate works on the deterministic profile alone. See reference/comprehension.md for the full step.

Hard Rules

• Treat python scripts/cli.py ... as an internal engine command, not the user-facing workflow.
• scripts/cli.py is a LAUNCHER that locates the engine root by itself: it works from this skill folder AND from the repo/plugin root (set BRAND_DOCS_ROOT to override). Never guess deeper paths like scripts/brandkit/....
• Run the dependency preflight before starting extract / comprehend / verify / generate, and report missing or unusable dependencies before proceeding.
• Extract opens the source template read-only and saves brand-kit/<name>/template/shell.xlsx byte-for-byte.
• Generate opens the saved shell and resolves every named cell/region through profile.json.
• Do not put style names, colors, fonts, or brand identifiers in a GridDocument.
• If the user did not provide a template or enough data, ask for the missing input.
• Return the generated file path plus a QA summary.
• Consult profile.json.artifact_catalog before generation when the user asks to mimic a specific piece of the template.

Preflight (always first)

Before doing any work, run:

python scripts/cli.py doctor

Use its output to decide the run mode:

• If a required Python dependency is missing, install/repair it before extraction or generation; the core engine is not ready.
• If only visual renderers are missing or unusable (soffice plus pdftoppm or optional PyMuPDF/fitz), the core L0 workflow can still run, but a full visual audit cannot be claimed. Tell the user what is missing, include the install/repair hint printed by doctor, and either proceed with degraded QA or install the renderer first.
• If optional OCR (tesseract) is missing, the visual audit can still run, but rendered residual-text proof is incomplete. Report that limitation when judging stale placeholders or field caches.
• For --qa deep or --qa strict, prefer repairing/installing renderers before generation. If the environment cannot run them, deep generates a degraded manifest and strict fails with a visual proof blocker.

Agent Workflow

1. Run the dependency preflight above and report any degraded capability.
2. Determine the brand name and locate the user-provided .xlsx template.
3. If no matching brand-kit/<name> exists, extract one.
4. Comprehend the template (optional, model-driven; see below). Skip when a current comprehension is already cached or no model is available.
5. Convert the user's tabular/model data into GridDocument JSON.
6. Generate the .xlsx with the internal engine.
7. Run QA and report any warnings honestly.
8. Feedback (only after returning the file): invite a refinement of the understanding for future workbooks (see below).

Before generation, inspect profile.json.artifact_catalog when the user asks to mimic a specific workbook piece. It records OOXML parts, named ranges, formulas, sheet dimensions, table names, merged cells, row/column sizing, cell styles, and number formats.

Authoring the GridDocument

The Grid is where "correct workbook" becomes "great workbook". Author it region-first, against the profile, never cell-address-first:

1. Read brand-kit/<name>/PROFILE.md before writing a fill. It lists the named-region roles, the brand cell styles, and the palette tokens. Address content to NAMED regions from that table; the engine fills the template's real ranges.
2. Respect region bounds. Size data to the region; when real data overflows, split it or ask the user to grow the template range instead of spilling into unnamed cells.
3. Formulas are content, not output. Never paste a computed value where the template keeps a formula: the engine preserves formulas, and QA fails the build if one is lost.
4. Numbers carry the brand too. Use the captured number.<family> roles (currency, percent, date, ...) for masks; never invent a format string.
5. Color discipline. Brand cell styles already carry the look: the default is NO direct color. For true emphasis, reference a palette role or a theme slot, never a hex.
6. Never name a style, font, or hex. If a fill needs something the role table cannot express, say so in the QA summary instead of inventing formatting: the resolver is the only author of values.

Feedback (end of generation)

Ask for feedback only after you have returned the generated .xlsx and its QA summary - never before or during generation. Invite the user to reply with text or a screenshot of the workbook, and name the roles, palette colors, and sections you actually used so the answer is concrete. A screenshot is your own multimodal read; the engine only ever ingests the structured JSON delta you distil from it.

Turn the answer into a small refinement delta of verbatim ids and merge it with the refine verb (see reference/comprehension.md):

python scripts/cli.py refine --name <brand> --input refinement.json --accept

A refinement improves FUTURE generations of this brand only - it mutates the saved profile, never the .xlsx you just produced. To apply it, generate again.

When the SAME QA finding recurs across runs, you can also propose a shell-bound correction with propose-overrides: the comprehend-input bundle surfaces the recurring generation_history, and you NAME a shell-backed re-point (a stub role to an existing healthy role, a number_format mask the shell uses, or a captured demo value) that the engine binds fail-closed (see reference/comprehension.md). It is advisory until --accept, improves FUTURE generations only, and every live correction surfaces as an INFO override_applied finding in QA.

Internal Extract

python scripts/cli.py extract --name <brand> --template <template.xlsx> --scope project

Internal Comprehend (optional, model-driven)

Read reference/comprehension.md for the full guidance, the six questions, and the anti-overfitting directive. In short:

python scripts/cli.py comprehend-input --name <brand>   # prints {facts, excerpt} for the model
python scripts/cli.py comprehend --name <brand> --input comprehension.json  # the ONLY writer

Skip this verb when comprehension.status is present and its source_shell_sha256 equals the live provenance.shell.sha256. Never re-run it at generate time.

xlsx readiness. The Excel extractor surfaces named-region cover anchors and sample-data regions, while fields is intentionally empty because workbooks do not have a TOC-style derived index. A current comprehension can therefore steer cover fills/clears and demo-region cleanup, but it must not invent an index ref; a ref into the empty field inventory is fail-closed and will be rejected.

Internal Verify

python scripts/cli.py verify --name <brand> --scope auto --qa auto

--qa selects the QA depth (see reference/visual-audit.md):

• fast: deterministic L0 only.
• auto: L0 + L1 visual pixel proxies when renderers (soffice plus pdftoppm or optional PyMuPDF/fitz) are present; otherwise L0 plus a single INFO visual.unavailable.
• deep: L0 + L1 + a visual_manifest.json and per-page PNGs; if tesseract is installed the manifest also includes OCR text/hits. The orchestrator must then run the L2 step (see below).
• strict: deep visual audit plus gate errors when full render proof is unavailable or L1/OCR evidence is not clean.

Verify has no output to render, so all modes behave as L0 at verify time; the visual stages run at generate time.

Internal Generate

python scripts/cli.py generate --name <brand> --input <grid-document.json> --output <output.xlsx> --scope auto --qa auto

See reference/comprehension.md and reference/visual-audit.md.

Visual audit (two-stage)

The engine renders the output and runs deterministic pixel proxies, but the qualitative visual judgement is yours (the orchestrator), never the engine's - the Python engine never calls a model. To run the full two-stage audit:

1. Generate with --qa deep. The engine renders each printed page to a PNG, runs the L1 proxies, and writes visual_manifest.json next to the output in an <output-file>.visual/ dir, such as workbook.xlsx.visual/ (a side artifact; the .xlsx bytes never change).
2. Read the manifest path from stdout (visual manifest: <path>).
3. Open the PNGs listed in pages[*].png. For every entry in checklist, judge PASS/FAIL against the rendered pages, taking l1_findings and ocr.hits into account.
4. If any checklist item FAILS (or an L1 WARNING is confirmed visually as a real defect, or a visual.ocr_residual_text hit is confirmed as stale visible template text): repair the grid/content or the generated composition, regenerate, then re-run the audit. Loop until the checklist is clean, or until no further targeted repair can be justified without user input.

L1 findings are WARNING-only and never fail the gate by themselves; the real qualitative gate is your L2 judgement.

During repair, treat the template as a source of reusable workbook affordances, not a rule to preserve blindly. If inherited print areas, hidden rows/columns, frozen regions, named-region geometry, or other template structures create blank printed pages, overflow, clipped tables, or stale visible content, diagnose the structure as the cause and make the smallest targeted composition change. It is acceptable to adjust or collapse inherited scaffolding when preserving it damages the final workbook. After every repair, regenerate and rerun --qa deep or --qa strict.

Current Guarantees and Limits

M2 fills named cells and named regions while preserving formulas and workbook topology in the shell. Region fills that exceed the named range are refused before saving. When a current comprehension block is present, generation can clear corroborated cover/demo regions while preserving formulas; derived indexes remain out of scope for XLSX because the field inventory is intentionally empty.

The two-stage visual audit closes the "L0-only" gap: L1 deterministic pixel proxies catch rendered-layout defects L0 cannot see (blank/broken printed pages, content bleeding past the printable margins), and the L2 manifest drives the orchestrator's qualitative judgement and repair loop. See reference/visual-audit.md. When soffice and both PDF rasterizers (pdftoppm, optional PyMuPDF/fitz) are absent (e.g. CI), the audit degrades cleanly to L0 plus a single INFO visual.unavailable; exit codes are unchanged.