# Brand Pptx

> >-

- **Type:** Skill
- **Install:** `agentstack add skill-ferdinandobons-brand-docs-brand-pptx`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ferdinandobons](https://agentstack.voostack.com/s/ferdinandobons)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [ferdinandobons](https://github.com/ferdinandobons)
- **Source:** https://github.com/ferdinandobons/brand-docs/tree/main/skills/brand-pptx
- **Website:** https://ferdinandobons.github.io/brand-docs/

## Install

```sh
agentstack add skill-ferdinandobons-brand-docs-brand-pptx
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# brand-pptx

Use this skill when the user wants reusable branded PowerPoint generation from
a company `.pptx` template and variable user-provided content.

This is an AI-agent skill for Codex and Claude Code. The user should describe
the deck they want; the agent converts that request into an IntermediateDocument,
uses the internal Python engine, verifies the output, and returns the generated
`.pptx`.

## 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 `.pptx` 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** | content (an IntermediateDocument) + a profile | a new on-brand `.pptx` |
| **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](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//template/shell.pptx` byte-for-byte.
- Generate opens the saved shell and resolves every semantic block through `profile.json`.
- Do not put style names, colors, fonts, or brand identifiers in an IntermediateDocument.
- If the user did not provide a template or enough content, 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:

```bash
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 `.pptx` template.
3. If no matching `brand-kit/` 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 outline/content into `IntermediateDocument` JSON.
6. **Generate** the `.pptx` 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 decks (see below).

Before generation, inspect `profile.json.artifact_catalog` when the user asks
to mimic a specific template piece. It records OOXML parts, media parts, slide
layouts, masters, placeholder geometry, slide texts, and slide size.

## Authoring the IntermediateDocument

The IDoc is where "correct deck" becomes "great deck". Author it role-first,
against the profile, never layout-first:

1. **Read `brand-kit//PROFILE.md` before writing a block.** It lists the
   role table and the brand palette tokens. Choose every block by MEANING from
   that table; the engine maps it to the template's real masters and layouts.
2. **One idea per slide.** A slide carries one heading and a few supporting
   blocks; split dense content across slides instead of shrinking it. Decks
   read in seconds per slide, not minutes.
3. **Lead with structure.** Cover fields first, an agenda where the deck keeps
   sections, then one section heading per major topic so the template's
   section scaffolding stays meaningful.
4. **Prefer native objects.** `chart`, `table` (merged cells included) and
   diagram blocks are authored NATIVELY: never describe a chart in prose or
   paste it as an image when a native block exists.
5. **Color discipline.** Placeholders inherit the brand from the layout: the
   default is NO run color. For true emphasis, reference a palette role
   (`primary`, `text`, ...) or a theme slot (`accent1`), never a hex.
6. **Reuse before re-deriving.** When a comprehension is present, prefer its
   `component` / `section` fragments (`comprehension.fragments` in
   `profile.json`) with `{{slot}}` values over hand-building recurring slides.
7. **Never name a layout, font, size, or hex.** If a block 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 `.pptx` and its
QA summary - never before or during generation. Invite the user to reply with
**text or a screenshot** of the deck, 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](reference/comprehension.md)):

```bash
python scripts/cli.py refine --name  --input refinement.json --accept
```

A refinement improves **FUTURE** generations of this brand only - it mutates the
saved profile, never the `.pptx` 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](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

```bash
python scripts/cli.py extract --name  --template  --scope project
```

## Internal Comprehend (optional, model-driven)

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

```bash
python scripts/cli.py comprehend-input --name    # prints {facts, excerpt} for the model
python scripts/cli.py comprehend --name  --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.

> **pptx readiness.** The PowerPoint extractor surfaces cover anchors, the
> agenda/section-list field inventory when present, and slide regions. A current
> comprehension can therefore steer cover fill, demo-slide clearing, and
> agenda/section-list regeneration. If a deck genuinely has no agenda/section
> field, do not force one; a ref into an empty inventory is fail-closed and will be
> rejected. Deeper native-object authoring remains a pptx enrichment milestone.

## Internal Verify

```bash
python scripts/cli.py verify --name  --scope auto --qa auto
```

`--qa` selects the QA depth (see [reference/visual-audit.md](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

```bash
python scripts/cli.py generate --name  --input  --output  --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 slide to a PNG, runs the L1
   proxies, and writes `visual_manifest.json` next to the output in an
   `.visual/` dir, such as `deck.pptx.visual/` (a side artifact;
   the `.pptx` bytes never change).
2. Read the manifest path from stdout (`visual manifest: `).
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 IntermediateDocument/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 layout affordances, not
a rule to preserve blindly. If inherited placeholders, section/agenda slides,
layout geometry, or other template structures create blank slides, overlaps,
stale entries, or visibly broken pagination, diagnose the structure as the cause
and make the smallest targeted composition change. It is acceptable to collapse,
move, or remove inherited scaffolding when preserving it damages the final deck.
After every repair, regenerate and rerun `--qa deep` or `--qa strict`.

## Current Guarantees and Limits

M2 supports title/content deck generation from the saved shell. Long content is
split across multiple content slides with a conservative capacity guard. When a
current comprehension block is present, generation reconciles the deck by keeping
structural slides, filling cover placeholders in place, clearing corroborated
demo slides, and regenerating the agenda/section list from the new headings.
Table blocks are authored as native PowerPoint table objects (honoring
colspan/rowspan merges). Chart, SmartArt, KPI and image blocks are also authored
natively and on-brand (a real `graphicFrame` chart inheriting the deck theme,
chevron/box autoshapes for SmartArt, a brand-styled metric table for KPIs, a
placed picture for images). A `divider` has no native pptx form and degrades
loudly (a visible `block_degraded` warning, never a silent drop).

The two-stage visual audit closes the "L0-only" gap: L1 deterministic pixel
proxies catch rendered-layout defects L0 cannot see (blank/broken slides, content
bleeding past the slide edges), and the L2 manifest drives the orchestrator's
qualitative judgement and repair loop. See
[reference/visual-audit.md](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.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ferdinandobons](https://github.com/ferdinandobons)
- **Source:** [ferdinandobons/brand-docs](https://github.com/ferdinandobons/brand-docs)
- **License:** MIT
- **Homepage:** https://ferdinandobons.github.io/brand-docs/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ferdinandobons-brand-docs-brand-pptx
- Seller: https://agentstack.voostack.com/s/ferdinandobons
- Browse the marketplace: https://agentstack.voostack.com/browse

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