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@hugohe3 — per your note on #157 ("let's open an issue to discuss [the native diagram] direction first") and your #156 review, this is the dedicated thread for the native-diagram track, kept separate from the self-evolution / visual thread (#163). I'll follow exactly the four-step order you laid out in #156, and end with a single, self-contained PR you can run to test the real effect yourself. The five stacked PRs (#156, #158, #159, #160, #161) stay held until we've agreed the direction here.
TL;DR — the shape
Native DrawingML diagrams are an opt-in, experimental tool for one narrow niche: keeping a complex produced figure (3D / gradient / skeuomorphic) editable and brand-recolorable when the user already owns the source. The repo ships only the mechanism + one CC0/synthetic demo component — never a lifted library, never wired into the core flow. For the large majority of figures, hand-authored SVG stays the right tool, and I'll say so explicitly below.
1. What the existing modes already cover — and the one cell DrawingML adds
PPT Master already has several visual-production modes. The honest question isn't "is SVG bad" (it isn't) — it's "is there a cell in this matrix that none of them fills?"
Figure type
Hand-authored SVG
AI image
Chart
Native DrawingML
Flat / structural (flow, columns, matrix)
✅ already good
—
—
not needed
Data visualization
ok
—
✅
—
Complex produced figure (3D isometric, glossy gradient, skeuomorphic)
⚠️ doable but very expensive to hand-author, low quality ceiling
❌ raster: not editable, can't recolor to brand exactly, text is baked in
partial
✅
(Formulas — latex_render — and the icon library are separate concerns: neither is a complex produced figure, so they're out of scope for this comparison.)
The narrow conclusion (and I want it to be self-limiting): you're right that for most structural figures hand-authored SVG is already good enough. DrawingML earns its keep in exactly one cell — a produced-quality complex figure that must also stay editable and brand-recolorable. AI images give the look but are raster and dead to editing/recoloring; SVG stays editable but the production cost for that class of figure is high and the quality ceiling low. DrawingML is the only mode that is both — conditioned on the user already owning the source figure.
Where it should NOT be used (drawing the boundary myself): flat structural diagrams, data charts, anything SVG already does cleanly. This is deliberately not a general-purpose path.
The decisive contrast to show, not assert, is DrawingML vs AI image on the same complex figure: the AI version looks right but can't be recolored to the deck's brand or edited; the DrawingML version recolors to brand in one step and stays editable. That's the demo in §4.
Applicability — the dividing line we found in practice
Not theory — this is what running the same content with and without native across three scenarios (government / tech / project), then iterating on the failures, actually taught us:
Reach for native when all three hold:
the figure is a complex produced structure — multi-layer isometric, gradient / 3D-shaded, skeuomorphic — the class that is expensive to hand-author in SVG and whose hand-SVG quality ceiling is low;
a component's structure genuinely matches the content (a 3-tier platform ↔ a 3-platform component) and the content fits the slots' length / role (short labels for short-label slots);
you own a license-clean source for it.
Stay with hand-authored SVG when:
the figure is simple / flat — a basic pyramid, funnel, flow, or card row — where hand-SVG comes out clean and complete (in our tests it matched or beat native there);
the page is content-dense (long descriptions, mixed data) that won't condense into a component's short slots without losing substance;
nothing in the library matches, or there is no license-clean source.
Net: native is a narrow, opt-in enhancement for complex produced figures you already own — not a general-purpose diagram path. The text-fusion friction is real but fixable (the three fixes in §4); this applicability line is the durable takeaway, and it's earned from practice, not asserted.
2. Licensing — dissolved by design, not negotiated
Your #156#2 is the precondition, and I think the design removes the exposure rather than arguing around it:
The repo redistributes nothing third-party. It ships only the mechanism (extract a slide's editable DrawingML → component; inject + recolor / text-fill / font-unify) plus one CC0 or synthetic demo component. No lifted library ships, by default or otherwise.
Each user grows their own local library from sources they have the rights to — their own decks, their org's, or properly-licensed / CC0 open material. (To be precise: "open" ≠ unrestricted — CC-BY-NC, attribution terms, etc. exist; the import-side responsibility sits with the user, and the docs frame the tool as bring-your-own-licensed. The tool never bundles or scrapes arbitrary decks.)
We never ship non-commercial material, and the default workflow never encourages lifting — it requires the user to point at a source they own.
What's extracted is a single-page component, not a whole deck — a building block the user recolors into something of their own, not a verbatim asset reused as-is.
Net: what we provide is a generative reuse mechanism — the feasibility for each user to reuse from their own successful material — not a redistributed asset pile. That's the difference from "lifting a library into a widely-used repo."
3. Positioning — opt-in / experimental, not core
Behind an explicit opt-in path — concretely, a standalone workflows/native-diagram.md + its own CLI, never invoked by any SKILL.md core step — documented as experimental; no core-pipeline wiring until the "where SVG falls short" cases are agreed with you.
On recurring value + the slot-mapping friction (your 【bug】windows环境生成ppt中svg显示异常 #3): you're right that "pull a component, then hand-map slots from meta.json" sits close to dropping in a diagram and editing it. The value over manual editing is the one-shot automation: theme-flatten so a single base-hex remap re-derives every gradient/shade/tint, plus text-fill and font unification, applied across the whole component in one step — versus hand-recoloring every shape. The demo shows this one-shot brand recolor. Honest boundary: for a simple figure this isn't worth it; for a produced figure you own, one-shot recolor still beats redrawing or manually recoloring it. Hence: narrow, opt-in. (This niche is the materials layer of Direction: Track A — visual guidance as soft reference + human-curated layered structure #163; the memory layer proposed there is exactly what would smooth the remaining hand-mapping over repeated use — the two tracks are one idea split into two threads at your request.)
4. The minimal, verifiable PR (the evidence — same artifact as §1)
Evidence — a fair test already run (3 scenarios)
To avoid a cherry-picked demo, I ran the same content twice per scenario: once where the model only hand-authors SVG, once where the native library is available and the model decides for itself whether any page's content warrants a component. Three auto-branded scenarios (no lifted/branded assets in the deck — each got its own palette), three pages each:
Scenario (auto-brand)
Where the model chose native
Where it declined → hand-SVG
Government report (navy + gold)
only the platform-architecture page → a 4-layer isometric stack, recolored to navy
cover, KPI-comparison page
Tech launch (navy + cyan)
only the system-architecture page → a 3-layer platform with card-rows, recolored to cyan
cover, scenario-data page
Project report (sea-blue + amber)
only the platform-architecture page → a 3-layer platform, recolored to sea-blue
cover, Q2/Q3 page
It isn't rigged. Given a free choice, the model reached for native on exactly one page per deck — the architecture figure — and declined on covers and data pages, judging no genuine fit. That self-limiting behavior is the §1 boundary observed, not asserted.
But the first native pages broke on text. On close inspection the native architecture pages leaked the component's original source-deck text and overflowed — the produced 3D shape was right, the text fusion was wrong. Rather than hide it, I root-caused it to three concrete, fixable causes:
data-text fails silently on the wrong shape. The resolver only accepts an object {"<id>":"text"}; given an array it silently keeps the original text — and the model can't tell (it sees the attribute it wrote, not the render). → fix: accept both forms and warn when a data-text matches zero slots, so a silent no-op becomes impossible.
Length budgets were char-count, not CJK visual width. A slot sized for a 4-char Latin label overflows with 4 Chinese characters (~2× width). → fix: budget in CJK-visual-width.
Library metadata was thin/wrong (one component tagged cycle was actually a radiate-and-columns layout). → fix: per-slot slot_spec (role / length-budget / style) + corrected structure, so the model selects and maps reliably.
With those fixed, the complex page came out clean (before/after attached): a 3-tier cloud-edge-device platform, the deck's own content on every slot within budget, recolored to brand in one line, carrying isometric gradient-shaded depth — a produced quality hand-authored SVG can't reach cheaply. The very same page before the fix was 100% leaked original text.
The honest dividing line: for simple figures (a 5-tier pyramid, a 4-stage funnel) hand-SVG comes out clean and complete — native isn't worth its friction there. Native earns its keep on complex produced structures (multi-layer isometric, gradient-heavy) where the depth is real and hand-SVG's ceiling is low. The text-fusion friction is not fundamental — it's the three fixable issues above.
The PR itself
A single self-contained PR, off main, not the five stacked ones — PR: #168
Mechanism + the three robustness fixes — the machinery you reviewed as sound in feat(native-diagram): component format + resolver core #156 (theme-flatten, byte-exact splice, foreign-rel stripping): extract → inject → recolor — plusdata-text array-tolerance + zero-match warning, CJK-width budgets, and the slot_spec metadata that makes matching reliable.
One synthetic / CC0 demo component that ships in the repo — demo_synthetic_platform, an original 3-layer capability platform (application / capability / foundation, 5 cards per tier) authored from plain DrawingML, no vendor or client source. The evidence above used my own licensed components, which stay local / bring-your-own (the repo redistributes nothing); the shipped demo is synthetic so you run it license-clean.
One reproduce command that renders the with/without, so you test the real effect rather than read a claim:
It builds a two-slide before/after PPTX through the real finalize_svg + svg_to_pptx pipeline: slide 1 places the component via a data-native-diagram placeholder — recolored to a sample brand and re-texted onto a new scenario in one step; slide 2 is the same content hand-drawn as flat SVG (the "without" baseline). Open native_diagram_demo_out/exports/*.pptx. (Requires py -3.11 with python-pptx + lxml.)
The extracted component library / assets → never ships in the repo. Bring-your-own-licensed, local-only — so there is nothing on our side to license-clean.
What I'm asking
Let's align on this shape — opt-in, bring-your-own-licensed, mechanism-only, one narrow niche, not core. To make the decision concrete:
If it doesn't → that's a fine answer, not a negotiation: I drop the core ambition entirely, the path stays a personal external tool, and we close the five stacked PRs.
Either way the demo PR is there for you to verify necessity directly, and #163 stays the separate self-evolution thread. Your call on whether this lives as its own issue or a comment on #156.
Thanks again for the depth of the #156 review — the OOXML feedback especially. 🙏
@hugohe3 — per your note on #157 ("let's open an issue to discuss [the native diagram] direction first") and your #156 review, this is the dedicated thread for the native-diagram track, kept separate from the self-evolution / visual thread (#163). I'll follow exactly the four-step order you laid out in #156, and end with a single, self-contained PR you can run to test the real effect yourself. The five stacked PRs (#156, #158, #159, #160, #161) stay held until we've agreed the direction here.
TL;DR — the shape
Native DrawingML diagrams are an opt-in, experimental tool for one narrow niche: keeping a complex produced figure (3D / gradient / skeuomorphic) editable and brand-recolorable when the user already owns the source. The repo ships only the mechanism + one CC0/synthetic demo component — never a lifted library, never wired into the core flow. For the large majority of figures, hand-authored SVG stays the right tool, and I'll say so explicitly below.
1. What the existing modes already cover — and the one cell DrawingML adds
PPT Master already has several visual-production modes. The honest question isn't "is SVG bad" (it isn't) — it's "is there a cell in this matrix that none of them fills?"
(Formulas —
latex_render— and the icon library are separate concerns: neither is a complex produced figure, so they're out of scope for this comparison.)The narrow conclusion (and I want it to be self-limiting): you're right that for most structural figures hand-authored SVG is already good enough. DrawingML earns its keep in exactly one cell — a produced-quality complex figure that must also stay editable and brand-recolorable. AI images give the look but are raster and dead to editing/recoloring; SVG stays editable but the production cost for that class of figure is high and the quality ceiling low. DrawingML is the only mode that is both — conditioned on the user already owning the source figure.
Where it should NOT be used (drawing the boundary myself): flat structural diagrams, data charts, anything SVG already does cleanly. This is deliberately not a general-purpose path.
The decisive contrast to show, not assert, is DrawingML vs AI image on the same complex figure: the AI version looks right but can't be recolored to the deck's brand or edited; the DrawingML version recolors to brand in one step and stays editable. That's the demo in §4.
Applicability — the dividing line we found in practice
Not theory — this is what running the same content with and without native across three scenarios (government / tech / project), then iterating on the failures, actually taught us:
Reach for native when all three hold:
Stay with hand-authored SVG when:
Net: native is a narrow, opt-in enhancement for complex produced figures you already own — not a general-purpose diagram path. The text-fusion friction is real but fixable (the three fixes in §4); this applicability line is the durable takeaway, and it's earned from practice, not asserted.
2. Licensing — dissolved by design, not negotiated
Your #156 #2 is the precondition, and I think the design removes the exposure rather than arguing around it:
Net: what we provide is a generative reuse mechanism — the feasibility for each user to reuse from their own successful material — not a redistributed asset pile. That's the difference from "lifting a library into a widely-used repo."
3. Positioning — opt-in / experimental, not core
workflows/native-diagram.md+ its own CLI, never invoked by any SKILL.md core step — documented as experimental; no core-pipeline wiring until the "where SVG falls short" cases are agreed with you.meta.json" sits close to dropping in a diagram and editing it. The value over manual editing is the one-shot automation: theme-flatten so a single base-hex remap re-derives every gradient/shade/tint, plus text-fill and font unification, applied across the whole component in one step — versus hand-recoloring every shape. The demo shows this one-shot brand recolor. Honest boundary: for a simple figure this isn't worth it; for a produced figure you own, one-shot recolor still beats redrawing or manually recoloring it. Hence: narrow, opt-in. (This niche is the materials layer of Direction: Track A — visual guidance as soft reference + human-curated layered structure #163; the memory layer proposed there is exactly what would smooth the remaining hand-mapping over repeated use — the two tracks are one idea split into two threads at your request.)4. The minimal, verifiable PR (the evidence — same artifact as §1)
Evidence — a fair test already run (3 scenarios)
To avoid a cherry-picked demo, I ran the same content twice per scenario: once where the model only hand-authors SVG, once where the native library is available and the model decides for itself whether any page's content warrants a component. Three auto-branded scenarios (no lifted/branded assets in the deck — each got its own palette), three pages each:
data-textfails silently on the wrong shape. The resolver only accepts an object{"<id>":"text"}; given an array it silently keeps the original text — and the model can't tell (it sees the attribute it wrote, not the render). → fix: accept both forms and warn when adata-textmatches zero slots, so a silent no-op becomes impossible.cyclewas actually a radiate-and-columns layout). → fix: per-slotslot_spec(role / length-budget / style) + corrected structure, so the model selects and maps reliably.The PR itself
A single self-contained PR, off
main, not the five stacked ones — PR: #168Mechanism + the three robustness fixes — the machinery you reviewed as sound in feat(native-diagram): component format + resolver core #156 (theme-flatten, byte-exact splice, foreign-rel stripping): extract → inject → recolor — plus
data-textarray-tolerance + zero-match warning, CJK-width budgets, and theslot_specmetadata that makes matching reliable.One synthetic / CC0 demo component that ships in the repo —
demo_synthetic_platform, an original 3-layer capability platform (application / capability / foundation, 5 cards per tier) authored from plain DrawingML, no vendor or client source. The evidence above used my own licensed components, which stay local / bring-your-own (the repo redistributes nothing); the shipped demo is synthetic so you run it license-clean.One reproduce command that renders the with/without, so you test the real effect rather than read a claim:
It builds a two-slide before/after PPTX through the real
finalize_svg+svg_to_pptxpipeline: slide 1 places the component via adata-native-diagramplaceholder — recolored to a sample brand and re-texted onto a new scenario in one step; slide 2 is the same content hand-drawn as flat SVG (the "without" baseline). Opennative_diagram_demo_out/exports/*.pptx. (Requirespy -3.11withpython-pptx+lxml.)What happens to the five stacked PRs
Treating them as one direction, as you asked:
What I'm asking
Let's align on this shape — opt-in, bring-your-own-licensed, mechanism-only, one narrow niche, not core. To make the decision concrete:
Either way the demo PR is there for you to verify necessity directly, and #163 stays the separate self-evolution thread. Your call on whether this lives as its own issue or a comment on #156.
Thanks again for the depth of the #156 review — the OOXML feedback especially. 🙏