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Reconstruct slide images, screenshots, scanned PDFs, or image-only PPTX files as object-level editable PowerPoint. Use for 图片转可编辑PPT、截图还原PPT and diagram reconstruction; not authoring a new deck from research notes.
npx skills add yuan1z0825/nature-skills --skill nature-image2pptInstalls from the source repository, including any supporting files.
Downloads contain the instructions shown here. Use the install command for the complete skill.
Use this directory as the complete runtime. Run deterministic actions only through:
python <image2ppt-root>/cli/image2ppt/cli.py <command> ...
Use Python 3.10 or later with requirements.txt installed. When a dedicated
environment exists, substitute <image2ppt-root>/.venv/bin/python on macOS/Linux
or <image2ppt-root>/.venv/Scripts/python.exe on Windows for every python
command below. Do not continue after a failed doctor; install only the reported
missing dependency, then rerun it.
Do not discover or invoke another Skill, CLI, Prompt, Schema, module, or state machine.
Always read references/workflow.md. Read references/runtime-dependencies.md
only for setup or doctor failures, and read
references/ocr-text-hints-contract.md only when choosing or troubleshooting OCR.
Before writing a page manifest, read references/page-decision-tree.md and
references/manifest-schema.md. Add only the references needed by that page:
references/region-decomposition.md and
references/object-routing.md;references/manifest-arrow-extension.md;references/assets-provenance-contract.md.Before accepting or delivering output, read references/qa-contract.md.
page_jobs.json as the only page-state source.pages/page_NNN/manifest.json as the only page-content source.deck_manifest.json as the final-assembly source.prepare, run next/dispatch/record/reset/hints/finalize, and the
page commands in the local CLI for stateful lifecycle operations.manifest.json.image2ppt_region_decomposition.--out..image importprocess-sheet --asset-sheet-sourceUse builtin-imagegen when the agent runtime exposes image_gen.imagegen; it is
the preferred backend because the worker can inspect edit inputs and import the
explicit local result. Use the CLI image contract only when the built-in tool is
unavailable, errors, cannot read an input, or returns no valid local output. A
missing optional argument such as model, mask, size, quality, or output path never
authorizes fallback. Record the actual producer and permitted fallback reason in
imagegen-jobs.json.
The CLI image contract is provider-neutral at the transport boundary. Select
codex-oauth only for GPT Image model ids. Select openai-compatible-api for any
provider-specific model whose endpoint implements the OpenAI Images-compatible
/images/generations and/or /images/edits schema. Do not infer the image backend
from the task's language model. Use an explicit backend when provenance matters;
auto uses Codex OAuth only for compatible GPT Image ids and otherwise selects the
configured API without sending Codex OAuth credentials to third parties.
python <image2ppt-root>/cli/image2ppt/cli.py doctor --json
Use Baidu AI Studio PADDLE_OCR_TOKEN when configured. If it is absent, tell the
user once that the local builtin-ink fallback measures text geometry but does
not recognize characters; offer the configuration path in
references/ocr-text-hints-contract.md. Respect an offline-only choice.
python <image2ppt-root>/cli/image2ppt/cli.py prepare <input...> \
--out-root output/image2ppt --image-backend builtin-imagegen
To pin a configured third-party provider/model for auditable provenance, prepare
with --image-backend openai-compatible-api. The run contract records the exact
IMAGE2PPT_IMAGE_MODEL from the active project config or environment; it does not
substitute a GPT Image default merely because no --model flag was passed.
Use --no-text-hints only when OCR processing is intentionally disabled. Regenerate
hints without creating a new run when needed:
python <image2ppt-root>/cli/image2ppt/cli.py run hints <run-dir>
python <image2ppt-root>/cli/image2ppt/cli.py run next <run-dir> --json
python <image2ppt-root>/scripts/build_page_worker_prompt.py \
<run-dir> --page <page-id> --out <absolute-page-dir>/worker-prompt.md
python <image2ppt-root>/cli/image2ppt/cli.py run dispatch \
<run-dir> --page <page-id> --agent-id <id> --prompt-file <absolute-prompt>
For exactly one page, claim it with --local and reconstruct it in the current
agent. For multiple pages, dispatch independent page workers up to the capacity in
page_jobs.json. Do not reset a live worker merely because it is slow.
Plan a structured page as 3–5 semantic regions and route each region independently. Use measured compound diagrams: measure every node, relation, and protected anchor. Keep measurable circles, cards, straight/dashed relations, and simple connectors native. Use bounded transparent assets only for complex local subparts.
Represent a thin arrow as one connector with its arrowhead on the same object. Represent a filled arrow as one Arrow AutoShape, with centered label text inside the same object. Never construct an ordinary arrow from a line plus triangle and never flatten a whole knowledge graph into one image.
Write new page manifests with schema_version: 2. Use structured
visual_inventory items with explicit kind and representation values, and
write a concrete quality_evidence observation for every required quality check.
Formula rendering is a hard gate: a missing engine, converter, or failed compile
must keep the page failed unless the user explicitly approves that exact formula
exception and the manifest records both user_approved_exception: true and a
concrete approval_note.
The worker Prompt performs the deterministic sequence. Its final gates are:
python <image2ppt-root>/cli/image2ppt/cli.py page build <page-dir>
python <image2ppt-root>/scripts/run_image2ppt_qa.py <page-dir>
# The first run writes visual-review-evidence.template.json and remains pending.
# Inspect source.png against render/rendered.png, copy and complete the template
# as visual-review-evidence.json, repair if needed, then:
python <image2ppt-root>/scripts/run_image2ppt_qa.py <page-dir> \
--visual-review-status reviewed \
--visual-review-evidence <page-dir>/visual-review-evidence.json
python <image2ppt-root>/cli/image2ppt/cli.py page contact-sheet <page-dir>
The evidence file must cover the current source/render hashes and every required
check with a specific observation. --visual-review-notes is optional context and
cannot substitute for the evidence file.
Record only after standard validation and the Image2PPT region, arrow, and rendered gates pass:
python <image2ppt-root>/cli/image2ppt/cli.py run record \
<run-dir> --page <page-id> --agent-id <id>
Use the same run reset → dispatch → record lifecycle to repair rejected pages.
When run next reports finalize, run:
python <image2ppt-root>/cli/image2ppt/cli.py run finalize <run-dir>
python <image2ppt-root>/scripts/run_final_image2ppt_qa.py <run-dir>
# The first run writes final/visual-review-evidence.template.json and remains pending.
# Inspect every rendered slide, complete final/visual-review-evidence.json, then:
python <image2ppt-root>/scripts/run_final_image2ppt_qa.py <run-dir> \
--visual-review-status reviewed \
--visual-review-evidence <run-dir>/final/visual-review-evidence.json
Finalize rebuilds from page manifests, preserves source speaker notes, validates the
package, and writes the output recorded by deck_manifest.json. Final QA reapplies
manifest arrows, verifies arrow atomicity and compound structure, renders every
slide, checks speaker-note integrity, and writes final/image2ppt_qa.json.
Return the final PPTX path, standard final validation, and
final/image2ppt_qa.json. Report which complex visuals remain replaceable bitmap
assets. Do not call the deck complete while any page/final gate is pending or failed.
--- name: nature-image2ppt description: "Reconstruct slide images, screenshots, scanned PDFs, or image-only PPTX files as object-level editable PowerPoint. Use for 图片转可编辑PPT、截图还原PPT and diagram reconstruction; not authoring a new deck from research notes." --- # Nature Image2PPT Use this directory as the complete runtime. Run deterministic actions only through: ```bash python <image2ppt-root>/cli/image2ppt/cli.py <command> ... ``` Use Python 3.10 or later with `requirements.txt` installed. When a dedicated environment exists, substitute `<image2ppt-root>/.venv/bin/python` on macOS/Linux or `<image2ppt-root>/.venv/Scripts/python.exe` on Windows for every `python` command below. Do not continue after a failed `doctor`; install only the reported missing dependency, then rerun it. Do not discover or invoke another Skill, CLI, Prompt, Schema, module, or state machine. ## Read the local contracts progressively Always read `references/workflow.md`. Read `references/runtime-dependencies.md` only for setup or `doctor` failures, and read `references/ocr-text-hints-contract.md` only when choosing or troubleshooting OCR. Before writing a page manifest, read `references/page-decision-tree.md` and `references/manifest-schema.md`. Add only the references needed by that page: - structured or compound page: `references/region-decomposition.md` and `references/object-routing.md`; - arrows: `references/manifest-arrow-extension.md`; - raster assets or image-backend work: `references/assets-provenance-contract.md`. Before accepting or delivering output, read `references/qa-contract.md`. ## Preserve the single source of truth - Treat `page_jobs.json` as the only page-state source. - Treat each `pages/page_NNN/manifest.json` as the only page-content source. - Treat `deck_manifest.json` as the final-assembly source. - Use only `prepare`, `run next/dispatch/record/reset/hints/finalize`, and the page commands in the local CLI for stateful lifecycle operations. - Keep semantic-region evidence in `manifest.json.image2ppt_region_decomposition`. - Never create a second job file, reconstruction plan, OCR normalizer, page controller, packager, or finalize path. - Let supplemental QA report failures; never let it mutate lifecycle state. ## Keep every write inside its owner directory - Page build, validation, hints, and QA may read and write only inside that page directory. Manifest paths, recorded assets, formulas, reports, previews, and `--out` overrides must not use `..`, symlinks, or absolute paths to escape it. The sole external-input exception is an explicit image-tool result supplied to `image import` or as `process-sheet --asset-sheet-source`; it is copied into the page before becoming a build dependency. - Run-level manifests and final outputs must remain inside the prepared run directory. Finalization rebuilds into a same-directory temporary file and publishes it atomically only after a successful build. - Treat any boundary rejection as a hard failure; do not copy the rejected file back into scope and present it as runtime output. ## Preserve pre-migration behavior - Treat self-containment as a path/import/entrypoint migration, not a redesign of reconstruction behavior. - Generate each worker Prompt from the complete local base layer plus the preserved Image2PPT profile layer. Do not condense, reinterpret, or replace either layer. - Prefer the previously validated visual strategy when several routes satisfy the contracts. Keep simple measured objects native and retain bounded complex assets wherever a native redraw would reduce fidelity. - Never re-author an accepted baseline page merely to prove runtime independence. ## Run the workflow ### Image backend selection Use `builtin-imagegen` when the agent runtime exposes `image_gen.imagegen`; it is the preferred backend because the worker can inspect edit inputs and import the explicit local result. Use the CLI image contract only when the built-in tool is unavailable, errors, cannot read an input, or returns no valid local output. A missing optional argument such as model, mask, size, quality, or output path never authorizes fallback. Record the actual producer and permitted fallback reason in `imagegen-jobs.json`. The CLI image contract is provider-neutral at the transport boundary. Select `codex-oauth` only for GPT Image model ids. Select `openai-compatible-api` for any provider-specific model whose endpoint implements the OpenAI Images-compatible `/images/generations` and/or `/images/edits` schema. Do not infer the image backend from the task's language model. Use an explicit backend when provenance matters; `auto` uses Codex OAuth only for compatible GPT Image ids and otherwise selects the configured API without sending Codex OAuth credentials to third parties. ### 1. Preflight and OCR choice ```bash python <image2ppt-root>/cli/image2ppt/cli.py doctor --json ``` Use Baidu AI Studio `PADDLE_OCR_TOKEN` when configured. If it is absent, tell the user once that the local `builtin-ink` fallback measures text geometry but does not recognize characters; offer the configuration path in `references/ocr-text-hints-contract.md`. Respect an offline-only choice. ### 2. Prepare one run ```bash python <image2ppt-root>/cli/image2ppt/cli.py prepare <input...> \ --out-root output/image2ppt --image-backend builtin-imagegen ``` To pin a configured third-party provider/model for auditable provenance, prepare with `--image-backend openai-compatible-api`. The run contract records the exact `IMAGE2PPT_IMAGE_MODEL` from the active project config or environment; it does not substitute a GPT Image default merely because no `--model` flag was passed. Use `--no-text-hints` only when OCR processing is intentionally disabled. Regenerate hints without creating a new run when needed: ```bash python <image2ppt-root>/cli/image2ppt/cli.py run hints <run-dir> ``` ### 3. Advance and claim pages ```bash python <image2ppt-root>/cli/image2ppt/cli.py run next <run-dir> --json python <image2ppt-root>/scripts/build_page_worker_prompt.py \ <run-dir> --page <page-id> --out <absolute-page-dir>/worker-prompt.md python <image2ppt-root>/cli/image2ppt/cli.py run dispatch \ <run-dir> --page <page-id> --agent-id <id> --prompt-file <absolute-prompt> ``` For exactly one page, claim it with `--local` and reconstruct it in the current agent. For multiple pages, dispatch independent page workers up to the capacity in `page_jobs.json`. Do not reset a live worker merely because it is slow. ### 4. Reconstruct and gate each page Plan a structured page as 3–5 semantic regions and route each region independently. Use measured compound diagrams: measure every node, relation, and protected anchor. Keep measurable circles, cards, straight/dashed relations, and simple connectors native. Use bounded transparent assets only for complex local subparts. Represent a thin arrow as one connector with its arrowhead on the same object. Represent a filled arrow as one Arrow AutoShape, with centered label text inside the same object. Never construct an ordinary arrow from a line plus triangle and never flatten a whole knowledge graph into one image. Write new page manifests with `schema_version: 2`. Use structured `visual_inventory` items with explicit `kind` and `representation` values, and write a concrete `quality_evidence` observation for every required quality check. Formula rendering is a hard gate: a missing engine, converter, or failed compile must keep the page failed unless the user explicitly approves that exact formula exception and the manifest records both `user_approved_exception: true` and a concrete `approval_note`. The worker Prompt performs the deterministic sequence. Its final gates are: ```bash python <image2ppt-root>/cli/image2ppt/cli.py page build <page-dir> python <image2ppt-root>/scripts/run_image2ppt_qa.py <page-dir> # The first run writes visual-review-evidence.template.json and remains pending. # Inspect source.png against render/rendered.png, copy and complete the template # as visual-review-evidence.json, repair if needed, then: python <image2ppt-root>/scripts/run_image2ppt_qa.py <page-dir> \ --visual-review-status reviewed \ --visual-review-evidence <page-dir>/visual-review-evidence.json python <image2ppt-root>/cli/image2ppt/cli.py page contact-sheet <page-dir> ``` The evidence file must cover the current source/render hashes and every required check with a specific observation. `--visual-review-notes` is optional context and cannot substitute for the evidence file. Record only after standard validation and the Image2PPT region, arrow, and rendered gates pass: ```bash python <image2ppt-root>/cli/image2ppt/cli.py run record \ <run-dir> --page <page-id> --agent-id <id> ``` Use the same `run reset` → dispatch → record lifecycle to repair rejected pages. ### 5. Finalize and revalidate the rebuilt deck When `run next` reports `finalize`, run: ```bash python <image2ppt-root>/cli/image2ppt/cli.py run finalize <run-dir> python <image2ppt-root>/scripts/run_final_image2ppt_qa.py <run-dir> # The first run writes final/visual-review-evidence.template.json and remains pending. # Inspect every rendered slide, complete final/visual-review-evidence.json, then: python <image2ppt-root>/scripts/run_final_image2ppt_qa.py <run-dir> \ --visual-review-status reviewed \ --visual-review-evidence <run-dir>/final/visual-review-evidence.json ``` Finalize rebuilds from page manifests, preserves source speaker notes, validates the package, and writes the output recorded by `deck_manifest.json`. Final QA reapplies manifest arrows, verifies arrow atomicity and compound structure, renders every slide, checks speaker-note integrity, and writes `final/image2ppt_qa.json`. ## Deliver Return the final PPTX path, standard final validation, and `final/image2ppt_qa.json`. Report which complex visuals remain replaceable bitmap assets. Do not call the deck complete while any page/final gate is pending or failed.