SenseNova-Skills is a collection of modular skills that extend SenseNova models with office-assistant capabilities such as image generation, presentation creation, spreadsheet analysis, and research. The skills are designed for use in agent runtimes and can be combined into productivity workflows; the catalogue entries are individual skills and agents from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OpenSenseNova/SenseNova-Skills --skill sn-ppt-standardgit clone --depth 1 https://github.com/OpenSenseNova/SenseNova-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-ppt-standard)<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-ppt-standard"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-ppt-standard/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-ppt-standard"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-ppt-standard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 38 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00170 | $0.08905 |
| Opus 5 | $0.00085 | $0.04452 |
| Sonnet 5 | $0.00034 | $0.01781 |
| Haiku 4.5 | $0.00017 | $0.00890 |
Grade A, and why
sn-ppt-standard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 478 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sn-ppt-standard
⚠️ This skill must be invoked through
/skill sn-ppt-entry. Never start here directly — the entry skill collects parameters, parses uploaded files, and writestask_pack.json+info_pack.jsonthat this skill requires. If you arrived here without those files, stop and tell the user to enter via/skill sn-ppt-entryor "生成 PPT".
This skill is self-contained — no dependency on sn-image-base for LLM/VLM (T2I still goes through sn-image-base). Generation logic stays in $SKILL_DIR/scripts/run_stage.py; use run_stage_with_progress.py only as a WebUI progress wrapper. Every subcommand is deterministic: one input set → one output artifact → one-line JSON status.
Preconditions
<deck_dir>/task_pack.jsonexists andppt_mode in {"standard", "fast"}<deck_dir>/info_pack.jsonexists
Any missing → stop and tell user to enter via /skill sn-ppt-entry.
When ppt_mode == "fast": build first, then iterate. Make decisions autonomously — do not ask the user about colors, fonts, page count, or layout preferences. Infer reasonable defaults from the query and start building immediately. Skip optional web search and image search. Run the full pipeline including PPTX export. Data: use uploaded documents first; if none, use mock data labeled [Sample Data] and tell the user in chat which data needs replacement. Images: AI generation for decorative images, ECharts for charts — no questions asked.
Post-generation (fast mode only)
After the PPTX is generated, do NOT just say "done, any feedback?" Instead, provide a structured set of refinement suggestions based on the actual content you generated. This helps the user understand what changed between your fast draft and what a polished standard-mode version would look like.
1. Quick wins (3-5 specific suggestions): Point to concrete things the user could improve with one-line instructions. Tie each suggestion to a specific slide or element. Examples:
- "Slide 3: replace the mock revenue numbers with your actual Q4 data"
- "Slide 5: swap the generic team photo placeholder with your real team picture"
- "Cover slide: try a darker background for more impact — I can switch it to deep navy"
- "Slide 7: the bar chart is using sample data — give me your real numbers and I'll regenerate it"
What ships with it
35 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- lib/__init__.py 0 B runs code
- lib/model_client.py 12 KB runs code
- prompts/asset_plan.md 3.9 KB
- prompts/asset_qc.md 475 B
- prompts/deck_review.md 734 B
- prompts/outline.md 5.7 KB
- prompts/page_html_rewrite.md 9.5 KB
- prompts/page_html.md 7.2 KB
- prompts/page_review.md 784 B
- prompts/page_rewrite.md 469 B
- prompts/refine_apply.md 2.2 KB
- prompts/refine_review.md 279 B
- prompts/style_samples.md 1.8 KB
- prompts/style_spec.md 2.6 KB
- references/html_constraints.md 2.3 KB
- references/style_catalog.md 14 KB
- scripts/build_style_catalog.py 5.4 KB runs code
- scripts/export_pptx/html_to_pdf.mjs 9.0 KB runs code
- scripts/export_pptx/html_to_pptx.mjs 4.9 KB runs code
- scripts/export_pptx/lib/browser_setup.mjs 3.3 KB runs code
- scripts/export_pptx/lib/cli_guards.mjs 14 KB runs code
- scripts/export_pptx/lib/dom_extractor.mjs 34 KB runs code
- scripts/export_pptx/lib/echarts_to_pptx.mjs 29 KB runs code
- scripts/export_pptx/lib/image_downloader.mjs 4.1 KB runs code
- scripts/export_pptx/lib/postprocess_pptx.mjs 6.9 KB runs code
- scripts/export_pptx/lib/pptx_builder.mjs 83 KB runs code
- scripts/export_pptx/lib/style_parser.mjs 18 KB runs code
- scripts/export_pptx/package-lock.json 9.1 KB
- scripts/export_pptx/package.json 341 B
- scripts/export_pptx/screenshot.mjs 2.8 KB runs code
- scripts/launch_workbench.py 19 KB runs code
- scripts/progress_event.py 7.8 KB runs code
- scripts/resume_scan.py 1.8 KB runs code
- scripts/run_stage_with_progress.py 4.2 KB runs code
- scripts/run_stage.py 80 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago Changed e76d3365def5
- 8d ago First seen · 478 lines · 170 tokens per session scan A 4743e18ab104
sn-ppt-standard is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,570 stars, last pushed today), licensed MIT. It adds 170 tokens to every session and 8,905 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
skin-creator
Create and apply a two-asset LobsterAI visual skin from the user's style description. Use only when the AI Skin Designer kit supplies the structured skinpack workflow marker; do not use for ordinary theme or image requests.
music-search
Search cloud drives for downloadable music resources (songs, albums, lossless audio). Use this skill when the user wants to download a specific song or album. Do NOT use for general music information, lyrics, or recommendations.
canvas-design
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
image-gen
Instructions for generating images such as illustrations, posters, avatars, and logos through an OpenAI-compatible image API. It explains how to select a configured image provider and save the result in the project.
remotion-best-practices
Best practices for Remotion - Video creation in React.
ad-creative
Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for.