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-image-resumegit 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-image-resume)<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-image-resume"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-image-resume/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-image-resume"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-image-resume.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00082 | $0.02467 |
| Opus 5 | $0.00041 | $0.01234 |
| Sonnet 5 | $0.00016 | $0.00493 |
| Haiku 4.5 | $0.00008 | $0.00247 |
Grade A, and why
sn-image-resume 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 4d 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sn-image-resume
Resume image generation scene skill (tier 1), relying on the sn-text-optimize and sn-image-generate tools provided by sn-image-base (tier 0).
Features:
- Accepts resume content directly from conversational text
- Supports optional user-provided style direction
- Applies the fixed portfolio-resume layout rules in
prompts/resume.md - Generates a tall designed resume image through
sn-image-generate
Non-goals
- Editing or polishing a plain text resume document without generating an image
- Parsing uploaded resume files as the primary input format
- Creating a conventional single-column ATS resume
- Guaranteeing exact preservation of every long paragraph when the image layout requires compression
Input Specification
| Parameter | Type | Default Value | Description |
|---|---|---|---|
resume_content |
string | Required | Resume text provided by the user in conversation, including name, profile, education, experience, skills, projects, contact details, etc. |
style |
string | Optional | User-specified visual style, tone, color palette, profession aesthetic, or reference mood. May be embedded in resume_content. |
aspect_ratio |
string | 9:16 |
Output aspect ratio. Allowed values: 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 1:1, 16:9, 9:16, 21:9, 9:21. Default is 9:16 (vertical) because the template is a tall stacked portfolio-resume page. |
image_size |
string | 2k |
Image size preset, 1k or 2k. |
output_mode |
string | friendly |
Output mode: friendly or verbose. |
API Configuration
All API calls in this skill are executed through the sn_agent_runner.py of the sn-image-base skill, with authentication parameters using default values (CLI > environment variables > built-in defaults), so they do not need to be passed explicitly in normal use.
| Call Type | Tool | Authentication Parameters | Description |
|---|---|---|---|
| LLM | sn-text-optimize |
Default reads SN_TEXT_API_KEY -> SN_CHAT_API_KEY -> SN_API_KEY |
Converts user resume text into a detailed image generation prompt using prompts/resume.md as the system prompt |
| Image Generation | sn-image-generate |
Default reads SN_IMAGE_GEN_API_KEY -> SN_API_KEY |
Generates the final resume image |
What ships with it
1 file 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.
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.
- 4d ago Changed 9618db1eadee
- 9d ago First seen · 248 lines · 82 tokens per session scan A bdb271e8e1f2
sn-image-resume is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,570 stars, last pushed yesterday), licensed MIT. It adds 82 tokens to every session and 2,467 once invoked, about $0.0004 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.
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