/sn-infographic

name: sn-infographic
description: |
  Generates professional infographics with various layout types and visual styles.
  Analyzes content, recommends layout and style, and generates publication-ready infographics.
  Use when user asks to create "infographic", "信息图", "visual summary", or "可视化".
metadata:
  project: SenseNova-Skills
  tier: 1
  category: scene
  priority: 9
  user_visible: true
triggers:
  - "infographic"
  - "information graphic"
  - "infographics generation"
  - "visual summary"
  - "data visualization"
  - "visual explanation"
  - "diagram"
  - "生成信息图"
  - "信息图生成"
  - "生成 infographic"
  - "信息图表"
  - "图表生成"
  - "数据可视化"
  - "图解"

sn-infographic

Info graphic generation scene skill (tier 1), relying on the `sn-image-generate`, `sn-image-recognize`, and `sn-text-optimize` tools provided by `sn-image-base` (tier 0).

Features:

• Evaluation of prompt quality (auto mode)
• Prompt expansion (force/auto mode)
• Multiple rounds of image generation and VLM review
• Output the best result based on quality ranking

Input Specification

| Parameter | Type | Default Value | Description | |-----------|------|---------------|-------------| | `user_prompt` | string | **Required** | Original user request. UTF-8 text; may include Markdown, URLs, or structured data. Length bounded only by the underlying LLM context budget. | | `max_rounds` | int | `1` | Maximum number of generation rounds. Valid range: `1`–`8`. When `max_rounds=1`, the Step 3 VLM review and the early-termination check are both skipped. | | `output_mode` | string | `friendly` | `friendly`: one-line content description + rank=1 single image | | | | | `verbose`: full quality ranking + timing stats + all images (ordered by rank) | | `prompts_expand_mode` | string | `auto` | `auto`: evaluate `user_prompt` quality first; enter Step 2 expansion only when it falls short | | | | | `force`: skip evaluation, always execute Step 2 expansion | | | | | `disable`: skip Step 2, use `user_prompt` directly as `expanded_prompt` | | `aspect_ratio` | string | *inferred* (`16:9`) | Set by **Main Agent** when the user states an explicit supported ratio (e.g. `16:9` / `9:16`, optionally via `宽高比` / `画面比例` / `aspect ratio`); otherwise left unset and the **Worker** infers it in Step 0 from `user_prompt` (orientation / scene cues) per `references/runtime-parameters.md`. | | `image_size` | string | *inferred* (`2k`) | Set by **Main Agent** when the user states an explicit size (`2k` / `4k`); otherwise the **Worker** infers it in Step 0 (currently a single option, `2k`). `4k` is forwarded to the model and may be rejected (e.g. sensenova) → surfaced as an error. |

> **Who extracts what:** Main Agent parameter extraction resolves `max_rounds`, `output_mode`, `prompts_expand_mode`, and `aspect_ratio` / `image_size` (each only when the user gives an explicit value). `aspect_ratio` and `image_size` without an explicit value are inferred by the Worker in Step 0.

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),无需显式传入。

| Call Type | Tool | Authentication Parameters | Description | |-----------|------|---------------------------|-------------| | **LLM** | sn-text-optimize (evaluation/expansion) | Default reads `SN_TEXT_API_KEY` -> `SN_CHAT_API_KEY` -> `SN_API_KEY` | Built-in default points to Sensenova internal network service | | **VLM** | sn-image-recognize (image review) | Default reads `SN_VISION_API_KEY` -> `SN_CHAT_API_KEY` -> `SN_API_KEY` | Built-in default points to Sensenova internal network service | | **Image Generation** | sn-image-generate | Default reads `SN_IMAGE_GEN_API_KEY` -> `SN_API_KEY`; `SN_IMAGE_GEN_API_KEY` is only needed for image-specific override | Default uses image generation configuration of `sn-image-base` |

**When encountering `MissingApiKeyError` or needing to specify a model**: pass explicitly via CLI parameters, parameter reference `$SN_IMAGE_BASE/references/api_spec.md`.

**`$SN_IMAGE_BASE` path explanation**: `$SN_IMAGE_BASE` is the installation directory of the `sn-image-base` skill (`SKILL.md` exists). The agent can locate this path by skill name `sn-image-base` in the list of installed skills.

Architecture: Main Agent + Worker Agent

This skill uses a two-tier agent architecture:

| Role | Responsibility | |------|----------------| | **Main Agent** | Receive user request, normalize parameters, send preflight, start Worker, collect results, send text and images to user | | **Worker Agent** | Execute the generation pipeline (expand → multiple rounds of generation + review → sort), return structured JSON |

**Responsibility Boundaries**:

• Worker Agent **does not send any messages to the user directly**, only returns structured JSON
• Main Agent is responsible for sending all user-visible messages
• Worker Agent's last message **must be and only be** the JSON string defined in the Return Contract
• Worker Agent's internal VLM calls **always execute directly**, without spawning subagents

Workflow

Main Agent Workflow

1. **Parameter extraction** from the user request, in three passes: 1. **Inline KV directives** — parse tokens of the form `key=value` where `key` ∈ {`max_rounds`, `output_mode`, `prompts_expand_mode`, `aspect_ratio`, `image_size`}; strip recognized tokens from the user message, and the remainder becomes `user_prompt`. Example: `"生成一张信息图 max_rounds=3 output_mode=verbose"` → `user_prompt="生成一张信息图"`, `max_rounds=3`, `output_mode=verbose`. 2. **Keyword recognition** (case-insensitive, applied to the stripped text) — fill any parameter not yet set by inline KV using the table below:

| Parameter | Trigger keywords | Resolved value | |-----------|------------------|----------------|