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	User original request
`max_rounds`	int	`1`	Maximum number of generation rounds
`output_mode`	string	`friendly`	Output mode: friendly / verbose
`prompts_expand_mode`	string	`auto`	expand strategy: auto / force / disable

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 orchestration loop (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

Extract

user_prompt

max_rounds

(default 1),

output_mode

(default

friendly

), and

prompts_expand_mode

(default

auto

) from user request

Send uniform preflight message:

"Using sn-infographic skill to generate infographic, please wait..."

Start Worker Agent (Sub-Agent), passing in complete parameters and working directory
When Worker Agent returns
```
status=ok
```
and
```
need_main_agent_send=true
```
:
- max_rounds = 1: Send a one-sentence description of the image content, then send the rank=1 single image
- max_rounds > 1, friendly mode: Generate a one-sentence natural language description based on
```
result
```
  and
```
violations
```
  , send the evaluation text, then send the rank=1 single image
- max_rounds > 1, verbose mode: Send complete text summary message, then send all images in rank order to the user
If Worker Agent returns
```
status=error
```
, report the real
```
error
```
field content to the user

Worker Agent Workflow

Worker Agent receives

user_prompt

max_rounds

output_mode

prompts_expand_mode

, and the working directory of this skill (

SN_IMAGE_INFOG

Step 0 — Initialization

Generate
```
task_id
```
(using timestamp, format
```
YYYYMMDD_HHMMSS
```
)

Create a uniform temporary directory:

/tmp/openclaw/sn-infographic/<task_id>/

TEMP_DIR

Initialize an empty
```
rounds
```
list

Infer

aspect_ratio

(default

16:9

) and

image_size

(default

2k

) from

user_prompt

based on the rules in

$SKILL_DIR/references/runtime-parameters.md

Step 1 —

prompts_expand_mode

Processing

disable
mode:

Skip expand, directly use
```
user_prompt
```
as
```
expanded_prompt
```

Assign variable and write to temporary directory:

bash

EXPANDED_PROMPT="$USER_PROMPT"
echo "$EXPANDED_PROMPT" > "$TEMP_DIR/expanded-prompt.txt"

Record
```
prompts_expand_skipped = true
```

force
mode:

Directly execute Step 2

auto
mode:

Call sn-text-optimize for evaluation
Parse JSON, extract
```
required_results
```
and
```
optional_results
```

Determine logic:

required_pass

: All

answer

required_results

are

"yes"

optional_pass

: The number of

answer="yes"

optional_results

/ total ≥ 0.6

should_expand = not (required_pass and optional_pass)

If JSON parsing fails, default
```
should_expand = true
```
(conservative strategy)

should_expand = false

: Skip Step 2, assign variable and write to temporary directory, record

prompts_expand_skipped = true

bash

EXPANDED_PROMPT="$USER_PROMPT"
echo "$EXPANDED_PROMPT" > "$TEMP_DIR/expanded-prompt.txt"

If
```
should_expand = true
```
: Execute Step 2

Evaluation Call (using

sn-image-base

sn-text-optimize

tool):

bash

python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-text-optimize \
  --system-prompt-path "$SKILL_DIR/references/evaluation-standard.md" \
  --user-prompt "$USER_PROMPT" \
  --output-format json

Step 2 — Content Analysis + Layout & Style Selection + Prompt Expansion

2.0 Content Analysis (using

sn-image-base

sn-text-optimize

tool):

bash

ANALYSIS=$(python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-text-optimize \
  --system-prompt-path "$SKILL_DIR/references/analysis-framework.md" \
  --user-prompt "$USER_PROMPT" \
  --output-format json)

Save analysis result stdout to

analysis.json

in temporary directory

$TEMP_DIR/analysis.json

bash

echo "$ANALYSIS" > "$TEMP_DIR/analysis.json"

2.1 Layout & Style Selection

Read analysis result from temporary directory
```
$TEMP_DIR/analysis.json
```
;

bash

ANALYSIS=$(cat "$TEMP_DIR/analysis.json")

Based on

data_type

tone

audience

, select

layout

and

style

based on the rules in

$SKILL_DIR/references/layout-style-selection.md

;

Read layout/style definition files:

bash

LAYOUT_DEF=$(cat "$SKILL_DIR/references/layouts/<layout>.md")
STYLE_DEF=$(cat "$SKILL_DIR/references/styles/<style>.md")

If file does not exist, fallback to

hub-spoke

corporate-memphis

Save selection result to temporary directory:
```
$TEMP_DIR/layout-style.json
```
;

Format of

layout-style.json

json

{
  "layout": "<layout>",
  "style": "<style>"
}

2.2 Structured Content Generation

Read analysis result and structured content template, convert

user_prompt

into a design-ready structured content based on the template rules:

bash

ANALYSIS=$(cat "$TEMP_DIR/analysis.json")
LAYOUT_STYLE=$(cat "$TEMP_DIR/layout-style.json")
STRUCTURED_CONTENT_TEMPLATE=$(cat "$SKILL_DIR/references/structured-content-template.md")

Follow the three phases defined in the template (High-Level Outline → Section Development → Data Integrity Check), combine the learning objectives, visual opportunities, and key data in

analysis.json

, generate structured content, and save it to the temporary directory:

bash

cat > "$TEMP_DIR/structured-content.md" << 'EOF'
<Content generated based on structured-content-template.md format>
EOF

Rules: All data must be preserved exactly. Do not rewrite. Do not add information that is not in the source.

2.3 Prompt Expansion (using

sn-image-base

sn-text-optimize

tool):

Read structured content and layout/style selection from temporary directory, dynamically concatenate system prompt, and write to temporary file:

bash

STRUCTURED_CONTENT=$(cat "$TEMP_DIR/structured-content.md")
LAYOUT_STYLE=$(cat "$TEMP_DIR/layout-style.json")
LAYOUT=$(echo "$LAYOUT_STYLE" | jq -r '.layout')
STYLE=$(echo "$LAYOUT_STYLE" | jq -r '.style')
LAYOUT_DEF=$(cat "$SKILL_DIR/references/layouts/${LAYOUT}.md")
STYLE_DEF=$(cat "$SKILL_DIR/references/styles/${STYLE}.md")

cat > "$TEMP_DIR/expand-system-prompt.md" << EOF
$(cat "$SKILL_DIR/references/prompts-expand-system.md")

---

## Selected Layout: $LAYOUT

$LAYOUT_DEF

---

## Selected Style: $STYLE

$STYLE_DEF

---

## Output Template Reference

$(cat "$SKILL_DIR/references/base-prompt.md")
EOF

Use the content of

structured-content.md

as user-prompt, read system prompt from temporary file and call sn-text-optimize:

bash

python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-text-optimize \
  --system-prompt-path "$TEMP_DIR/expand-system-prompt.md" \
  --user-prompt "$STRUCTURED_CONTENT" \
  --output-format json

Parse JSON stdout, extract

result

field as

expanded_prompt

, and write to temporary directory:

bash

echo "$EXPANDED_PROMPT" > "$TEMP_DIR/expanded-prompt.txt"

If parsing fails or truncation is suspected (the returned content is incomplete), notify the user and terminate the workflow.

Step 3 — Image Generation Loop

Execute

round

from

max_rounds

sequentially:

Generate Image (using

sn-image-base

sn-image-generate

tool):

bash

python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-image-generate \
  --prompt "$EXPANDED_PROMPT" \
  --image-size "$IMAGE_SIZE" \
  --aspect-ratio "$ASPECT_RATIO" \
  --save-path "$TEMP_DIR/round_<N>.png" \
  -o json

Review Image (only executed when

max_rounds > 1

VLM configuration requirements:

When
```
max_rounds > 1
```
, call VLM for review
Select VLM model from OpenClaw configuration as parameter for image recognition
If no suitable VLM model exists in OpenClaw configuration:
- Notify user that current parameter combination cannot be executed
- Suggest adding VLM configuration or changing max_rounds to 1 to avoid VLM calls
If VLM call times out or fails: do not fallback, report the real error directly

bash

python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-image-recognize \
  --system-prompt-path "$SN_IMAGE_INFOG/references/prompts-critic-system.md" \
  --user-prompt "Evaluate the diagram in the image against the rules. Output your assessment." \
  --images "$TEMP_DIR/round_<N>.png" \
  --output-format json

System prompt comes from

references/prompts-critic-system.md

, user prompt is provided directly.

Save Round Result：

json

{
  "round": 1,
  "image": "$TEMP_DIR/round_1.png",
  "result": "PASS|FAIL",
  "violations_count": 0,
  "violations": [],
  "reasoning": "<Reasoning process, empty string when max_rounds=1>",
  "timing": {
    "image_generation": { "elapsed_seconds": 12.34, "model": "sn_image_model" },
    "vlm_review": { "elapsed_seconds": 5.67, "model": "sensenova-6.7-flash-lite" }
  }
}

Note:

elapsed_seconds

is read from the

--output-format json

return of each CLI call;

image_generation.model

is fixed to the hardcoded placeholder

"sn_image_model"

(sn-image-generate does not return the model field);

vlm_review.model

is read from the JSON return of sn-image-recognize.

timing.vlm_review

is omitted when

max_rounds=1

Early Termination Check (only executed when

max_rounds > 1

If
```
result=PASS
```
, immediately exit the loop, do not continue generating
If
```
result=FAIL
```
, continue to the next round (if there are remaining rounds)

Step 4 — Image Quality Ranking

Sort images by

violations_count

ascending +

round

ascending, return structured JSON to Main Agent.

Return Contract

After Worker Agent completes, its last message must be and only be the following JSON string (bare JSON, no code fences, no preceding or trailing text).

Normal Flow:

json

{
  "status": "ok",
  "need_main_agent_send": true,
  "output_mode": "friendly|verbose",
  "expanded_prompt": "<always contains when output_mode=verbose; value is original user_prompt when prompts_expand_skipped=true, otherwise is expanded result>",
  "prompts_expand_skipped": true,
  "early_terminated": true,
  "timing": {
    "total_elapsed_seconds": 35.12,
    "prompt_detection": { "elapsed_seconds": 2.11, "model": "sensenova-6.7-flash-lite" },
    "content_analysis": { "elapsed_seconds": 3.22, "model": "sensenova-6.7-flash-lite" },
    "prompt_expand": { "elapsed_seconds": 8.45, "model": "sensenova-6.7-flash-lite" }
  },
  "rounds": [
    {
      "round": 1,
      "image": "$TEMP_DIR/round_1.png",
      "result": "PASS|FAIL",
      "violations_count": 0,
      "violations": [],
      "reasoning": "<Reasoning process, empty string when max_rounds=1>",
      "timing": {
        "image_generation": { "elapsed_seconds": 12.34, "model": "sn_image_model" },
        "vlm_review": { "elapsed_seconds": 5.67, "model": "sensenova-6.7-flash-lite" }
      }
    }
  ]
}

Error Flow:

json

{
  "status": "error",
  "error": "<Actual error information>"
}

Rules:

```
status=ok
```
must contain
```
need_main_agent_send: true
```

expanded_prompt

must contain when

output_mode=verbose

; value is original

user_prompt

when

prompts_expand_skipped=true

```
prompts_expand_skipped
```
must contain when expand is not executed (value is
```
true
```
), covering two cases:
```
prompts_expand_mode=disable
```
and
```
prompts_expand_mode=auto
```
and evaluation passes and skip expand
```
early_terminated
```
must contain when early termination (value is
```
true
```
), omitted when normal execution completes
```
violations
```
is an array of strings, from review results
```
reasoning
```
is an empty string when
```
max_rounds=1
```
Top-level
```
timing
```
contains:
- ```
total_elapsed_seconds
```
  : Worker Agent's wall time from Step 0 to returning JSON, calculated by Worker Agent itself
- ```
prompt_detection
```
  : Step 1 evaluation call, containing
```
elapsed_seconds
```
  and
```
model
```
  (read from sn-text-optimize JSON return); omitted when
```
prompts_expand_mode=disable
```
- ```
content_analysis
```
  : Step 2.0 content analysis call, containing
```
elapsed_seconds
```
  and
```
model
```
  (read from sn-text-optimize JSON return); omitted when expand is skipped
- ```
prompt_expand
```
  : Step 2.3 prompt expansion call, containing
```
elapsed_seconds
```
  and
```
model
```
  (read from sn-text-optimize JSON return); omitted when expand is skipped

rounds[].timing.image_generation.model

is fixed to the hardcoded placeholder

"sn_image_model"

```
rounds[].timing.vlm_review
```
is omitted when
```
max_rounds=1
```

Output Format

friendly mode (default)

Text Summary:

when
max_rounds = 1
: Generate a one-sentence description of the image content based on
```
expanded_prompt
```
,不超过50字
when
max_rounds > 1
: Generate a one-sentence description of the image content based on
```
result
```
and
```
violations
```
,不超过50字：
- ```
result=PASS
```
  : Describe in a positive tone
- ```
result=FAIL
```
  (1-2 violations): Gently point out specific issues
- ```
result=FAIL
```
  (3 or more): Objectively summarize the main issues

Image: rank=1 best single image

verbose mode

Quality ranking result (high -> low)
---
Expanded prompt: [expanded | not expanded, using original prompt]
<expanded_prompt>
---
#1 round=<n> result=<PASS|FAIL> violations=<n> [early terminated]
#2 round=<n> result=<PASS|FAIL> violations=<n>
...
---
Time statistics: Total <total>s | Prompt evaluation <t>s | Content analysis <t>s | Prompt expansion <t>s | Image generation <t>s×<n> rounds | VLM review <t>s×<n> rounds
---
Images (sent in rank order)

Call Relationship

Bottom-level dependency:

sn-image-base

→

sn-image-generate

sn-image-recognize

sn-text-optimize

References

```
references/analysis-framework.md
```
- Analysis methodology
```
references/base-prompt.md
```
- Prompt template
```
references/evaluation-standard.md
```
- Evaluation standard
```
references/layout-style-selection.md
```
- Layout and style selection rules
```
references/prompts-expand-system.md
```
- Prompt expansion system prompt
```
references/prompts-critic-system.md
```
- Prompt critic system prompt
```
references/runtime-parameters.md
```
- Runtime parameters

references/structured-content-template.md

- Structured content template

```
references/layouts/<layout>.md
```
- Layout definitions (87 layouts)
```
references/styles/<style>.md
```
- Style definitions (66 styles)

sn-infographic

NPX Install

Tags

SKILL.md Content

sn-infographic

Input Specification

API Configuration

Architecture: Main Agent + Worker Agent

Workflow

Main Agent Workflow

Worker Agent Workflow

Step 0 — Initialization

Step 1 —
`prompts_expand_mode`
Processing

Step 2 — Content Analysis + Layout & Style Selection + Prompt Expansion

Step 3 — Image Generation Loop

Step 4 — Image Quality Ranking

Return Contract

Output Format

friendly mode (default)

verbose mode

Call Relationship

References