visual-refresh

visual-refresh is a skill for Claude Code, Codex from canvora/canvora-mcp. It costs 55 tokens per session (1,224 once invoked), scanned A, original, MIT.

A tool for making targeted changes to visuals already produced by Canvora, such as edits, language versions, alternative variations, and reverting edits.

In plain words
What is it for?
Use it to adjust details, localize a visual, create alternatives, review edit history, or undo a previous edit.
Why use it?
It lets you refine an existing visual while preserving its design history, instead of starting over for every change.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Install

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.

agentmods
npx agentmods add skills/canvora/canvora-mcp/visual-refresh
Any agent
npx skills add canvora/canvora-mcp --skill visual-refresh
Clone the repo
git clone --depth 1 https://github.com/canvora/canvora-mcp

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for visual-refresh

README.md
[![agentmods](https://agentmods.dev/badge/skills/canvora/canvora-mcp/visual-refresh.svg)](https://agentmods.dev/skills/canvora/canvora-mcp/visual-refresh)
Your own site
<a href="https://agentmods.dev/skills/canvora/canvora-mcp/visual-refresh"><img src="https://agentmods.dev/badge/skills/canvora/canvora-mcp/visual-refresh.svg" alt="Measured on agentmods" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,224 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00055 $0.01224
Opus 5 $0.00028 $0.00612
Sonnet 5 $0.00011 $0.00245
Haiku 4.5 $0.00006 $0.00122

Measured 5d ago against content hash dae6a2ba8450, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

visual-refresh 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 5d 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.

skills/visual-refresh/SKILL.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Refining what already exists

The most expensive mistake an agent makes with Canvora is regenerating. A new generation costs full price, throws away a design the user already liked, and comes back different in ways they didn't ask for. Almost every "can you change X" is a 3-credit edit.

Setup and credits: see the canvora skill.

Pick the right operation

The user says Operation Cost
"make the headline bigger", "change 73% to 78%", "warmer colors" edit one output 3
"same thing in Spanish" localize the generation 10 per visual per language
"show me some other options" variations 10 per image (15 per carousel slide)
"go back to the previous one" revert_edit (MCP) free
"what did we change?" get_edit_history (MCP) free
"completely different message" a new generate full price — the only honest case

If you're unsure between edit and variations: does the user want this design fixed, or a different design? Fixed → edit. Different → variations.

Targeted edits

# Preview first (prints Canvora's interpretation, spends nothing)
canvora edit $OUTPUT_ID --prompt "make the headline bigger and increase contrast"

# Execute
canvora edit $OUTPUT_ID --prompt "make the headline bigger and increase contrast" --yes --json
  • Edits act on one output, identified by outputId (not the generation id). Get it from canvora status <generationId> --json.
  • Without --yes you get a preview of what it understood — show that to the user when the instruction was ambiguous.
  • "needs clarification" means the instruction was too vague. Re-prompt with a concrete noun: which text, which element.
  • One change per edit. "Bigger headline and swap the photo and make it blue" is three edits, and each one preserves more of the design than a combined instruction would.

Whole-generation operations

# Every visual in the generation, new language, layout untouched
NEW=$(canvora localize $GENERATION_ID --language es | head -1)
canvora status "$NEW" --json    # repeat every ~30s until completed

# Fresh takes on the same content
NEW=$(canvora variations $GENERATION_ID --count 2 | head -1)
canvora status "$NEW" --json

Read the full file on GitHub · 108 lines

Changes

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.

  1. 5d ago First seen · 108 lines · 55 tokens per session scan A dae6a2ba8450

Subscribe to this mod's changes

visual-refresh is a skill published in the GitHub repository canvora/canvora-mcp (0 stars, last pushed 23d ago), licensed MIT. It adds 55 tokens to every session and 1,224 once invoked, about $0.0003 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 2026-08-31.

Related

Other skills, from other repositories

gpt-image-2-style-library

Choose GPT-Image2 / gpt-image-2 visual styles and industrial prompt templates from the awesome-gpt-image-2 style library. Use when an agent needs to create, rewrite, classify, or improve image-generation prompts with repository-backed templates, categories, style tags, scene tags, pitfalls, and example cases.

freestylefly/awesome-gpt-image-2 · 72 tokens

add-pattern

Use this skill when you learn one or more design pattern(s) in the Langroid (multi) agent framework, and want to make a note for future reference for yourself. Use this either autonomously, or when asked by the user to record a new pattern.

langroid/langroid · 56 tokens

patterns

Design patterns for the Langroid multi-agent LLM framework. Covers agent configuration, tools, task control, and integrations.

langroid/langroid · 26 tokens

guizang-ppt-skill

生成横向翻页网页 PPT(单 HTML 文件),含 WebGL 背景、章节幕封、数据大字报、图片网格等模板。提供两种风格:① "电子杂志 × 电子墨水"(衬线 + 流体背景 + 暖色) ② "瑞士国际主义"(无衬线 + 网格点阵 + IKB/柠檬黄/柠檬绿/安全橙高亮)。当用户需要制作分享 / 演讲 / 发布会风格的网页 PPT,或提到"杂志风 PPT"、"瑞士风 PPT"、"Swiss Style"、"horizontal swipe deck"时使用。.

proma-ai/Proma · 161 tokens

proma-coach

Proma 使用顾问,主动把用户在 Proma/Agent/Skill/Chat 工具/项目里的摩擦、疑惑、重复解释和低效流程,转成更顺手的使用方式或合适的知识维护动作。触发要积极:用户表达不满、困惑、重复提醒、"为什么没用/不会自动/又要我说"、"算了,我自己来"、"你上次不是说..."、"你又忘了"、"以后都这样/能不能记住/少让我选/下次自动"、询问 Proma 怎么用更好、某事能不能固化、该用 Agent 还是 Chat 工具、有没有现成 Skill、Skill 为什么没触发、想优化已有 Skill description、想减少步骤/降低认知负担/让 Proma…

proma-ai/Proma · 386 tokens

skill-creator

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

proma-ai/Proma · 64 tokens