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 Niall-Young/Canvasight --skill canvasight-imagegengit clone --depth 1 https://github.com/Niall-Young/CanvasightWrote 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/niall-young/canvasight/canvasight-imagegen)<a href="https://agentmods.dev/skills/niall-young/canvasight/canvasight-imagegen"><img src="https://agentmods.dev/badge/skills/niall-young/canvasight/canvasight-imagegen/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/niall-young/canvasight/canvasight-imagegen"><img src="https://agentmods.dev/badge/skills/niall-young/canvasight/canvasight-imagegen.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 | $0.00072 | $0.00888 |
| Opus 5 | $0.00036 | $0.00444 |
| Sonnet 5 | $0.00014 | $0.00178 |
| Haiku 4.5 | $0.00007 | $0.00089 |
Grade A, and why
canvasight-imagegen 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 11d 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 — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Canvasight Image Generation
Generate with the system $imagegen Skill, then import the accepted bitmap outputs through Canvasight's atomic generated-image tool. Never hand-edit .scatter, pass an unmanaged path to write_canvasight_graph, or substitute browser automation.
Workflow
- Read the active task's exact
CODEX_THREAD_ID. - Reuse a Canvasight native widget only when this task already has verified fullscreen ready evidence. Otherwise follow
canvasight-open: callopen_canvasightwith thatthreadId, preserve itssessionIdandopenAttemptId, then callawait_canvasight_widget_ready. Stop before generation unless the result is verifiedstatus: "ready"with React, project hydration, rendered canvas, and visible non-zero canvas evidence. - Call
get_canvasight_graph_contextwith the samethreadId. Preserve itsprojectPath,contextId,documentRevision, and active Page identity before starting image generation. Later Page switches must not retarget the import. - Invoke the system
$imagegenSkill. Use its built-in tool path by default, one call per requested final image or variant. Follow its transparency, inspection, retry, and CLI-consent rules exactly. - Keep only outputs that pass imagegen inspection. Call
add_canvasight_generated_imageswith:- the exact
threadIdand capturedprojectPath; - the captured
contextIdanddocumentRevisionasexpectedRevision; - one stable unique
clientMutationIdfor this exact batch, reused only for retries; - one
{ "path", "title"? }entry per final image, in the requested display order.
- the exact
- Treat
written,merged, andconflict-copyas successful imports. Report the target Page, created node count, managed project-relative paths, final prompt set, and whether imagegen used the built-in or user-approved CLI path.
Product-design option flow
When the request is to explore a product or UI design before frontend implementation:
- Resolve the target surface, intended user, outcome, viewport, and hard constraints before generation.
- Unless the user specifies another count, generate exactly three independent UI images. Each image is one distinct direction with a meaningfully different hierarchy, layout, or interaction model; never combine multiple directions into one image.
- Import all accepted options as separate Asset Nodes in their visible result order, then stop so the user can connect the preferred image into the ordinary canvas flow. Do not choose a direction or start implementation on the user's behalf.
- Run continues to start only from executable Task or Group surfaces. For this option flow, run the ordinary Task flow: connected image Assets travel with that Task exactly like node attachments did, while generated images outside its reachable flow are unrelated. An Asset never runs independently, and no separate selection marker or persisted role is needed.
- If the project exposes a matching product-design or image-to-code Skill, it may be named visibly in the downstream Task body. Do not persist a hidden Skill assignment or assume that an unavailable external Skill is installed.
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.
- 11d ago First seen · 41 lines · 72 tokens per session scan A 603a88f7c673
canvasight-imagegen is a skill published in the GitHub repository Niall-Young/Canvasight (210 stars, last pushed 14d ago), licensed MIT. It adds 72 tokens to every session and 888 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 2026-08-30.
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