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.
git clone --depth 1 https://github.com/TheSmokeDev/taskchad-osWrote 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/commands/thesmokedev/taskchad-os/image-node-select)<a href="https://agentmods.dev/commands/thesmokedev/taskchad-os/image-node-select"><img src="https://agentmods.dev/badge/commands/thesmokedev/taskchad-os/image-node-select/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/commands/thesmokedev/taskchad-os/image-node-select"><img src="https://agentmods.dev/badge/commands/thesmokedev/taskchad-os/image-node-select.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00024 | $0.01299 |
| Opus 5.5 | $0.00010 | $0.00520 |
| Sonnet 5.5 | $0.00005 | $0.00260 |
| Haiku 4.5 | $0.00002 | $0.00130 |
Grade A, and why
image-node-select 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 2mo 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Node Select
Workflow ID: $WORKFLOW_ID
Contract
Choose the single strongest prompt template and the nearest worked example cases
for this brief by USING the installed gpt-image-2-style-library skill. This is
the data-driven selection step. The library holds hundreds of worked cases and a
set of structured templates across its categories. You select from that library
dynamically per brief. You do not hardcode a fixed template, and you do not copy
or vendor the library corpus into this repo. The library taxonomy is attributed
to the awesome-gpt-image-2 style library.
Read:
$ARTIFACTS_DIR/image-node-brief.json- Upstream intake JSON:
$intake.output
Use:
- The
gpt-image-2-style-libraryskill and itsreferences/style-library.mdindex. Follow the skill selection order: template category first, then visual style tag, then scene tag, then nearest example cases. Read the reference before choosing, and prefer it over memory when template names, categories, style tags, or case ids matter. - The skill's
gallery.mdand any case BODY it links to are NON-AUTHORITATIVE: those links point at files that are not installed, and this node runs with web search disabled. Never follow them and never reconstruct a case from memory. Case ids are resolved downstream by thegroundnode against a pinned corpus. Emit ids; do not imagine their contents.
Write:
$ARTIFACTS_DIR/image-node-selection.json
Then output ONLY the same JSON object.
How To Select
- Detect the target output from the brief and the
category_hint: UI, poster, infographic, product, brand, photo, illustration, character, scene, history, or document. - Ask the skill to match this brief. Take the strongest template. If the brief
is genuinely split across two categories, pick the one whose worked cases fit
the operator intent best and record the runner-up in
selection_reason. - Capture the chosen
template_idand the nearestexample_case_idsfrom the skill. Emit them as INTEGERS, exactly as the library records them (for example[17, 2, 4], never["case 17"]). These case ids are the concrete anchors thegroundnode resolves and the prompt-pack node builds from; ids the corpus does not carry are reported back as unresolved and are never cited. Do not paste case prompt text here. Reference the ids only. - Record the library
category,style_tags, andscene_tagsthe skill reported for that template. - Assemble a
prompt_structureblock list for the downstream prompt using the skill blocks: subject and task, composition and layout, visual style and materials, text and label requirements, aspect ratio and output format, constraints and negative details.
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.
- 2mo ago First seen · 125 lines · 24 tokens per session scan A d79f91c2db72
image-node-select is a command published in the GitHub repository TheSmokeDev/taskchad-os (28 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 1,299 once invoked, about $0.0001 per session on Opus 5.5. 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.
image-node-select compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| image-node-selectTheSmokeDev/taskchad-os | Command | 28 | 1mo ago | MIT | A | 24 | Claude Code |
| cierremycelium-hq/ai-brain-starter | Command | 50 | yesterday | MIT | A | 26 | Claude Code |
| deconstructmycelium-hq/ai-brain-starter | Command | 50 | yesterday | MIT | A | 17 | Claude Code |
| instinct-importmycelium-hq/ai-brain-starter | Command | 50 | yesterday | MIT | A | 16 | Claude Code |
| patternsmycelium-hq/ai-brain-starter | Command | 50 | yesterday | MIT | A | 20 | Claude Code |
| second-brain-mappingmycelium-hq/ai-brain-starter | Command | 50 | yesterday | MIT | A | 22 | Claude Code |
| setup-vault-typesmycelium-hq/ai-brain-starter | Command | 50 | yesterday | MIT | A | 23 | Claude Code |