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 WiseWong6/wise-skills --skill blue-postergit clone --depth 1 https://github.com/WiseWong6/wise-skillsWrote 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/wisewong6/wise-skills/blue-poster)<a href="https://agentmods.dev/skills/wisewong6/wise-skills/blue-poster"><img src="https://agentmods.dev/badge/skills/wisewong6/wise-skills/blue-poster/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/wisewong6/wise-skills/blue-poster"><img src="https://agentmods.dev/badge/skills/wisewong6/wise-skills/blue-poster.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 | $0.00095 | $0.01415 |
| Opus 5 | $0.00048 | $0.00707 |
| Sonnet 5 | $0.00019 | $0.00283 |
| Haiku 4.5 | $0.00010 | $0.00142 |
Grade A, and why
blue-poster 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blue Poster
Generate each final poster from the original source image in one image-generation call. Default to a complete full-design poster. Never create a base image and then add effects in a second content-generation stage.
Required tool policy
- Detect the host environment before generation and use only an image-generation capability built into that host.
- In Codex, use only the Codex host built-in
image_gen.imagegentool. Never invoke, install, or fall back to a third-party image skill, Ark, Doubao, Gemini, a local model, CLI, or API workflow. - Outside Codex, use the current model or platform's own built-in image-generation capability when it is available, such as GPT's native image generation in ChatGPT or Doubao's native image generation in Doubao. Do not call another provider's API, CLI, local model, or third-party image skill as a fallback.
- If the current host has no built-in image-generation capability, or that capability fails, stop and report the blocking reason.
- Treat style reference images as browse-only. Never pass them to image generation.
MPO source normalization
.mpois an accepted source container. MPO is normalized before generation; it is not a new poster output mode.- Inspect frames with
python3 scripts/extract_mpo.py <source.mpo> --list. - Unless the user chooses another frame, extract frame
0, the primary JPEG, and use that normalized JPEG as the only image input to the selected host-native image generator. - Do not merge stereo frames or invent depth semantics. If the user asks for left/right or multi-view treatment, clarify the selected frame before generation.
- This local extraction step only unwraps the source container. It does not generate, alter, crop or stylize image content.
Generate the default full poster
- Require one source image. If it is a local file, inspect it with
view_imagebefore generation. - If the user names
S01–S11, honor it. Otherwise read style-selection.md, analyze the source semantics, and select exactly one style. - Read prompt-full-design.md and the selected block in style-programs.md.
- Replace
{{EFFECT_PROGRAM}}with exactly one complete style block. Do not leave placeholders or merge multiple styles. - Call the selected host-native image generator once with the original source as the only image input. In Codex, this means
image_gen.imagegen. State that the source is a semantic and structural source, not a style reference. - Save the selected image under the caller's current workspace at
outputs/blue-poster/<source>-full-Sxx-vN.png, unless the user supplied another destination. Never overwrite an existing file. - Save the exact final prompt beside it as
<source>-full-Sxx-vN.prompt.md. - Run
python3 scripts/validate_output.py <image> --mode full. If it fails, retry once from the original source with the same style and a stronger native 3:4 instruction. Never stretch or crop to hide a ratio failure. If the retry fails, report it as unaccepted. - Perform only a lightweight check on the returned image: the subject remains recognizable, the whole page is designed, no photographic window remains, and the full-mode carrier contract is visible—3–6 active regions, no more than two clusters, no isolated small rectangle, a continuous subject corridor and at least 40% quiet paper. Do not start a browser or create extra screenshots.
- Report the selected style ID and name, one concrete selection reason, image path, prompt path, validation result, and catalog path.
What ships with it
50 files 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.
- agents/openai.yaml 239 B
- assets/examples/E01-FULL-S08-material-tectonics.webp 639 KB
- assets/examples/E01-SOURCE-tokyo-tower.jpg 5777 KB
- assets/examples/E01-SPLIT-S08-material-tectonics.webp 407 KB
- assets/social/xiaohongshu-qr.jpg 172 KB
- assets/style-references/secondary/R-S08-B-material-tectonics-aesthetic-only-2x3.webp 445 KB
- assets/style-references/split-1x1/R-S01-A-blue-exposure-laboratory-split.webp 313 KB
- assets/style-references/split-1x1/R-S02-A-optical-field-array-split.webp 407 KB
- assets/style-references/split-1x1/R-S03-A-edgeloom-effect-sampler-split.webp 441 KB
- assets/style-references/split-1x1/R-S04-A-quiet-effect-cabinet-split.webp 363 KB
- assets/style-references/split-1x1/R-S05-A-ink-grid-interference-split.webp 314 KB
- assets/style-references/split-1x1/R-S06-A-cyanotype-optical-plates-split.webp 376 KB
- assets/style-references/split-1x1/R-S07-A-registration-weather-split.webp 341 KB
- assets/style-references/split-1x1/R-S08-A-material-tectonics-split.webp 407 KB
- assets/style-references/split-1x1/R-S09-A-monochrome-data-garden-split.webp 340 KB
- assets/style-references/split-1x1/R-S10-A-selected-synthesis-split.webp 400 KB
- assets/style-references/split-1x1/R-S11-A-cyanotype-ma-registry-split.webp 288 KB
- assets/style-references/tokyo-tower-full/R-S01-A-blue-exposure-laboratory-full.webp 644 KB
- assets/style-references/tokyo-tower-full/R-S02-A-optical-field-array-full.webp 592 KB
- assets/style-references/tokyo-tower-full/R-S03-A-edgeloom-effect-sampler-full.webp 635 KB
- assets/style-references/tokyo-tower-full/R-S04-A-quiet-effect-cabinet-full.webp 590 KB
- assets/style-references/tokyo-tower-full/R-S05-A-ink-grid-interference-full.webp 513 KB
- assets/style-references/tokyo-tower-full/R-S06-A-cyanotype-optical-plates-full.webp 622 KB
- assets/style-references/tokyo-tower-full/R-S07-A-registration-weather-full.webp 566 KB
- assets/style-references/tokyo-tower-full/R-S08-A-material-tectonics-full.webp 639 KB
- assets/style-references/tokyo-tower-full/R-S09-A-monochrome-data-garden-full.webp 540 KB
- assets/style-references/tokyo-tower-full/R-S10-A-selected-synthesis-full.webp 631 KB
- assets/style-references/tokyo-tower-full/R-S11-A-cyanotype-ma-registry-full.webp 520 KB
- assets/web/E01-SPLIT-S08-material-tectonics.webp 134 KB
- assets/web/R-S01-A-blue-exposure-laboratory-full.webp 202 KB
- assets/web/R-S02-A-optical-field-array-full.webp 200 KB
- assets/web/R-S03-A-edgeloom-effect-sampler-full.webp 242 KB
- assets/web/R-S04-A-quiet-effect-cabinet-full.webp 191 KB
- assets/web/R-S05-A-ink-grid-interference-full.webp 170 KB
- assets/web/R-S06-A-cyanotype-optical-plates-full.webp 207 KB
- assets/web/R-S07-A-registration-weather-full.webp 185 KB
- assets/web/R-S08-A-material-tectonics-full.webp 225 KB
- assets/web/R-S09-A-monochrome-data-garden-full.webp 184 KB
- assets/web/R-S10-A-selected-synthesis-full.webp 210 KB
- assets/web/R-S11-A-cyanotype-ma-registry-full.webp 147 KB
- LICENSE 1.0 KB
- README_EN.md 14 KB
- README.md 13 KB
- references/prompt-full-design.md 4.4 KB
- references/prompt-split-1x1.md 4.4 KB
- references/style-catalog.html 7.2 KB
- references/style-programs.md 7.1 KB
- references/style-selection.md 2.5 KB
- scripts/extract_mpo.py 9.4 KB runs code
- scripts/validate_output.py 2.9 KB runs code
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 · 71 lines · 95 tokens per session scan A 67ad95b697c5
blue-poster is a skill published in the GitHub repository WiseWong6/wise-skills (6 stars, last pushed 2d ago), licensed MIT. It adds 95 tokens to every session and 1,415 once invoked, about $0.0005 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.
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