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 bestagentkits/agency-skills --skill baoyu-xhs-imagesgit clone --depth 1 https://github.com/bestagentkits/agency-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/bestagentkits/agency-skills/baoyu-xhs-images)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/baoyu-xhs-images"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/baoyu-xhs-images/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/bestagentkits/agency-skills/baoyu-xhs-images"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/baoyu-xhs-images.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 | $0.00100 | $0.07316 |
| Opus 5 | $0.00050 | $0.03658 |
| Sonnet 5 | $0.00020 | $0.01463 |
| Haiku 4.5 | $0.00010 | $0.00732 |
Grade A, and why
baoyu-xhs-images 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 12d 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.
This is a copy
100% identical to baoyu-xhs-images — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 485 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Card Series Generator
Break down complex content into eye-catching image card series with multiple style options.
User Input Tools
When this skill prompts the user, follow this tool-selection rule (priority order):
- Prefer built-in user-input tools exposed by the current agent runtime — e.g.,
AskUserQuestion,request_user_input,clarify,ask_user, or any equivalent. - Fallback: if no such tool exists, emit a numbered plain-text message and ask the user to reply with the chosen number/answer for each question.
- Batching: if the tool supports multiple questions per call, combine all applicable questions into a single call; if only single-question, ask them one at a time in priority order.
Concrete AskUserQuestion references below are examples — substitute the local equivalent in other runtimes.
Image Generation Tools
When this skill needs to render an image, resolve the backend in this order:
- Current-request override — if the user names a specific backend in the current message, use it.
- Saved preference — if
EXTEND.mdsetspreferred_image_backendto a backend available right now, use it. - Auto-select (when the preference is
auto, unset, or the pinned backend isn't available):- Codex (
imagegen) — first, inspect your available-skills / tool inventory. If a skill namedimagegenis listed, you are running inside Codex and MUST use it: invoke via theSkilltool withskill: "imagegen", passing the saved prompt file's content (plus output path and aspect ratio per Codeximagegen's own args). Codeximagegenis the official raster backend in that runtime and outranks any non-native skill (e.g.,baoyu-image-gen) unless the user has explicitly pinned a differentpreferred_image_backend. - Codex via
codex exec(codex-imagegen) — if the current runtime exposes no nativeimagegenskill but thecodexCLI is onPATHwith an activecodex login, route throughbaoyu-image-gen --provider codex-cli(preferred), or — if baoyu-image-gen is unavailable — invoke the bundled wrapper directly. Details, parameters, and the runtime-discovery procedure live in references/codex-imagegen.md — load that file only when this branch is selected. - Cursor (
GenerateImage) — if the runtime exposes a nativeGenerateImagetool, you are running inside Cursor and it outranks any non-native skill the same way Codeximagegendoes. Two hard caveats: (a) it has no aspect-ratio parameter — state the target aspect ratio / dimensions explicitly in the prompt text passed asdescription; (b) it does not accept an output directory — it saves to a tool-managed location, so after generation copy/move the file to the skill's expected output path (e.g.,outputs/.../NN-xxx.png). Reference images go inreference_image_paths. - Other runtime-native tools — if the runtime exposes a different native image tool (e.g., Hermes
image_generate), use it the same way. - Otherwise, if exactly one non-native backend is installed (e.g.,
baoyu-image-gen), use it. - Otherwise (multiple non-native backends with no runtime-native tool), ask the user once — batch with any other initial questions.
- Codex (
- If none are available, tell the user and ask how to proceed.
What ships with it
29 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 205 B
- references/codex-imagegen.md 3.6 KB
- references/config/first-time-setup.md 3.4 KB
- references/config/preferences-schema.md 3.9 KB
- references/config/watermark-guide.md 1.9 KB
- references/confirmation.md 3.8 KB
- references/elements/canvas.md 3.6 KB
- references/elements/decorations.md 4.9 KB
- references/elements/image-effects.md 2.9 KB
- references/elements/typography.md 3.2 KB
- references/palettes/macaron.md 1.4 KB
- references/palettes/neon.md 1.2 KB
- references/palettes/warm.md 1.2 KB
- references/presets/bold.md 1.5 KB
- references/presets/chalkboard.md 2.3 KB
- references/presets/cute.md 1.5 KB
- references/presets/fresh.md 1.5 KB
- references/presets/minimal.md 1.4 KB
- references/presets/notion.md 1.6 KB
- references/presets/pop.md 1.5 KB
- references/presets/retro.md 1.4 KB
- references/presets/screen-print.md 2.6 KB
- references/presets/sketch-notes.md 3.1 KB
- references/presets/study-notes.md 2.9 KB
- references/presets/warm.md 1.4 KB
- references/style-presets.md 1.9 KB
- references/workflows/analysis-framework.md 7.1 KB
- references/workflows/outline-template.md 6.0 KB
- references/workflows/prompt-assembly.md 10 KB
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.
- 12d ago First seen · 485 lines · 100 tokens per session scan A 17bf5498641c
baoyu-xhs-images is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 100 tokens to every session and 7,316 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to baoyu-xhs-images, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
exposure-risk-quantification
FAIR-aligned exposure quantification: turns a pile of recon findings into a defensible 0-100 + A-F org risk score (Likelihood x Impact, three ownership-aware factors: exposure/threat/impact), an ownership + proof demotion cap so unproven or weakly-owned findings can't inflate the number, a $-denominated FAIR…
osint-methodology
Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 6-stage recon pipeline (seed → asset expansion → enrichment → exposure analysis → convergence → operator-armed active validation) with connector-resilience and stage-vs-gating discipline, asset-graph…
bencium-impact-designer
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics. Based on Anthropic's Frontend Designer Skill.
file-processing
Process and analyze CSV, JSON, and text files with data transformation, cleaning, analysis, and visualization capabilities.
design-audit
Premium UI/UX design audit and refinement skill. Conducts systematic visual audits of existing apps and produces phased, implementation-ready design plans. Use this skill whenever the user asks to audit a UI, improve an app's visual design, make an interface feel more polished or premium, review design consistency…
bencium-code-conventions
Bence's code style, tech stack, and workflow conventions.