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 serejaris/kimi-skills --skill cross-platform-adaptergit clone --depth 1 https://github.com/serejaris/kimi-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/serejaris/kimi-skills/cross-platform-adapter)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/cross-platform-adapter"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/cross-platform-adapter/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/serejaris/kimi-skills/cross-platform-adapter"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/cross-platform-adapter.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 | $0.00087 | $0.02839 |
| Opus 5 | $0.00044 | $0.01419 |
| Sonnet 5 | $0.00017 | $0.00568 |
| Haiku 4.5 | $0.00009 | $0.00284 |
Grade A, and why
cross-platform-adapter 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 9d 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Platform Adapter — One Source, Five Platforms
Take any long-form content (blog post, report, speech, internal doc, newsletter) and produce tailored versions for LinkedIn, Twitter/X, WeChat Official Accounts, Zhihu, and Slack. Each version respects the platform's character limits, audience expectations, formatting conventions, and cultural context.
When to Use
- User has a piece of content and wants to distribute it across multiple platforms
- User asks to "repurpose this," "adapt this for LinkedIn/Twitter/WeChat/Zhihu/Slack," or "make platform versions"
- User wants to maximize reach from a single content investment
Input
The user provides source content (article, blog post, report, talking points, etc.) and optionally specifies:
- Target platforms (default: all five)
- Target audience per platform (if different from general)
- Language preference per platform (Chinese for WeChat/Zhihu, English for LinkedIn/Twitter/Slack — or user-specified)
- Tone override (e.g., "keep LinkedIn more casual than usual")
- Specific CTA per platform
SOP — Step-by-Step Process
Length scaling: The per-platform character recommendations below assume a medium-length source (~500–1,500 words). For shorter sources, scale down proportionally — a 300-word blog post should NOT be padded to hit 1,500 Chinese characters on WeChat. Quality over length.
Step 1: Analyze the Source Content
Read the full input and extract:
- Core message — the single main idea in one sentence
- Key supporting points — 3–7 distinct arguments, data points, or stories
- Target audience — who benefits from this content
- Content type — educational, opinion, announcement, case study, how-to, thought leadership
- Quotable moments — short, punchy phrases that can stand alone
- Data and evidence — statistics, research citations, concrete examples
- Original language — note whether the source is in English, Chinese, or mixed
Step 2: Platform Analysis Matrix
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.
- 9d ago First seen · 244 lines · 87 tokens per session scan A 118a5df1148e
cross-platform-adapter is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 87 tokens to every session and 2,839 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 2026-09-03.
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