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 lovstudio/skills --skill distribution-surfacesgit clone --depth 1 https://github.com/lovstudio/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/lovstudio/skills/distribution-surfaces)<a href="https://agentmods.dev/skills/lovstudio/skills/distribution-surfaces"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/distribution-surfaces/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/lovstudio/skills/distribution-surfaces"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/distribution-surfaces.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 | $0.00059 | $0.00540 |
| Opus 5.5 | $0.00024 | $0.00216 |
| Sonnet 5.5 | $0.00012 | $0.00108 |
| Haiku 4.5 | $0.00006 | $0.00054 |
Grade A, and why
lov-atom-distribution-surfaces 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 24d 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.
What it actually says
功能多端分发 · Feature Distribution
把一个已验证的 SDK 适配为适用的 CLI、REST API 与 Agent Skill。adapter 负责输入翻译、 传输、权限和呈现，不复制业务规则。
Triggers
Activate when
- “基于共享 SDK 同时补 CLI、REST API 和 Agent Skill。”
- “检查这些 surface 是否真的行为一致。”
- “Build CLI, API and Agent surfaces for this atom.”
Do not activate when
- 目标没有已批准的 contract 或可调用核心；先执行 feature-contract 与 core-sdk。
Workflow (MANDATORY)
- 从 manifest 选择真正适用的 surface，不为空形态造目录。
- CLI 使用任务型命令、稳定 JSON envelope、明确退出码、
doctor与capabilities。 - REST API 使用版本化 request/response schema、OpenAPI、认证边界、幂等和结构化错误。
- Agent Skill/tool schema 复用 contract 字段；先合并 Profile Preset，再只追问结果关键缺失值。
- 所有 adapter 只调用公开 SDK，不复制校验、默认值或状态迁移。
- 用同一 acceptance vector 分别调用至少两个 surface，规范化后比较业务结果和副作用。
- 回读命令、OpenAPI 或工具注册状态；远端发布仍需独立授权与渠道证据。
Output Contract
返回每个 surface 的入口、artifact、schema、示例、测试命令和验证层级；明确
implemented、verified 与 released 的区别。
Dependencies
已批准的 Feature Contract 与可调用 Core SDK；其余依赖由目标项目决定。
What ships with it
12 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.
- cases/cases.json 750 B
- CHANGELOG.md 298 B
- pricing-card.yaml 537 B
- README.md 450 B
- references/skill-card-standard.md 660 B
- references/skill-composition.md 752 B
- references/user-profile.md 2.7 KB
- scripts/profile_store.py 8.3 KB runs code
- scripts/validate_skill.py 26 KB runs code
- skill-card.md 1.7 KB
- skill-card.yaml 2.8 KB
- skill.yaml 1.6 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.
- 24d ago First seen · 58 lines · 59 tokens per session scan A 80eb3b0d7a8b
lov-atom-distribution-surfaces is a skill published in the GitHub repository lovstudio/skills (72 stars, last pushed 6d ago), licensed MIT. It adds 59 tokens to every session and 540 once invoked, about $0.0002 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 2026-09-26.
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