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 kangise/ecommerce-ai-skills --skill ecom-socialgit clone --depth 1 https://github.com/kangise/ecommerce-ai-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/kangise/ecommerce-ai-skills/ecom-social)<a href="https://agentmods.dev/skills/kangise/ecommerce-ai-skills/ecom-social"><img src="https://agentmods.dev/badge/skills/kangise/ecommerce-ai-skills/ecom-social/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/kangise/ecommerce-ai-skills/ecom-social"><img src="https://agentmods.dev/badge/skills/kangise/ecommerce-ai-skills/ecom-social.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00060 | $0.00625 |
| Opus 5 | $0.00030 | $0.00313 |
| Sonnet 5 | $0.00012 | $0.00125 |
| Haiku 4.5 | $0.00006 | $0.00063 |
Grade A, and why
ecom-social 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.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Media Skill
When to Use
Create and optimize e-commerce social media content and advertising across platforms. Use for content creation (Reels, Shorts, Stories, Carousel, Pin, 种草笔记), posting strategy, hashtag research, ad copy, influencer collaboration, community management, or cross-channel repurposing.
Method
Step 1: Read Platform Constraints
Read references/constraints.md for platform-level rules. Social media has no numeric constraints in the ontology yet — platform-specific best practices live in each chapter's prompt templates in the playbook.
Step 2: Review Boundaries
Read references/boundaries.md to know when this skill should NOT be used (e.g. no visual story, no local-language capacity, chasing this-month conversion, mechanical reposting).
Step 3: Pick the Prompt
Pick the appropriate prompt from references/playbook.md for your platform and scenario:
- e1 — Instagram/Facebook: Reels, Stories, Carousel, Shopping, Meta Ads, hashtags, influencer collaboration
- e2 — YouTube: SEO keywords, titles, scripts, Shorts, thumbnails, affiliate descriptions
- e3 — 小红书: 种草笔记, SEO, 达人合作, 算法优化, 数据分析
- e4 — Pinterest: SEO, Pins, Idea Pins, Shopping, seasonal calendar, Shopping Ads
- e5 — WhatsApp: 客服 AI, chatbot flows, 复购营销, sales assistant
- e6 — Reddit: community participation, Ads, reputation monitoring, GEO
- e7 — Cross-channel: content adaptation, weekly repurposing, cross-platform analytics, attribution
Step 4: Execute and Verify
Execute the prompt with your data. Use the <自检>/<self_check>/<セルフチェック> self-check block in each prompt to verify output quality before delivering results.
References
- Constraints — Platform rules and limits
- Playbook — Prompt collection (54 prompts, e1–e7)
- Boundaries — When not to use
Templates
Copy-ready prompt templates (in assets/templates/):
What ships with it
8 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.
- assets/templates/template-1-instagram-reels-scripts.md 2.5 KB
- assets/templates/template-2-youtube-video-description.md 2.4 KB
- assets/templates/template-3-xiaohongshu-seeding-note.md 2.9 KB
- assets/templates/template-4-cross-channel-adaptation.md 2.5 KB
- manifest.yaml 1.4 KB
- references/boundaries.md 7.9 KB
- references/constraints.md 981 B
- references/playbook.md 115 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 · 52 lines · 60 tokens per session scan A 478b7004d828
ecom-social is a skill published in the GitHub repository kangise/ecommerce-ai-skills (67 stars, last pushed 5d ago), licensed CC0-1.0. It adds 60 tokens to every session and 625 once invoked, about $0.0003 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 2026-08-30.
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