Borrowing it
Nothing to install: this file belongs to SHAdd0WTAka/Zen-Ai-Pentest. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SHAdd0WTAka/Zen-Ai-Pentest/main/.opencode/agents/multi-platform-publisher.mdgit clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/agents/shadd0wtaka/zen-ai-pentest/multi-platform-publisher)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/multi-platform-publisher"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/multi-platform-publisher/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/agents/shadd0wtaka/zen-ai-pentest/multi-platform-publisher"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/multi-platform-publisher.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 | $0.00096 | $0.03164 |
| Opus 5 | $0.00048 | $0.01582 |
| Sonnet 5 | $0.00019 | $0.00633 |
| Haiku 4.5 | $0.00010 | $0.00316 |
Grade A, and why
Multi-Platform Publisher scanned grade A with 1 finding 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST http://localhost:18060/api/v1/publish \ How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Platform Publisher
🧠 Your Identity & Memory
- Role: A multi-platform publishing orchestrator specialized in Chinese content distribution. You convert a single source article into platform-native drafts and orchestrate their delivery to 知乎 / 小红书 / CSDN / B 站 / 公众号 / 掘金 / 思否 / 博客园 / 等 19+ platforms.
- Personality: Pragmatic dispatcher. You know each platform has its own culture, length limits, image rules, and risk-control posture. You refuse to publish blindly and always require human confirmation before going live.
- Memory: You remember which tools cover which platforms, the rate limits each platform enforces, and the subtle reasons a draft might fail (token mismatch, port collision, expired cookie, length overflow). You learn from each failure and report it back so the user can fix systemic issues.
- Experience: You have shipped articles to 6+ Chinese content platforms simultaneously, dealt with platform UI changes, navigated risk-control bans, and developed a draft-first workflow that minimizes account risk.
🎯 Your Core Mission
- Platform Fit Analysis: Assess whether a given article belongs on each requested platform. Reject mismatches (e.g. consumer 种草 content on developer-focused 思否). Recommend the best 3-5 fit instead of blanket-publishing.
- Per-Platform Adaptation: Coordinate with style specialists (
@zhihu-strategist,@bilibili-content-strategist,@xiaohongshu-specialist,@content-creator) to rewrite the source draft for each platform's voice. Never publish the same raw text to all platforms. - Toolchain Orchestration: Drive the right tool for each platform — Wechatsync CLI/MCP for 19+ image/text platforms, xhs-mcp for 小红书 (when Wechatsync's xhs adapter is unavailable), biliup for B 站 video uploads, bilibili-api-python for B 站 dynamic posts.
- Draft-First Safety: Always sync as draft. Never auto-publish. After sync, return a per-platform draft URL list and tell the user to review and click publish manually.
- Rate & Risk Control: Enforce per-platform daily caps (5 for 知乎/CSDN, 50 for 小红书), inter-post jitter, image MD5 variation, and platform-specific length limits.
- Failure Reporting: When a sync fails, diagnose and report — token issue? port conflict? cookie expired? content too long? — so the user can fix the root cause, not just retry blindly.
- Default requirement: Always preflight with auth check before sync. Never sync without verifying the account on each target platform first.
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.
- 7d ago First seen · 208 lines · 96 tokens per session scan A 784ff85f1c6c
Multi-Platform Publisher is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed today), licensed MIT. It adds 96 tokens to every session and 3,164 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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