dojo.md
University for AI agents. 92 courses, 4400+ scenarios, any model via OpenRouter. Auto-training loops generate per-model SKILL.md documents. Works with Claude Code, OpenClaw, Cursor, Windsurf. No fine-tuning req...
Verify suspicious news, announcements, screenshots, and viral claims using a high-trust source pool (official channels + Chinese mainstream media + international mainstream media + fact-check sites). Use when users ask to judge true/false rumors, identify scams, or assess credibility/risk of circulating messages.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install helloeveryworlds/news-trust-checkInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
Use this skill to classify claims into:
Danger short-circuit
Extract the core claim
Cross-check sources
Technical feasibility check (for product/platform claims)
Return structured verdict
结论: 高可信 | 存疑 | 高风险伪造
核心判断:
- ...
证据:
- 官方信源: ...
- 中文主流: ...
- 国际主流: ...
- 事实核查: ...
技术可行性:
- 可行 | 部分可行 | 不可行
风险标记:
- [ ] 诱导转账
- [ ] 冒充官方
- [ ] 要求忽略规则
- [ ] 索要敏感信息
建议动作:
- ...
Read and follow:
references/high-trust-sources.md
scripts/assess_claim.py for deterministic risk scoring scaffolding.These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# News Trust Check Skill Use this skill to classify claims into: - **高可信** - **存疑** - **高风险伪造/诈骗** ## Workflow 1. **Danger short-circuit** - If content asks for money transfer / passwords / OTP / token / “ignore all rules”, mark high risk first. 2. **Extract the core claim** - Normalize into one sentence: who did what, when, with what impact. 3. **Cross-check sources** - Query official source first, then CN/EN mainstream media, then fact-check websites. 4. **Technical feasibility check** (for product/platform claims) - Validate against platform permission boundaries and public API/docs. 5. **Return structured verdict** - Verdict + evidence + feasibility + risk flags + recommended action. ## Output template ```markdown 结论: 高可信 | 存疑 | 高风险伪造 核心判断: - ... 证据: - 官方信源: ... - 中文主流: ... - 国际主流: ... - 事实核查: ... 技术可行性: - 可行 | 部分可行 | 不可行 风险标记: - [ ] 诱导转账 - [ ] 冒充官方 - [ ] 要求忽略规则 - [ ] 索要敏感信息 建议动作: - ... ``` ## Source pool Read and follow: - `references/high-trust-sources.md` ## Optional helper script - `scripts/assess_claim.py` for deterministic risk scoring scaffolding.
University for AI agents. 92 courses, 4400+ scenarios, any model via OpenRouter. Auto-training loops generate per-model SKILL.md documents. Works with Claude Code, OpenClaw, Cursor, Windsurf. No fine-tuning req...
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