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 SubmergeDan/Fact-Shield --skill evidence-reviewgit clone --depth 1 https://github.com/SubmergeDan/Fact-ShieldWrote 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/submergedan/fact-shield/evidence-review)<a href="https://agentmods.dev/skills/submergedan/fact-shield/evidence-review"><img src="https://agentmods.dev/badge/skills/submergedan/fact-shield/evidence-review/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/submergedan/fact-shield/evidence-review"><img src="https://agentmods.dev/badge/skills/submergedan/fact-shield/evidence-review.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 | $0.00039 | $0.00313 |
| Opus 5.5 | $0.00016 | $0.00125 |
| Sonnet 5.5 | $0.00008 | $0.00063 |
| Haiku 4.5 | $0.00004 | $0.00031 |
Grade A, and why
evidence-review 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 2mo 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
证据复核技能
当用户要求对研究结论、底稿或一组主张做证据复核时使用本技能。
执行步骤
- 把待复核的每条主张拆成独立条目并编号。
- 对每条主张回查底稿/证据中对应的原文依据；证据不足时明确标注“证据不足”。
- 按以下维度输出核验意见：
- 主张原文
- 依据来源（链接或文档位置）
- 来源可信度：高 / 中 / 低，并说明理由
- 是否存在冲突证据
- 建议结论：保留 / 修改 / 排除 / 补充取证
- 汇总所有条目结论，优先列出需要人工复核的疑点。
- 全程不臆造来源；找不到依据时如实说明。
注意事项
- 本技能为纯指令型技能：按上述步骤使用已有工具（web、memory、document 等）完成复核。
- 输出使用中文，先给汇总结论，再给逐条明细。
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.
- 2mo ago First seen · 28 lines · 39 tokens per session scan A cda04c0504fd
evidence-review is a skill published in the GitHub repository SubmergeDan/Fact-Shield (12 stars, last pushed 3mo ago), licensed MIT. It adds 39 tokens to every session and 313 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 2026-08-30.
evidence-review compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| evidence-reviewSubmergeDan/Fact-Shield | Skill | 12 | 3mo ago | MIT | A | 39 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |