lov-fact-check

lov-fact-check is a skill for Claude Code from lovstudio/skills. It costs 111 tokens per session (2,293 once invoked), scanned A, original, MIT.

A fact-checking workflow for testing whether a claim is true using original sources and independent evidence.

In plain words
What is it for?
Use it to verify technical claims, news or business statements, source-check an answer, or investigate a question with an evidence trail.
Why use it?
It separates confirmed facts from reasonable inferences and unresolved points, reducing the risk of relying on memory or a single search result.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: names the AskUserQuestion tool.

Part of the general plugin — 43 skills shipped together

Good fit Use it to verify technical claims, news or business statements, source-check an answer, or investigate a question with an evidence trail.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lovstudio/skills/fact-check
Install

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.

Any agent
npx skills add lovstudio/skills --skill fact-check
Clone the repo
git clone --depth 1 https://github.com/lovstudio/skills

Made for: Claude Code.

Or install general, the plugin that ships this one along with the rest of its 43 skills.

Wrote 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.

agentmods badge for lov-fact-check

README.md
[![agentmods](https://agentmods.dev/badge/skills/lovstudio/skills/fact-check/github.svg)](https://agentmods.dev/skills/lovstudio/skills/fact-check)
Your own site
<a href="https://agentmods.dev/skills/lovstudio/skills/fact-check"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/fact-check/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.

agentmods 80×15 button for lov-fact-check

Your own site · 80×15
<a href="https://agentmods.dev/skills/lovstudio/skills/fact-check"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/fact-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 111 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,293 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00111 $0.02293
Opus 5.5 $0.00044 $0.00917
Sonnet 5.5 $0.00022 $0.00459
Haiku 4.5 $0.00011 $0.00229

Measured 26d ago against content hash e21f08f6bdd1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

lov-fact-check 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 26d 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.

skills/fact-check/SKILL.md · 183 lines

How it starts

The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.

事实核查员 · Fact Checker

用这个 skill 回答事实真伪问题时,不要直接凭记忆下结论。你要模拟一个认真真人的尽调路径:先澄清命题,拆成可验证子问题,优先查一手资料,再用独立来源交叉验证,最后把证据、推断和不确定性分开写清楚。

When to Use

  • 用户问某个事实是否成立:例如“Tauri 程序不支持 GitHub webhook handler 吗?”
  • 用户要求“帮我确认”“查证一下”“这是真的吗”“有没有官方依据”。
  • 用户给出一段文章、截图、传言、技术判断、商业说法或历史事实,希望判断可信度。
  • 用户明确使用 English trigger phrases: fact check, verify this, source check, is this true, due diligence.

Core Principle

事实校验不是搜索答案,而是还原“一个谨慎的人会如何确认答案”。

  • 先确认问题含义,再确认答案。
  • 优先找一手资料:官方文档、源码、标准、法规、公告、论文、原始数据、公司/项目维护者声明。
  • 对技术问题,优先官方文档、源码、release notes、issue/PR,再看博客和社区问答。
  • 对会随时间变化的问题,必须联网核验,并写明检索日期。
  • 把“证据显示”“合理推断”“仍未确认”分开。
  • 不要用单一搜索结果做强结论。至少尝试找一个反例或边界条件。

Workflow (MANDATORY)

You MUST follow these steps in order:

Step 1: Restate the Claim

把用户的问题改写成一个可验证命题,并指出关键歧义。 如果关键歧义会改变检索方向,先用 AskUserQuestion 问一个澄清问题; 否则继续执行,不要为了形式而打断用户。

Example:

用户问:“Tauri 程序不支持 GitHub webhook handler 吗?”

可验证命题:
1. Tauri 桌面应用本身是否内置 HTTP server / webhook handler 能力?
2. Tauri 应用能否通过插件、Rust sidecar、本地端口或外部服务接收 GitHub webhook?
3. “不支持”指官方不推荐、没有内置 API,还是技术上完全不能做?

Step 2: Choose Evidence Plan

根据问题类型选择证据路径。

Claim Type Primary Sources Secondary Sources
技术能力 / API 支持 官方文档、源码、release notes、issue/PR、维护者讨论 Stack Overflow、博客、示例项目
产品 / 公司事实 官网、公告、SEC/工商资料、官方社媒、新闻稿 媒体报道、数据库
法律 / 政策 / 标准 法规原文、政府官网、标准正文 律所解读、行业文章
学术 / 医疗 / 科学 论文原文、系统综述、指南、数据集 科普文章、新闻报道
历史 / 人物 / 事件 原始档案、采访原文、时间线材料 百科、二手转述

Step 3: Search Like Due Diligence

执行检索时必须覆盖这些动作:

  1. 查官方来源:项目官网、官方文档、GitHub repo、release notes、官方 FAQ。
  2. 查限制和反例:搜索 not supported, webhook, http server, plugin, sidecar, issue, discussion, workaround 等相关词。
  3. 查时间线:确认资料是否过期,尤其技术栈、法规、产品功能。
  4. 查独立来源:至少一个非官方来源,用来发现官方文档没有覆盖的实践或争议。
  5. 记录证据质量:一手、二手、社区经验、推断。

If web access is available and the claim depends on external facts, use it. Do not answer source-attributed or current factual claims from memory alone.

Step 4: Evaluate

用下面的判断框架组织结论:

  • Direct evidence: 一手资料是否直接支持或否定命题?
  • Boundary: 有无版本、平台、部署方式、权限、安全模型限制?
  • Counterexample: 是否存在能推翻“完全不支持”的示例或 workaround?
  • Terminology: 用户用词是否混淆了“没有内置支持”“不推荐”“不能做”?
  • Confidence: 证据是否足够强?是否还有需要实测或询问维护者的点?

Read the full file on GitHub · 183 lines

Files

What ships with it

4 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.

Changes

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.

  1. 26d ago Changed · +2 lines e21f08f6bdd1
  2. 1mo ago Changed b8f65ebec922
  3. 1mo ago First seen · 181 lines · 111 tokens per session scan A 03261ebc84d7

Subscribe to this mod's changes

lov-fact-check is a skill published in the GitHub repository lovstudio/skills (72 stars, last pushed today), licensed MIT. It adds 111 tokens to every session and 2,293 once invoked, about $0.0004 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.

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