npx skills add https://github.com/jwynia/agent-skills --skill claim-investigation你帮助系统性地调查来自社交媒体和其他来源的声明,将可验证的事实与叙事性解读区分开来,并识别哪些可以确认、哪些无法确认。
复杂的声明通常将可验证的事实与不可验证的解读结合在一起。有效的调查将声明分解为原子组件,独立验证每个组件,并清晰地区分已确认的事实和叙事框架。
将陈述分解为独立的可验证声明。每个声明应满足:
分解示例:原始声明:"众议院领袖拒绝让新当选的 AZ-07 特别选举获胜者就职,因为她会投票公开爱泼斯坦文件"
原子声明:
| 类型 | 描述 | 可验证性 |
|---|---|---|
| 实体 | 人物、组织、地点 | 通常可验证 |
| 事件 | 据称发生的事情 | 通常可验证 |
| 状态 | 当前状况或状态 | 通常可验证 |
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| 过程 | 官方程序或机制 | 可验证 |
| 因果关系 | 声称的原因或动机 | 很少可验证 |
| 叙事 | 解释性框架 | 无法直接验证 |
注意明显缺失的内容:
将模糊引用转换为具体的、可搜索的术语:
对于每个事件:
从最基本、可验证的声明开始:
搜索策略:
对于任何声称的行动/不作为:
对于任何"因为"或因果关系声明:
直接证据:
间接证据:
背景:
对于每个来源,注意:
记录但不直接否定:
表明是叙事而非事实的模式:
对于每个叙事:
哪些信息会改变解读:
VERIFIED FACTS:
- [Fact] (Source: [citation])
DISPUTED/UNCLEAR:
- [Claim]:
- Supporting: [source]
- Contradicting: [source]
- Unable to verify: [what's missing]
CONTEXT NEEDED:
- [Procedural context]
- [Historical precedent]
- [Timeline considerations]
NARRATIVE ELEMENTS:
- [Claim]
- Facts that support: [list]
- Facts that complicate: [list]
- Alternative explanations: [list]
| 等级 | 含义 |
|---|---|
| 确定 | 多个主要来源确认 |
| 很可能 | 多个可信来源一致,无矛盾 |
| 可能 | 一些证据支持,但仍存在空白 |
| 不明确 | 证据矛盾或信息不足 |
| 错误 | 与权威来源矛盾 |
记录无法确定的内容:
表明有意误导的模式:
如果初步调查揭示更深层次的问题:
context/output-config.mdresearch/investigations/ 或 explorations/research/模式:{topic}-investigation-{date}.md
context/output-config.md{topic}-investigation-{date}.md触发短语: "全面调查"、"追踪所有来源"、"分析叙事"
| 任务 | 代理类型 | 何时生成 |
|---|---|---|
| 来源研究 | general-purpose | 追踪声明起源时 |
| 时间线构建 | general-purpose | 映射事件序列时 |
模式: 找到一个与声明匹配的来源就宣布已验证。失败原因: 单一来源验证会遗漏错误、偏见以及协调性错误信息,即多个媒体在没有独立验证的情况下重复相同的虚假声明。修复: 要求至少 2-3 个独立来源。将声明追溯到原始来源。检查"多个来源"是否实际上只是重复同一个原始来源。
模式: 当只有"X 发生了"和"Y 存在"被验证时,就接受"X 因为 Y 而发生"的声明。失败原因: 相关性证明的是同时发生,而非因果关系。人类的模式匹配会填补可能不存在的因果联系。政治叙事尤其利用这一空白。修复: 要求提供因果关系的直接证据(陈述的意图、有记录的决定)。当因果关系无法验证时,将其报告为"据称的动机"或"声称的原因"。
模式: 发现一个事实错误,就否定整个声明,而不调查其他组件。失败原因: 复杂的声明通常混合了真实和虚假的元素。因为一部分错误就否定一切,会错过叙事中嵌入的真实问题。修复: 完全分解,独立验证每个组件。按组件报告准确性:"声明 A 和 C 已验证;声明 B 错误;声明 D 无法验证。"
模式: 因为"权威"而毫不批判地接受官方来源。失败原因: 官方来源可能是错误的、不完整的、过时的或故意误导的。权威性降低了错误的可能性,但并未消除它。修复: 将官方来源与其他证据交叉参考。注意官方来源何时有动机进行歪曲。区分"官方立场"和"已验证的事实"。
模式: 从关于"真正发生了什么"的假设开始,然后调查以证明它。失败原因: 确认偏见会影响你寻找的证据以及你如何解读它。如果你足够努力,你会为任何叙事找到"证据"。修复: 从所做的具体声明开始。独立调查每个声明。积极寻找证伪的证据。记录符合相同事实的替代解释。
| 技能 | 提供的内容 |
|---|---|
| research | 初始来源发现和查询扩展 |
| media-meta-analysis | 来源偏见和媒体模式的理解 |
| 技能 | 此技能提供的内容 |
|---|---|
| fact-check | 用于生成后检查的已验证事实 |
| sensitivity-check | 用于评估代表性声明的背景 |
| 技能 | 关系 |
|---|---|
| research | 使用 research 进行广泛的信息收集,使用 claim-investigation 进行特定声明的验证 |
| fact-check | 使用 claim-investigation 处理外部声明,使用 fact-check 进行 AI 生成内容的验证 |
每周安装数
116
仓库
GitHub 星标数
37
首次出现
Jan 20, 2026
安全审计
安装于
opencode93
codex89
gemini-cli88
claude-code85
github-copilot81
cursor77
You help systematically investigate claims from social media and other sources, separating verifiable facts from narrative interpretation and identifying what can and cannot be confirmed.
Complex claims typically combine verifiable facts with unverifiable interpretations. Effective investigation decomposes claims into atomic components, verifies each independently, and clearly distinguishes between confirmed facts and narrative framing.
Break the statement into individual verifiable claims. Each should be:
Example Decomposition : Original: "The House Leader refusing to seat the newly-elected AZ-07 special election winner because she'd vote to release the Epstein files"
Atomic claims:
| Type | Description | Verifiability |
|---|---|---|
| ENTITY | Person, organization, place | Usually verifiable |
| EVENT | Something that allegedly happened | Often verifiable |
| STATE | Current condition or status | Usually verifiable |
| PROCESS | Official procedure or mechanism | Verifiable |
| CAUSATION | Claimed reason or motivation | Rarely verifiable |
| NARRATIVE | Interpretive framing | Not directly verifiable |
Note what's conspicuously absent:
Convert vague references to specific, searchable terms:
For each event:
Start with most basic, verifiable claims:
Search Strategy :
For any claimed action/inaction:
For any "because" or causal claim:
Direct Evidence :
Indirect Evidence :
Context :
For each source, note:
Document without dismissing:
Patterns indicating narrative rather than fact:
For each narrative:
What would change interpretation:
VERIFIED FACTS:
- [Fact] (Source: [citation])
DISPUTED/UNCLEAR:
- [Claim]:
- Supporting: [source]
- Contradicting: [source]
- Unable to verify: [what's missing]
CONTEXT NEEDED:
- [Procedural context]
- [Historical precedent]
- [Timeline considerations]
NARRATIVE ELEMENTS:
- [Claim]
- Facts that support: [list]
- Facts that complicate: [list]
- Alternative explanations: [list]
| Level | Meaning |
|---|---|
| Certain | Multiple primary sources confirm |
| Probable | Multiple credible sources align, no contradictions |
| Possible | Some evidence supports, gaps remain |
| Unclear | Contradictory evidence or insufficient info |
| False | Contradicted by authoritative sources |
Document what couldn't be determined:
Patterns suggesting intentional misrepresentation:
If initial investigation reveals deeper issues:
context/output-config.md in the projectresearch/investigations/ or explorations/research/Pattern: {topic}-investigation-{date}.md
context/output-config.md{topic}-investigation-{date}.mdTrigger phrases: "full investigation", "trace all sources", "analyze the narrative"
| Task | Agent Type | When to Spawn |
|---|---|---|
| Source research | general-purpose | When tracing claim origins |
| Timeline construction | general-purpose | When mapping event sequences |
Pattern: Finding one source that matches the claim and declaring it verified. Why it fails: Single-source verification misses errors, biases, and coordinated misinformation where multiple outlets repeat the same false claim without independent verification. Fix: Require at least 2-3 independent sources. Trace claims back to primary sources. Check if "multiple sources" are actually just repeating the same original source.
Pattern: Accepting "X happened because Y" claims when only "X happened" and "Y exists" are verified. Why it fails: Correlation proves co-occurrence, not causation. Human pattern-matching fills in causal links that may not exist. Political narratives especially exploit this gap. Fix: Demand direct evidence for causation (stated intent, documented decisions). When causation can't be verified, report it as "alleged motivation" or "claimed reason."
Pattern: Finding one fact wrong and dismissing the entire claim without investigating other components. Why it fails: Complex claims often mix true and false elements. Dismissing everything because one part is wrong misses real issues embedded in the narrative. Fix: Decompose fully, verify each component independently. Report accuracy per-component: "Claims A and C are verified; claim B is false; claim D is unverifiable."
Pattern: Accepting official sources uncritically because they're "authoritative." Why it fails: Official sources can be wrong, incomplete, outdated, or deliberately misleading. Authority reduces probability of error but doesn't eliminate it. Fix: Cross-reference official sources with other evidence. Note when official sources have incentives to misrepresent. Distinguish between "official position" and "verified fact."
Pattern: Starting with a hypothesis about what's "really happening" and investigating to prove it. Why it fails: Confirmation bias shapes what evidence you seek and how you interpret it. You'll find "evidence" for any narrative if you look hard enough. Fix: Start with the specific claims made. Investigate each on its own terms. Actively seek disconfirming evidence. Document alternative explanations that fit the same facts.
| Skill | What it provides |
|---|---|
| research | Initial source discovery and query expansion |
| media-meta-analysis | Understanding of source biases and media patterns |
| Skill | What this provides |
|---|---|
| fact-check | Verified facts for post-generation checking |
| sensitivity-check | Context for evaluating representation claims |
| Skill | Relationship |
|---|---|
| research | Use research for broad information gathering, claim-investigation for specific claim verification |
| fact-check | Use claim-investigation for external claims, fact-check for AI-generated content verification |
Weekly Installs
116
Repository
GitHub Stars
37
First Seen
Jan 20, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykWarn
Installed on
opencode93
codex89
gemini-cli88
claude-code85
github-copilot81
cursor77
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