critical-thinking-framework

基于津巴多心理学六问法，对任何新观点、主张或信息进行系统性、结构化的批判性审视。通过六个递进维度的分析，识别潜在偏差、验证逻辑有效性，并形成经过多重检验的审慎结论。适用于评估新闻报道、科学主张、政策建议及日常决策。

Security Scan

This is an instruction-only critical‑thinking checklist whose declared parameters and runtime instructions match its stated purpose; it requests no credentials, installs no code, and does not attempt to access system files or external services.

Purpose & Capability

Name/description (a six-step critical‑thinking framework) aligns with the declared parameters (claim, source_info, evidence_available, domain). No unrelated binaries, env vars, or config paths are required.

Instruction Scope

SKILL.md contains stepwise analytic instructions limited to evaluating a claim using supplied inputs. It does not instruct reading system files, environment variables, or sending data to external endpoints. Outputs are confined to structured analysis fields.

Install Mechanism

No install spec or code files — instruction‑only. Nothing is downloaded or written to disk by the skill itself.

Credentials

The skill requires no credentials, keys, or config paths. The parameters expect user-provided claim/evidence metadata only; there are no disproportionate secret requests.

Persistence & Privilege

always:false and no indication of modifying agent/system settings. The skill does not request persistent presence or elevated privileges.

Assessment

This skill is low risk: it is a descriptive checklist (no code, no installs, no credentials). Consider these points before installing: (1) provenance: the skill has no homepage and an unknown source—review outputs cautiously. (2) Input sensitivity: don't pass secrets or sensitive personal data in source_info or evidence_available because the skill will process any user-supplied content. (3) Agent actions: confirm how your agent will use the skill's outputs (e.g., avoid automatic public posting or sharing). If you need stronger assurance, run the skill in a sandboxed agent session or inspect example runs before enabling it broadly.

Parameters

Parameter	Type	Required	Description
claim	string	Yes	待分析的具体观点或主张（如"糖会导致儿童亢奋"）
source_info	object	No	观点来源信息，包含author（作者）、credentials（资质）、affiliation（所属机构）、potential_interests（潜在利益）
evidence_available	array	No	当前可获取的支持性证据列表（轶事、统计数据、实验结果等）
domain	string	No	观点所属领域（医学/心理学/社会学/政策等），用于选择专业视角

Execution Steps

Step 1: 权威溯源与利益审查 (Source & Interest Verification)

• 核查提出者是否具备相关领域的实际专业资质（非自封专家）
• 识别利益冲突：是否存在政治资本、经济利益或媒体曝光动机？
• 输出标记: source_credibility (high/medium/low), conflict_of_interest (boolean)

Step 2: 主张合理性校准 (Claim Plausibility Calibration)

• 应用"卡尔·萨根原则"：不寻常的主张需要不寻常的证据
• 评估与现有科学共识的兼容性，警惕"突破/革命"式宣传
• 质疑"快速解决复杂问题"的承诺（如简单方法解决青少年犯罪）
• 输出标记: claim_plausibility (plausible/suspicious/extreme), evidence_barrier (high/low)

Step 3: 证据质量分级 (Evidence Hierarchy Evaluation)

• 区分轶事证据（个人证词、精心挑选的案例）与实证证据（可重复实验、大样本研究）
• 检查是否存在"将适合少数人的事物推断为适合所有人"的风险
• 输出标记: evidence_type (anecdotal/correlational/experimental), generalizability_risk (high/medium/low)

Step 4: 认知偏差识别 (Cognitive Bias Detection)

• 情感偏差：是否存在绝望、恐惧等强烈情绪影响判断？（如绝望父母 grab any straw）
• 证实性偏差：是否只记住支持信念的证据而忽略反证？（如占星术信徒的选择性记忆）
• 期望偏差：观察者是否因预期而看到"想看到的结果"？（如预期糖导致亢奋而高估活跃程度）
• 输出标记: detected_biases (array: [emotional_bias, confirmation_bias, expectancy_bias])

Step 5: 逻辑谬误规避 (Logical Fallacy Avoidance)

• 相关-因果谬误检查：是否将时间先后或统计关联等同于因果关系？
• 考虑第三变量解释（C变量）：是否存在同时影响A和B的隐藏因素？
• 警惕"常识替代科学"（如"物以类聚"与"异性相吸"的矛盾常识）
• 输出标记: causal_validity (valid/suspected_fallacy), third_variables (array)

Step 6: 多元视角整合 (Multi-perspective Integration)

• 强制要求至少3个不同学科视角：
  • 生物/心理视角：遗传、神经机制、人格特质（如糖代谢对大脑的影响）
  • 社会/情境视角：环境压力、群体规范、文化背景（如聚会氛围对儿童行为的影响）
  • 经济/系统视角：成本收益、结构性因素、激励机制
• 输出标记: perspectives_considered (array), complexity_level (simple/complex)

Output Format

{
  "analysis_summary": {
    "overall_trustworthiness": "low/medium/high",
    "key_risks": ["利益冲突", "相关-因果谬误", "情感偏差"],
    "recommended_action": "reject/suspend_judgment/accept_with_caution"
  },
  "detailed_assessment": {
    "source": {"credibility": "...", "conflicts": "..."},
    "claim": {"plausibility": "...", "required_evidence_level": "..."},
    "evidence": {"quality": "...", "generalizability": "..."},
    "biases": ["emotional_bias", "confirmation_bias"],
    "logic": {"causal_validity": "...", "alternative_explanations": ["..."]},
    "perspectives": ["biological", "social", "economic"]
  }
}