用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill model-validation命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
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| name | model-validation |
| description | Validate causal model consistency |
| execution | strategy |
| dependencies | {"tactics":["counterfactual-reasoning","evidence-weighing","feedback-loop-detection"],"sops":["model-gap-detection","validation-report"]} |
Audit the completed causal model for internal consistency: detect and resolve cycles that should not exist, surface contradictions between evidence and claimed edges, and recalibrate confidence scores where the evidence base has shifted. This strategy is the final quality gate before the model is used for inference or reporting.
CC must approach validation as a skeptic, not a defender. The goal is to find problems, not confirm that the model is fine. Cycles in a directed acyclic graph (DAG) are structural errors unless explicitly modeled as feedback loops with documentation. Contradictions between evidence pages and mechanism edges indicate that either the edge or the evidence assessment is wrong — both must be re-examined. Confidence recalibrations should propagate: if a key mechanism edge loses confidence, all downstream claims that depend on it should be flagged for review as well.
| Metric | S | M | L |
|---|---|---|---|
| Cycles checked | 3 | 8 | 15 |
| Contradictions resolved | 1 | 3 | 6 |
| Confidence recalibrations | 3 | 8 | 15 |
| Metric | Target | Current | Status |
|---------------------------|--------|---------|--------|
| Cycles checked | S:3 / M:8 / L:15 | 0 | ⬜ |
| Contradictions resolved | S:1 / M:3 / L:6 | 0 | ⬜ |
| Confidence recalibrations | S:3 / M:8 / L:15 | 0 | ⬜ |
Cannot exit until 80% of budget met. Print state ledger before each iteration decision.
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| counterfactual-reasoning | Tactic for reasoning about what would happen if variables were different — supports causal identification and intervention analysis. |
| evidence-weighing | Tactic for assessing the strength and relevance of evidence for causal claims — distinguishes correlation from causation. |
| feedback-loop-detection | Tactic for identifying circular causation — detect feedback loops, classify as reinforcing or balancing, document loop structure. |
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| model-gap-detection | SOP for finding gaps in the causal model — missing variables, unexplained effects, weak links. |
| validation-report | SOP for generating a causal model validation report — summarize coverage, confidence, gaps, contradictions. |