causal-chain-analysis SKILL.md skillA
causal-chain-analysis from functoreality/agent-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add functoreality/agent-skills/causal-chain-analysis@v260822Pins v260822 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- functoreality/agent-skills
- Path
skills/causal-chain-analysis/SKILL.md- Versions recorded
- 5
- Latest version
- v260822, committed Oct 26, 2026
- First version recorded
- Aug 18, 2026
- Size
- 38,390 bytes
- Repository stars
- 11
- License
- LGPL-3.0
因果链分析是现代TRIZ理论中深入分析问题的重要工具。当用户提到"因果链分析"，或对某种情形不满、又想不出好办法时，使用此skill。该skill能够帮助用户从表层问题出发，通过反复追问"为什么"，层层深入挖掘问题的根本原因，建立完整的因果链，以便将来识别关键缺点、转化为可解决的关键问题。.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# 因果链分析 Skill ## 概述 因果链分析是一种系统性的问题根因分析方法。它从用户提供的原始问题（表层缺点）出发，先分析本质上需要解决的初始缺点，然后以"逐节点展开"的方式生长因果链： - 维护一个待展开节点的优先队列 - 每次取出优先级最高的一个节点（即当前对初始缺点影响最大的） - 拆分出它的下一层直接原因 - 把新发现的子节点按优先级放回队列，循环往复 - 直到队列中所有节点都展开完毕或被判定为末端缺点 这种"带优先级的树搜索"方式，避免了"一次性起草全文再回头检查"的弊端。 每展开一个节点，就即时确认： - 它的表述是否规范 - 拆分是否直接且完备 - 是否与已有节点重复 发现问题立即修正，必要时甚至回退修改上层节点。因果链在生长的每一步都保持正确，而不是先长成一棵可能有错的树再去修剪。 **核心输出**：一个Markdown文件，包含完整的因果链分析结果，使用缩进表示层级关系。 --- ## 何时使用此Skill 当用户提到以下内容时，应调用此skill： - "分析因果链" - "帮我做因果链分析" - "进行因果链分析" 如果用户只是对某种情况感到不满意，希望能解决眼前的问题，却想不出好的解决办法，则： 1. 先自行评估，如果对这个问题的产生机制（在理论层面）做一次完整充分的分析，是否有助于产生更多的候选方案 2. 如果是，询问用户：是否愿意由 AI 助手系统地把问题出现的原因梳理一遍，这或许能帮助想出更多的解决思路 3. 用户同意后，调用此skill 4. 若 1、2 中任何一个的答案为否，可忽略此skill --- ## 分析流程 请先阅读 `references/factor_definition.md` 了解因素定义。 因果链中的每个因素（包括初始缺点、推演中间结果、展开的子节点）都必须符合这一定义：主语+谓语、明确具体、原子化、客观事实、肯定陈述。 后续步骤中不再重复列举这些要求，统一以"符合因素定义"指代。 ### 步骤1：理解用户问题 1. 仔细阅读用户描述的问题 2. 识别用户提供的"表层缺点" 3. **关键**：分析这个表层缺点，判断它是否是"本质初始缺点"，还是用户最直接看到的表面因素 ### 步骤2：推导本质初始缺点 如果用户提供的只是一个导致不满的直接现象，而非根本上造成不满的初始缺点，需要进行推导。推演链中的每一个中间结果都应符合因素定义： 1. 询问"这个表层缺点会导致什么？" 2. 推演它的结果。推演仅限于客观因果（该缺点会引发什么客观后果），不讨论主观动机（使得什么功能无法实现、什么目的无法达成） - 注意这里"原因→结果"的分析方向与步骤4相反 3. 继续追问，直到找到用户本质上想避免的客观状况，即初始缺点 4. 依据因素定义，调整优化初始缺点的表述 …
Read the whole file at its exact version.
How to install
mdr add functoreality/agent-skills/causal-chain-analysis@sha256:2be5c61e4df0752cThe line at the top of this page pins the label v260822, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_m3bpgu5ywxlz7ngs)
2 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 5 versions of this file, between Aug 18, 2026 and Oct 26, 2026, about one every 17 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v260822 latest | 2026-10-26 | 2da679d | 38,390 B | A | view · diff |
| v260818 | 2026-08-18 | ab60944 | 38,272 B | A | view · diff |
| v260818 | 2026-08-18 | 577ae90 | 38,266 B | A | view · diff |
| v260818 | 2026-08-18 | c17dc93 | 38,364 B | A | view · diff |
| v260818 | 2026-08-18 | 00681d0 | 38,355 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (38390 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
functoreality/agent-skills · 11 stars · license LGPL-3.0 · pushed 2026-11-05 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_m3bpgu5ywxlz7ngs GET https://markdownregistry.com/api/v1/resolve?ref=functoreality/agent-skills/causal-chain-analysis GET https://markdownregistry.com/api/v1/blob/2be5c61e4df0752c37ba7ec685fe1371456f9b7ef3cd62893c1b0450b6d42fe9
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.