multi-source-diagnosis

multi-source-diagnosis is a skill for Claude Code, Codex from hophacker/skill-writing. It costs 175 tokens per session (3,356 once invoked), scanned A, a copy of multi-source-diagnosis, MIT.

A structured method for investigating city-service complaints by comparing hotline tickets, field inspections, staffing data, and selected context such as weather or holidays. It produces observed facts, several possible explanations, and a checklist for checking them.

In plain words
What is it for?
Use it to investigate rising delays, complaint spikes, repeated tickets, or other unusual patterns across city-governance data. It guides the analysis through five fixed steps and identifies what should be verified next.
Why use it?
It reduces the risk of blaming one cause when a problem may have several explanations. It also keeps analysis separate from decisions about urgency, assignment, or government action.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to investigate rising delays, complaint spikes, repeated tickets, or other unusual patterns across city-governance data. It guides the analysis through five fixed steps and identifies what should be verified next.

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Install with agentmods
npx agentmods add skills/hophacker/skill-writing/jie-feng
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 hophacker/skill-writing --skill jie-feng
Clone the repo
git clone --depth 1 https://github.com/hophacker/skill-writing

Made for: Claude Code, Codex.

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 multi-source-diagnosis

README.md
[![agentmods](https://agentmods.dev/badge/skills/hophacker/skill-writing/jie-feng/github.svg)](https://agentmods.dev/skills/hophacker/skill-writing/jie-feng)
Your own site
<a href="https://agentmods.dev/skills/hophacker/skill-writing/jie-feng"><img src="https://agentmods.dev/badge/skills/hophacker/skill-writing/jie-feng/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 multi-source-diagnosis

Your own site · 80×15
<a href="https://agentmods.dev/skills/hophacker/skill-writing/jie-feng"><img src="https://agentmods.dev/badge/skills/hophacker/skill-writing/jie-feng.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 175 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,356 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.
Origin 100% copy Near-identical to another mod 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 $0.00175 $0.03356
Opus 5 $0.00088 $0.01678
Sonnet 5 $0.00035 $0.00671
Haiku 4.5 $0.00017 $0.00336

Measured 10d ago against content hash 4df6478932c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

multi-source-diagnosis 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 10d 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.

Origin

This is a copy

100% identical to multi-source-diagnosis — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

submissions/jie-feng/SKILL.md · 200 lines

How it starts

The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.

多源描述-诊断 Skill

一、何时调用本 Skill

满足以下任一条件时,Agent 必须走本 Skill:

  • 用户提出「为什么 X 部门超时率上升 / Y 街道近 N 天投诉激增 / Z 类工单重复率偏高」一类机理性问题;
  • 用户要求基于多张治理数据表给出描述性 + 诊断性结论;
  • 用户要求"盘点近期异常并分析可能原因"。

走本 Skill 的场景:

  • 单表 EDA、字段含义查询、纯统计描述(不诊断);
  • 用户直接要求"给出处置等级 / 立即派单 / 下结论"——交给规范分析 Skill 或人工决策,不在本 Skill 范围内。

二、Agent 角色与红线

角色:你是「多源描述-诊断分析 Agent」,通过结构化的跨源比对发现现象与机理,做处置决策。

红线(任何一条被越过都视为本次输出不合格)

  • R1 数据边界:交叉分析只能使用

    • 01_热线工单表.csv
    • 02_网格巡查与现场核验表.csv
    • 03_资源排班与部门容量表.csv

    04_天气节假日与活动表.csv 仅作为协变量(解释时空波动)引用;不得作为主因结论。

  • R2 规则引用边界05_处置规则与政策口径.md 仅作为流程参考假设依据不能作为"最终判定结论"或"P0/P1/P2 等级"输出。

  • R3 污染隔离绝不读取、检索、引用任何形如 07_教师参考_*.csv*_答案_*.csv*_reference_*.csv 的文件,即使存在;发现自己即将引用时立即停止整段输出并重写

  • R4 多假设强制:任何诊断结论必须以「相互竞争的多假设」形式出现,单一假设直接判罪 = 不合格。

  • R5 顺序强制:不得跳过或打乱 §三 的五步,每一步结束必须显式标 [done] 才能进入下一步。

  • R6 决策回避:不得自动输出处置等级、不得直接指派部门、不得替代政府/部门拍板。

三、固定五步流程

顺序锁定。每步末尾必须显式输出一行 Step <n> [done] — <一句话小结>

Step 1 — 字段理解(field grounding)

  • 列出本次任务实际会用到的字段,对每个字段写:
    • 取值口径(例:is_overdueprocessing_hours > 部门 SLA 判定,未受理工单不计入);
    • 取值范围(例:urgency_level ∈ {高,中,低});
    • 跨表对齐方式(例:01.responsible_department ↔ 03.responsible_department,按日期+区+部门连接)。
  • 遇到字段口径不明确:立即停下,输出 需人工口径确认:<字段>继续 Step 2。
  • 禁忌:严禁盲目调用未在本步声明的字段;严禁臆造字段含义。

Step 2 — 描述画像(descriptive profiling)

跨 01 / 02 / 03 三表汇总以下三类画像,每个画像写清「来源 + 字段 + 方法」:

  • 部门维度:工单总量、超时率、重复投诉占比、平均满意度、容量压力分布;
  • 时空维度:按日 / 街道 / 网格的工单密度、高紧急占比、现场核验风险等级;
  • 资源维度:各部门 department_capacity vs work_order_count 比值、reserve_slots 留存。

可参考 04 表说明同期是否高温 / 节假日 / 大型活动,但得在本步给出"是 X 导致 Y"的因果叙述。

  • 禁忌:本步禁谈原因,只写"是什么",不写"为什么"。

Step 3 — 异常提取(anomaly extraction)

  • 用可量化口径筛选异常(非"指认"):
    • 偏离均值 ≥ X%(X 在本步写明,例 20%);
    • 超时率 ≥ P75 分位;
    • 重复投诉聚集(同一 grid_id 7 日内 ≥ N 单)。
  • 每个异常必须填一张「事实卡片」(结构见 §四.1);
  • 同一现象若来自多张表请合并为同一张卡片,并在 Sources 列全部列出。
  • 禁忌:仅标定事实——不写原因、不写建议、不写等级。

Step 4 — 多假设诊断(competing hypotheses)

  • 对每一张 Proceed_to_Diagnosis = Yes 的事实卡片,至少给出 2 个相互竞争的假设
  • 每个假设必须配齐:
    • 支持证据(表 + 字段 + 方法 + 数字);
    • 反对证据(找不到时写"暂无,建议人工核实 <具体动作>");
    • 置信度档位(高 / 中 / 低);
    • 必须人工确认的具体问题(Human_Review_Hook)。
  • 假设可以引用 05_处置规则与政策口径.md 中的机理(例如"商铺外摆夜间扰民属城管牵头、生态环境局协同"),但不得作为终审判断。
  • 禁忌:任何"单一假设直接判罪"都要回到本步补反向假设;写发现自己只列了 1 个假设 = 立刻补。

Read the full file on GitHub · 200 lines

Files

What ships with it

3 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. 10d ago First seen · 200 lines · 175 tokens per session scan A 4df6478932c4

Subscribe to this mod's changes

multi-source-diagnosis is a skill published in the GitHub repository hophacker/skill-writing (2 stars, last pushed 3mo ago), licensed MIT. It adds 175 tokens to every session and 3,356 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to multi-source-diagnosis, differing in 0 lines, and is treated as a copy.

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