Her · हेर - Claude Code 会话分析工具

TL;DR · AI 摘要
Her是一款分析Claude Code会话的工具,通过解析JSONL文件帮助用户理解操作和风险。
核心要点
- Her可自动解析Claude Code会话的.jsonl文件,标记风险操作如生产环境部署
- 工具完全本地运行,数据不离开本地环境且会自动删除
- 内置Ask Her Copilot可追溯工具调用并展示具体操作步骤
结构提纲
按章节快速跳转。
思维导图
用一张图看清主题之间的关系。
查看大纲文本(无障碍 / 无 JS 友好)
- Claude Code分析工具
- 核心功能
- 会话追溯
- 风险标记
- 技术实现
- 本地运行
- 数据销毁
- 使用场景
- 单会话分析
- 多会话对比
金句 / Highlights
值得收藏与分享的关键句。
Her通过解析JSONL文件还原会话过程,标记生产环境部署等风险操作
工具完全本地运行,数据不离开本地环境且会自动删除
内置Ask Her Copilot可追溯工具调用并展示具体操作步骤
Her · हेर — _Marathi for “detective.”_ A detective for your Claude Code sessions.
Try it here: Her on Hugging Face
Every Claude Code session leaves a trace — a .jsonl file with every turn, tool call, and token. But in practice, that trace is write-only. Rarely anyone reads 4,000 lines of JSON to figure out _why the agent reached for production_, where the context budget actually went, or which subagent quietly burned half the run.
Her reads it for you.
The premise is simple: drop a session file onto the page and let her investigate. She reconstructs what happened in plain English, flags the risky moves — deploys, config and production changes, secrets — and traces each one back to the exact turn where it happened.

She shows where the tokens went, which tools, subagents, skills, and MCP servers were used, and — only when a named, fixable pattern fires — what you could have done better, grounded in Anthropic’s and the community’s best practices. She _suggests, never asserts_, and stays silent when there’s nothing worth saying.
There’s also a built-in copilot: Ask Her. Ask _“why was this tool used?”_ and she answers from the trace, cites the turns, and opens the exact tool call. Drop one file for a session view; drop several to build a project view and hunt a question across many sessions at once.

No third-party AI API is ever called. The model — Nemotron-Mini-4B-Instruct — runs on the Space’s own GPU via ZeroGPU. Your session is uploaded only to a private, auto-deleted namespace that belongs to your run, and nothing about it leaves the box.
The split that makes this trustworthy: the evaluation engine is purely deterministic. The model is used _only_ to write the English and propose softer suggestions. It never asserts a finding. The numbers don’t move when the model changes.

One nice detail: Her doesn’t just list the CLI tools a session used — she identifies them. A database of top tools from Homebrew, npm, and PyPI ships with the Space, so most tools are named offline with a one-line blurb. When deploy tools, database clients, or dev servers are actually executed, Her flags that activity for the second look it deserves.
It grew over a weekend. It started as an operator’s view — a journey graph where every query is a node sized by cost, the heaviest one glowing — built for a friend.

I showed it to another friend who wanted it simpler, so the graph grew an executive Report that’s now the default. Then the first friend asked why _their_ CLI tool didn’t show up — which is how the tool database was born.

The frontend is a React app served straight off a Gradio server, with the deterministic engine doing the forensics and Nemotron handling the prose.
When Claude loses his mind, call Her. ;)
Try it here: Her on Hugging Face