type: user

role: user

content: 我需要建一个http页面，需要一个注册登录功能，记录用户名和密码，登录后，有一个生成apikey的功能。使用jwt生成key。 怎么存储这些信息你有什么建议。

permissionMode: default

kind: human

promptSource: typed

userType: external

entrypoint: cli

gitBranch: HEAD


addedTypes: claude claude-code-guide Explore general-purpose Plan statusline-setup

addedLines: - claude: Catch-all for any task that doesn't fit a more specific agent. FleetView's default when no agent name is typed. (Tools: *)

addedLines: - claude-code-guide: Use this agent when the user asks questions ("Can Claude...", "Does Claude...", "How do I...") about: (1) Claude Code (the CLI tool) - features, hooks, slash commands, MCP servers, settings, IDE integrations, keyboard shortcuts; (2) Claude Agent SDK - building custom agents; (3) Claude API (formerly Anthropic API) - Messages API for directly passing messages to Claude, Tool Runner (`client.beta.messages.tool_runner`) for running an agentic loop over your own tools, manual tool-use loops, Managed Agents for server-hosted agents with a managed sandbox, prompt caching, and general Anthropic SDK usage; (4) Claude Tag (Claude in Slack) - what it is, setting it up for a Slack workspace, `/install-slack-app`. **IMPORTANT:** Before spawning a new agent, check if there is already a running or recently completed claude-code-guide agent that you can continue via SendMessage. (Tools: Bash, Read, WebFetch, WebSearch)

addedLines: - Explore: Read-only search agent for broad fan-out searches — when answering means sweeping many files, directories, or naming conventions and you only need the conclusion, not the file dumps. It reads excerpts rather than whole files, so it locates code; it doesn't review or audit it. Specify search breadth: "medium" for moderate exploration, "very thorough" for multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)

addedLines: - general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)

addedLines: - Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)

addedLines: - statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)

type: attachment


content:
- deep-research: Deep research harness — fan-out web searches, fetch sources, adversarially verify claims, synthesize a cited report. - When the user wants a deep, multi-source, fact-checked research report on any topic. BEFORE invoking, check if the question is specific enough to research directly — if underspecified (e.g., "what car to buy" without budget/use-case/region), ask 2-3 clarifying questions to narrow scope. Then pass the refined question as args, weaving the answers in.
- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: "chart", "graph", "plot", "data viz", "visualization", "dashboard", "analytics", "visualize data", "categorical colors", "sequential / diverging palette", "stat tile", "sparkline", "heatmap", "legend", "axis", "tooltip", "chart colors", "color by series".
- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors ("from now on when X", "each time X", "whenever X", "before/after X") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions ("allow X", "add permission", "move permission to"), env vars ("set X=Y"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: "allow npm commands", "add bq permission to global settings", "move permission to user settings", "set DEBUG=true", "when claude stops show X". For simple settings like theme/model, suggest the /config command.
- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: "rebind ctrl+s", "add a chord shortcut", "change the submit key", "customize keybindings".
- verify: Verify that a code change actually does what it's supposed to by exercising it end-to-end and observing behavior — drive the affected flow, not just tests or typecheck. Run before committing nontrivial changes; bootstraps this repo's project verify skill if none exists yet. Don't invoke it on a diff that only touches tests, docs, or other code with no runtime surface to drive (a change to product source always has one) — there's nothing to observe.
- code-review: Review the current diff for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings). Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review.
- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.
- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.
- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo, defaults to 10m) - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. "check the deploy every 5 minutes", "keep running /babysit-prs"). Do NOT invoke for one-off tasks.
- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.
TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).
SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).
- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).
- init: Initialize a new CLAUDE.md file with codebase documentation
- review: Review a GitHub pull request; for your working diff use /code-review
- security-review: Complete a security review of the pending changes on the current branch

names: deep-research dataviz update-config keybindings-help verify code-review simplify fewer-permission-prompts loop claude-api run init review security-review


type: ai-title

aiTitle: 创建带注册登录和JWT的HTTP页面


type: message

role: assistant

type: thinking

thinking:
The user wants to build an HTTP page with registration/login, store username/password, and after login, generate an API key using JWT. They're asking for advice on how to store this information.

This is a design/advice question, not yet a "build it now" request. Let me check the working directory to understand context. The project is "pica-gateway". Let me give thoughtful advice on storage.

Let me first look at the project to understand what's there.

service_tier: standard

speed: standard

type: assistant

effort: low


Bash

command: ls -la /root/pica-gateway 2>/dev/null && echo "---" && find /root/pica-gateway -maxdepth 2 -type f 2>/dev/null | head -50

description: Inspect project directory


content:
total 12
drwxr-xr-x  2 root root 4096 Jul 18 20:42 .
drwx------ 11 root root 4096 Jul 19 21:03 ..
-rw-r--r--  1 root root  159 Jul 18 20:42 hello.py
---
/root/pica-gateway/hello.py


content:
1	#!/usr/bin/env python3
2	# -*- coding: utf-8 -*-
3	"""Hello World 示例程序"""
4
5
6	def main():
7	    print("Hello, World!")
8
9
10	if __name__ == "__main__":
11	    main()
12

type: text

content:
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Hello World 示例程序"""

def main():
    print("Hello, World!")

if __name__ == "__main__":
    main()


thinking:
This is essentially an empty project. The user is asking for advice on how to store info for a registration/login + JWT apikey system. Let me give a clear, opinionated recommendation rather than an exhaustive survey.

Key decisions:
1. Password storage — must hash with bcrypt/argon2, never plaintext
2. User storage — database (sqlite/postgres)
3. JWT apikey storage — JWTs are stateless, but for apikeys you often want revocability. So either store a hash of the apikey (JWT) in DB, or store a jti registry.

Let me give a concise recommendation.


text:
项目现在是空的（只有一个 hello.py），所以这里没有既定技术栈约束，我直接给建议。先回答存储层面的设计，这是这类系统最容易出错的地方。

## 核心建议：分两类信息，分别存储

### 1. 用户名 / 密码 —— 存数据库，密码只存哈希

**绝不存明文密码。** 用慢哈希算法，推荐顺序：

- **argon2**（首选，OWASP 当前推荐）
- **bcrypt**（次选，生态最成熟）
- 不要用 MD5/SHA-256/SHA-512 直接哈希（太快，可被暴力破解）

数据库选型：
- 个人/小项目起步 → **SQLite**（零运维，单文件），用 `passlib` + `argon2` 或 `bcrypt`
- 多实例/上线 → **PostgreSQL**

表结构示例（users）：

```sql
CREATE TABLE users (
  id            BIGSERIAL PRIMARY KEY,
  username      TEXT UNIQUE NOT NULL,
  password_hash TEXT NOT NULL,      -- argon2/bcrypt 哈希，含盐和参数
  created_at    TIMESTAMPTZ DEFAULT now()
);
```

注意：盐由 argon2/bcrypt 内部生成并编入哈希字符串，不需要单独列。

### 2. API Key（JWT）—— 这是关键，有两种思路

JWT 本身是无状态的（签名校验即可），但"生成 apikey"功能通常需要**可撤销 / 可列出 / 可统计用量**，纯无状态 JWT 做不到撤销。所以推荐**混合方案**：

**方案 A（推荐，可撤销）：JWT + 服务端登记**
- 用 JWT 作为 key 本身（客户端持有）
- 服务端在 `api_keys` 表里存 **JWT 的 `jti`（唯一 ID）的哈希** + 关联用户 + 状态
- 校验时：先验签 + 验过期，再查表确认该 `jti` 未被撤销
- 删除/吊销 = 把表里那行状态置为 revoked

```sql
CREATE TABLE api_keys (
  id           BIGSERIAL PRIMARY KEY,
  user_id      BIGINT NOT NULL REFERENCES users(id) ON DELETE CASCADE,
  key_hash     TEXT NOT NULL,        -- sha256(jwt) 或 sha256(jti)，用于查表
  jti          TEXT NOT NULL,        -- 方便定位
  name         TEXT,                 -- 用户给的备注，如 "production"
  last_used_at TIMESTAMPTZ,
  expires_at   TIMESTAMPTZ,          -- 和 JWT exp 对应
  revoked      BOOLEAN DEFAULT false,
  created_at   TIMESTAMPTZ DEFAULT now()
);
```

存哈希而非原 JWT：万一数据库泄露，攻击者拿不到可用的 key。

**方案 B（纯无状态，简单但不可撤销）**：JWT 签好直接返回，服务端不存。靠 `exp` 短有效期 + 刷新机制。适合 key 不需要吊销的场景。绝大多数"apikey 管理"后台需求都不适合这个方案。

### 3. JWT 本身的注意点

- 签名算法用 **HS256**（对称，简单）或 **RS256/EdDSA**（非对称，适合多服务校验）。单服务起步用 HS256 即可。
- `secret` 从环境变量读，不要写进代码，不要进 git。
- payload 至少包含：`sub`(user_id)、`jti`(随机 uuid)、`iat`、`exp`。
- 不要把密码哈希或敏感信息塞进 JWT payload —— JWT payload 只 base64 编码，**不加密**，谁拿到都能读。

---

## 关于"怎么存储这些信息"——一句话总结

| 信息 | 存哪 | 怎么存 |
|---|---|---|
| 用户名 | 数据库 | 明文（需唯一索引） |
| 密码 | 数据库 | argon2/bcrypt 哈希，不存明文 |
| JWT secret/私钥 | 环境变量 / 密钥管理服务 | 不入库、不入 git |
| API Key（JWT）本身 | 交给客户端 | 服务端只存其哈希 + 元数据，用于校验和吊销 |
| 登录态 session | 不需要 | 登录后直接发 JWT，无状态 |

---

在动手前我想确认两点，免得选错栈返工：


AskUserQuestion

options:
现有 hello.py 是 Python，FastAPI 自带 OAuth2/JWT 工具，开发快，适合 API 网关场景
更轻量经典，生态成熟，适合简单页面
前后端都用 JS，前后端同构
性能好、单二进制部署，适合网关

question: 后端用什么语言/框架？

options:
零运维单文件，适合个人/开发期，后续可平滑迁到 Postgres
生产级，多实例/上线直接用，需要额外部署
如果团队更熟悉 MySQL

question: 数据库选哪个？

options:
Jinja2/模板直接出页面，最快，无需前端构建
前后端分离，前端调后端 API
我先做 API，前端之后再说

question: 前端页面想怎么实现？


type: last-prompt

lastPrompt: 我需要建一个http页面，需要一个注册登录功能，记录用户名和密码，登录后，有一个生成apikey的功能。使用jwt生成key。 怎么存储这些信息你有什么建议。