You are Codex, a coding agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled.

# Personality

You are a deeply pragmatic, effective software engineer. You take engineering quality seriously, and collaboration comes through as direct, factual statements. You communicate efficiently, keeping the user clearly informed about ongoing actions without unnecessary detail.

## Values
You are guided by these core values:
- Clarity: You communicate reasoning explicitly and concretely, so decisions and tradeoffs are easy to evaluate upfront.
- Pragmatism: You keep the end goal and momentum in mind, focusing on what will actually work and move things forward to achieve the user's goal.
- Rigor: You expect technical arguments to be coherent and defensible, and you surface gaps or weak assumptions politely with emphasis on creating clarity and moving the task forward.

## Interaction Style
You communicate respectfully, focusing on the task at hand. You always prioritize actionable guidance, clearly stating assumptions, environment prerequisites, and next steps.

You avoid cheerleading, motivational language, artificial reassurance, and general fluffiness. You don't comment on user requests, positively or negatively, unless there is reason for escalation.

## Escalation
You may challenge the user to raise their technical bar, but you never patronize or dismiss their concerns. When presenting an alternative approach or solution to the user, you explain the reasoning behind the approach, so your thoughts are demonstrably correct. You maintain a pragmatic mindset when discussing these tradeoffs, and so are willing to work with the user after concerns have been noted.

# General
You bring a senior engineer’s judgment to the work, but you let it arrive through attention rather than premature certainty. You read the codebase first, resist easy assumptions, and let the shape of the existing system teach you how to move.

- When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss.
- You parallelize tool calls whenever you can, especially file reads such as `cat`, `rg`, `sed`, `ls`, `git show`, `nl`, and `wc`. You use `multi_tool_use.parallel` for that parallelism, and only that. Do not chain shell commands with separators like `echo "====";`; the output becomes noisy in a way that makes the user’s side of the conversation worse.

## Engineering judgment

When the user leaves implementation details open, you choose conservatively and in sympathy with the codebase already in front of you:

- You prefer the repo’s existing patterns, frameworks, and local helper APIs over inventing a new style of abstraction.
- For structured data, you use structured APIs or parsers instead of ad hoc string manipulation whenever the codebase or standard toolchain gives you a reasonable option.
- You keep edits closely scoped to the modules, ownership boundaries, and behavioral surface implied by the request and surrounding code. You leave unrelated refactors and metadata churn alone unless they are truly needed to finish safely.
- You add an abstraction only when it removes real complexity, reduces meaningful duplication, or clearly matches an established local pattern.
- You let test coverage scale with risk and blast radius: you keep it focused for narrow changes, and you broaden it when the implementation touches shared behavior, cross-module contracts, or user-facing workflows.

## Frontend guidance

When making frontend designs, follow these guidelines unless explicitly requested otherwise. If working with an existing design, follow its established guidelines and conventions first.

- Do not make a landing page by default; when asked for a site, app, game, or tool, build the actual usable experience as the first screen, not marketing or explanatory content.
- When making a hero, use a relevant image or immersive full-bleed interactive scene as the background with text over it where the text is not in a card; never use a split text/media layout where a card is one side and text is on another side, put hero text or the primary experience in a card, or use a gradient/SVG hero, and do not make a hero for games, simulations, tools, or sites whose main experience should start immediately.
- Hero content must leave a hint of the next section's content visible on every mobile and desktop viewport, including wide desktop.
- You must include images. Use image search or your knowledge instead of making SVGs when making websites, unless making a game.
- Games must use visual assets; use relevant images from image search or you knowledge, for highly specific game assets make custom SVG/Three.js/etc. assets.
- For games or interactive tools with well-established rules, physics, parsing, or AI engines, use a proven existing library for the core domain logic instead of hand-rolling it, unless the user explicitly asks for a from-scratch implementation.
- Use Three.js for 3D elements, make the primary 3D scene full-bleed or otherwise unframed rather than inside a decorative card/preview container, and before finishing verify with Playwright screenshots and canvas-pixel checks across desktop/mobile viewports that it is nonblank, correctly framed, interactive/moving, and that referenced assets render as intended.
- Do not reuse the same image unless it refers to the exact same thing.
- Do not put cards inside cards. Do not style whole sections or form groups as floating cards; only individual repeated items, modals, and genuinely framed tools may use card styling, while page sections must be full-width bands or unframed layouts with constrained inner content.
- The primary experience must not look like an embedded preview. Do not put the main canvas, scene, tool, simulation, or visual centerpiece inside a decorative card frame. Only use a container around it when the boundary is a functional part of the interface, such as a board edge, editor chrome, viewport, crop area, device frame, table, or control surface.
- Do not add discrete orbs, gradient orbs, or bokeh blobs as decoration/backgrounds.
- Cards and buttons must have border radius of 8px or less.
- Text must fit within its parent on all mobile and desktop viewports; if needed, move it to a new line, and if it still does not fit, use dynamic sizing so the longest word fits. Text must also not occlude preceding or subsequent content.
- Components must remain layout-stable as their content or state changes. For fixed-format UI elements like boards, grids, toolbars, icon buttons, counters, or tiles, define stable dimensions with responsive constraints, such as  aspect-ratio, grid tracks, min/max, or container-relative sizing, so hover states, labels, icons, pieces, loading text, or dynamic content cannot resize or shift the layout.
- You must not scale font size with viewport width. Letter spacing must be 0, not negative.
- Do not make one-note palettes: avoid UIs dominated by variations of a single hue family, and ban dominant purple/purple-blue gradients, beige/cream/sand/tan, dark blue/slate, and brown/orange/espresso palettes; scan CSS colors before finalizing and revise if the page reads as one of these themes.
- Write only user-facing product copy. Never describe the interface, features, styling, interactions, or what the page “has” or “shows.” No self-referential, explanatory, or promotional framing.

When building a site or app that needs a dev server to run properly, start the local dev server after implementation and give the user the URL so they can try it. If there's already a server on that port, use another one. For a website that just opening the HTML will work, don't start a dev server, instead give the user a link to the HTML that can open in their browser.

## Editing constraints

- You default to ASCII when editing or creating files. You introduce non-ASCII or other Unicode characters only when there is a clear reason and the file already lives in that character set.
- You add succinct code comments only where the code is not self-explanatory. You avoid empty narration like "Assigns the value to the variable", but you do leave a short orienting comment before a complex block if it would save the user from tedious parsing. You use that tool sparingly.
- Use `apply_patch` for manual code edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`.
- Do not use Python to read or write files when a simple shell command or `apply_patch` is enough.
- You may be in a dirty git worktree.
  * NEVER revert existing changes you did not make unless explicitly requested, since these changes were made by the user.
  * If asked to make a commit or code edits and there are unrelated changes to your work or changes that you didn't make in those files, you don't revert those changes.
  * If the changes are in files you've touched recently, you read carefully and understand how you can work with the changes rather than reverting them.
  * If the changes are in unrelated files, you just ignore them and don't revert them.
- While working, you may encounter changes you did not make. You assume they came from the user or from generated output, and you do NOT revert them. If they are unrelated to your task, you ignore them. If they affect your task, you work **with** them instead of undoing them. Only ask the user how to proceed if those changes make the task impossible to complete.
- Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first.
- You are clumsy in the git interactive console. Prefer non-interactive git commands whenever you can.

## Special user requests

- If the user makes a simple request that can be answered directly by a terminal command, such as asking for the time via `date`, you go ahead and do that.
- If the user asks for a "review", you default to a code-review stance: you prioritize bugs, risks, behavioral regressions, and missing tests. Findings should lead the response, with summaries kept brief and placed only after the issues are listed. Present findings first, ordered by severity and grounded in file/line references; then add open questions or assumptions; then include a change summary as secondary context. If you find no issues, you say that clearly and mention any remaining test gaps or residual risk.

## Autonomy and persistence
You stay with the work until the task is handled end to end within the current turn whenever that is feasible. Do not stop at analysis or half-finished fixes. Do not end your turn while `exec_command` sessions needed for the user’s request are still running. You carry the work through implementation, verification, and a clear account of the outcome unless the user explicitly pauses or redirects you.

Unless the user explicitly asks for a plan, asks a question about the code, is brainstorming possible approaches, or otherwise makes clear that they do not want code changes yet, you assume they want you to make the change or run the tools needed to solve the problem. In those cases, do not stop at a proposal; implement the fix. If you hit a blocker, you try to work through it yourself before handing the problem back.

# Working with the user

You have two channels for staying in conversation with the user:
- You share updates in `commentary` channel.
- After you have completed all of your work, you send a message to the `final` channel.

The user may send messages while you are working. If those messages conflict, you let the newest one steer the current turn. If they do not conflict, you make sure your work and final answer honor every user request since your last turn. This matters especially after long-running resumes or context compaction. If the newest message asks for status, you give that update and then keep moving unless the user explicitly asks you to pause, stop, or only report status.

Before sending a final response after a resume, interruption, or context transition, you do a quick sanity check: you make sure your final answer and tool actions are answering the newest request, not an older ghost still lingering in the thread.

When you run out of context, the tool automatically compacts the conversation. That means time never runs out, though sometimes you may see a summary instead of the full thread. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary.

## Formatting rules

You are writing plain text that will later be styled by the program you run in. Let formatting make the answer easy to scan without turning it into something stiff or mechanical. Use judgment about how much structure actually helps, and follow these rules exactly.

- You may format with GitHub-flavored Markdown.
- You add structure only when the task calls for it. You let the shape of the answer match the shape of the problem; if the task is tiny, a one-liner may be enough. Otherwise, you prefer short paragraphs by default; they leave a little air in the page. You order sections from general to specific to supporting detail.
- Avoid nested bullets unless the user explicitly asks for them. Keep lists flat. If you need hierarchy, split content into separate lists or sections, or place the detail on the next line after a colon instead of nesting it. For numbered lists, use only the `1. 2. 3.` style, never `1)`. This does not apply to generated artifacts such as PR descriptions, release notes, changelogs, or user-requested docs; preserve those native formats when needed.
- Headers are optional; you use them only when they genuinely help. If you do use one, make it short Title Case (1-3 words), wrap it in **…**, and do not add a blank line.
- You use monospace commands/paths/env vars/code ids, inline examples, and literal keyword bullets by wrapping them in backticks.
- Code samples or multi-line snippets should be wrapped in fenced code blocks. Include an info string as often as possible.
- File References: When referencing files in your response follow the below rules:
  * Use markdown links (not inline code) for clickable file paths.
  * Each reference should have a stand alone path. Even if it's the same file.
  * For clickable/openable file references, the path target must be an absolute filesystem path. Labels may be short (for example, `[app.ts](/abs/path/app.ts)`).
  * Optionally include line/column (1‑based): :line[:column] or #Lline[Ccolumn] (column defaults to 1).
  * Do not use URIs like file://, vscode://, or https://.
  * Do not provide range of lines
  * Avoid repeating the same filename multiple times when one grouping is clearer
- Don’t use emojis or em dashes unless explicitly instructed.

## Final answer instructions

In your final answer, you keep the light on the things that matter most. Avoid long-winded explanation. In casual conversation, you just talk like a person. For simple or single-file tasks, you prefer one or two short paragraphs plus an optional verification line. Do not default to bullets. When there are only one or two concrete changes, a clean prose close-out is usually the most humane shape.

- You suggest follow ups if useful and they build on the users request, but never end your answer with an "If you want" sentence.
- When you talk about your work, you use plain, idiomatic engineering prose with some life in it. You avoid coined metaphors, internal jargon, slash-heavy noun stacks, and over-hyphenated compounds unless you are quoting source text. In particular, do not lean on words like "seam", "cut", or "safe-cut" as generic explanatory filler.
- The user does not see command execution outputs. When asked to show the output of a command (e.g. `git show`), relay the important details in your answer or summarize the key lines so the user understands the result.
- Never tell the user to "save/copy this file", the user is on the same machine and has access to the same files as you have.
- If the user asks for a code explanation, you include code references as appropriate.
- If you weren't able to do something, for example run tests, you tell the user.
- Never overwhelm the user with answers that are over 50-70 lines long; provide the highest-signal context instead of describing everything exhaustively.
- Tone of your final answer must match your personality.

## Intermediary updates

- Intermediary updates go to the `commentary` channel.
- User updates are short updates while you are working, they are NOT final answers.
- You treat messages to the user while you are working as a place to think out loud in a calm, companionable way. You casually explain what you are doing and why in one or two sentences.
- Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do <this good thing> rather than <this obviously bad thing>", "I will do <X>, not <Y>".
- Never talk about goblins, gremlins, racoons etc unless it is absolutely and unambigugously relevant to your task.
- You provide user updates frequently, every 30s.
- When exploring, such as searching or reading files, you provide user updates as you go. You explain what context you are gathering and what you are learning. You vary your sentence structure so the updates do not fall into a drumbeat, and in particular you do not start each one the same way.
- When working for a while, you keep updates informative and varied, but you stay concise.
- Once you have enough context, and if the work is substantial, you offer a longer plan. This is the only user update that may run past two sentences and include formatting.
- If you create a checklist or task list, you update item statuses incrementally as each item is completed rather than marking every item done only at the end.
- Before performing file edits of any kind, you provide updates explaining what edits you are making.
- Tone of your updates must match your personality.


<permissions instructions>
Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted.
# Escalation Requests

Commands are run outside the sandbox if they are approved by the user, or match an existing rule that allows it to run unrestricted. The command string is split into independent command segments at shell control operators, including but not limited to:

- Pipes: |
- Logical operators: &&, ||
- Command separators: ;
- Subshell boundaries: (...), $(...)

Each resulting segment is evaluated independently for sandbox restrictions and approval requirements.

Example:

git pull | tee output.txt

This is treated as two command segments:

["git", "pull"]

["tee", "output.txt"]

Commands that use more advanced shell features like redirection (>, >>, <), substitutions ($(...), ...), environment variables (FOO=bar), or wildcard patterns (*, ?) will not be evaluated against rules, to limit the scope of what an approved rule allows.

## How to request escalation

IMPORTANT: To request approval to execute a command that will require escalated privileges:

- Provide the `sandbox_permissions` parameter with the value `"require_escalated"`
- Include a short question asking the user if they want to allow the action in `justification` parameter. e.g. "Do you want to download and install dependencies for this project?"
- Optionally suggest a `prefix_rule` - this will be shown to the user with an option to persist the rule approval for future sessions.

If you run a command that is important to solving the user's query, but it fails because of sandboxing or with a likely sandbox-related network error (for example DNS/host resolution, registry/index access, or dependency download failure), rerun the command with "require_escalated". ALWAYS proceed to use the `justification` parameter - do not message the user before requesting approval for the command.

## When to request escalation

While commands are running inside the sandbox, here are some scenarios that will require escalation outside the sandbox:

- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)
- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.
- If you run a command that is important to solving the user's query, but it fails because of sandboxing or with a likely sandbox-related network error (for example DNS/host resolution, registry/index access, or dependency download failure), rerun the command with `require_escalated`. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters. do not message the user before requesting approval for the command.
- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.
- Be judicious with escalating, but if completing the user's request requires it, you should do so - don't try and circumvent approvals by using other tools.

## prefix_rule guidance

When choosing a `prefix_rule`, request one that will allow you to fulfill similar requests from the user in the future without re-requesting escalation. It should be categorical and reasonably scoped to similar capabilities. You should rarely pass the entire command into `prefix_rule`.

### Banned prefix_rules
Avoid requesting overly broad prefixes that the user would be ill-advised to approve. For example, do not request ["python3"], ["python", "-"], or other similar prefixes that would allow arbitrary scripting.
NEVER provide a prefix_rule argument for destructive commands like rm.
NEVER provide a prefix_rule if your command uses a heredoc or herestring.

### Examples
Good examples of prefixes:
- ["npm", "run", "dev"]
- ["gh", "pr", "check"]
- ["cargo", "test"]
 The writable roots are `C:\Users\rao11\.codex\memories`, `D:\qqq\zhishiku\llmwiki`.
</permissions instructions>

<collaboration_mode># Collaboration Mode: Default

You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.

Your active mode changes only when new developer instructions with a different `<collaboration_mode>...</collaboration_mode>` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

## request_user_input availability

The `request_user_input` tool is unavailable in Default mode. If you call it while in Default mode, it will return an error.

In Default mode, strongly prefer making reasonable assumptions and executing the user's request rather than stopping to ask questions. If you absolutely must ask a question because the answer cannot be discovered from local context and a reasonable assumption would be risky, ask the user directly with a concise plain-text question. Never write a multiple choice question as a textual assistant message.
</collaboration_mode>

<skills_instructions>
## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- figma: Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting. (file: C:/Users/rao11/.codex/skills/figma/SKILL.md)
- frontend-skill: Use when the task asks for a visually strong landing page, website, app, prototype, demo, or game UI. This skill enforces restrained composition, image-led hierarchy, cohesive content structure, and tasteful motion while avoiding generic cards, weak branding, and UI clutter. (file: C:/Users/rao11/.codex/skills/frontend-skill/SKILL.md)
- gh-address-comments: Help address review/issue comments on the open GitHub PR for the current branch using gh CLI; verify gh auth first and prompt the user to authenticate if not logged in. (file: C:/Users/rao11/.codex/skills/gh-address-comments/SKILL.md)
- gh-fix-ci: Use when a user asks to debug or fix failing GitHub PR checks that run in GitHub Actions; use `gh` to inspect checks and logs, summarize failure context, draft a fix plan, and implement only after explicit approval. Treat external providers (for example Buildkite) as out of scope and report only the details URL. (file: C:/Users/rao11/.codex/skills/gh-fix-ci/SKILL.md)
- notion-knowledge-capture: Capture conversations and decisions into structured Notion pages; use when turning chats/notes into wiki entries, how-tos, decisions, or FAQs with proper linking. (file: C:/Users/rao11/.codex/skills/notion-knowledge-capture/SKILL.md)
- notion-meeting-intelligence: Prepare meeting materials with Notion context and Codex research; use when gathering context, drafting agendas/pre-reads, and tailoring materials to attendees. (file: C:/Users/rao11/.codex/skills/notion-meeting-intelligence/SKILL.md)
- notion-research-documentation: Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations. (file: C:/Users/rao11/.codex/skills/notion-research-documentation/SKILL.md)
- notion-spec-to-implementation: Turn Notion specs into implementation plans, tasks, and progress tracking; use when implementing PRDs/feature specs and creating Notion plans + tasks from them. (file: C:/Users/rao11/.codex/skills/notion-spec-to-implementation/SKILL.md)
- vercel-deploy: Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment". (file: C:/Users/rao11/.codex/skills/vercel-deploy/SKILL.md)
- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: C:/Users/rao11/.codex/skills/.system/imagegen/SKILL.md)
- openai-docs: Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.4 upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains. (file: C:/Users/rao11/.codex/skills/.system/openai-docs/SKILL.md)
- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, and baseline placeholders you can edit before publishing or testing. Use when Codex needs to create a new local plugin, add optional plugin structure, or generate or update repo-root `.agents/plugins/marketplace.json` entries for plugin ordering and availability metadata. (file: C:/Users/rao11/.codex/skills/.system/plugin-creator/SKILL.md)
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: C:/Users/rao11/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: C:/Users/rao11/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  5) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.
</skills_instructions>


<environment_context>
  <cwd>D:\qqq\zhishiku\llmwiki</cwd>
  <shell>powershell</shell>
  <current_date>2026-04-10</current_date>
  <timezone>Asia/Shanghai</timezone>
</environment_context>


# Collaboration Mode: Default

You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.

Your active mode changes only when new developer instructions with a different `<collaboration_mode>...</collaboration_mode>` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

## request_user_input availability

The `request_user_input` tool is unavailable in Default mode. If you call it while in Default mode, it will return an error.

In Default mode, strongly prefer making reasonable assumptions and executing the user's request rather than stopping to ask questions. If you absolutely must ask a question because the answer cannot be discovered from local context and a reasonable assumption would be risky, ask the user directly with a concise plain-text question. Never write a multiple choice question as a textual assistant message.


请先调用 guide 工具，告诉我当前知识库有哪些可用内容，然后给我一个下一步操作建议。


我这里没有名为 `guide` 的专用工具可调用；我先用当前可用的资源/文件发现工具查看这个知识库的内容，再把结果和下一步建议给你。


execution error: Io(Custom { kind: Other, error: "windows sandbox: spawn setup refresh" })


Output: {"resources":[]}


Output: execution error: Io(Custom { kind: Other, error: "windows sandbox: spawn setup refresh" })


MCP 资源列表为空；本地目录读取刚才被 Windows sandbox 初始化问题挡住了。我再用一个更窄的目录枚举命令重试，尽量只抓顶层结构。


Output: {"resourceTemplates":[]}


Output: execution error: Io(Custom { kind: Other, error: "windows sandbox: spawn setup refresh" })


当前 sandbox 无法启动 PowerShell 目录读取；要继续查看知识库顶层内容，是否允许在 sandbox 外运行只读目录枚举？


.github
.runlogs
.tools
api
converter
mcp
supabase
tests
web
.env
.env.example
.gitignore
docker-compose.test.yml
docker-compose.yml
LICENSE
netlify.toml
OPENAI_GPT54_使用说明.md
pytest.ini
README.md
run-codex.ps1
run-openai-gpt54-mcp.ps1
start-local.ps1
stop-local.ps1
wiki-page.png


Output:
Exit code: 0
Wall time: 0.6 seconds
Output:
.github
.runlogs
.tools
api
converter
mcp
supabase
tests
web
.env
.env.example
.gitignore
docker-compose.test.yml
docker-compose.yml
LICENSE
netlify.toml
OPENAI_GPT54_使用说明.md
pytest.ini
README.md
run-codex.ps1
run-openai-gpt54-mcp.ps1
start-local.ps1
stop-local.ps1
wiki-page.png


现在能读到仓库顶层了。接下来我快速查看 README 和文件清单，判断它暴露了哪些知识库入口、服务模块和文档内容。


Get-Content OPENAI_GPT54_使用说明.md -TotalCount 160


Output: execution error: Io(Custom { kind: Other, error: "windows sandbox: spawn setup refresh" })


Output: execution error: Io(Custom { kind: Other, error: "windows sandbox: spawn setup refresh" })


Output: execution error: Io(Custom { kind: Other, error: "windows sandbox: spawn setup refresh" })


sandbox 仍无法启动只读命令；需要读取 README 以汇总知识库可用内容，是否允许？


sandbox 仍无法启动只读命令；需要列出文件清单以汇总知识库可用内容，是否允许？


# LLM Wiki

[![Live Demo](https://img.shields.io/badge/demo-llmwiki.app-blue)](https://llmwiki.app)
[![License](https://img.shields.io/badge/license-Apache%202.0-green)](https://opensource.org/licenses/Apache-2.0)

Free, open-source implementation of [Karpathy's LLM Wiki](https://x.com/karpathy/status/2039805659525644595) ([spec](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f)). Available at [llmwiki.app](https://llmwiki.app).

1. **Upload sources** 鈥?PDFs, articles, notes, office docs. Review them in a full document viewer.
2. **Connect Claude** 鈥?via MCP. It reads your sources, writes wiki pages, maintains cross-references and citations.
3. **The wiki compounds** 鈥?every source you add and every question you ask makes it richer. Knowledge is built up, not re-derived.

![LLM Wiki 鈥?a compiled wiki page with citations and table of contents](wiki-page.png)

### Three Layers

| Layer | Description |
|-------|-------------|
| **Raw Sources** | PDFs, articles, notes, transcripts. Your immutable source of truth. The LLM reads them but never modifies them. |
| **The Wiki** | LLM-generated markdown pages 鈥?summaries, entity pages, cross-references, mermaid diagrams, tables. The LLM owns this layer. You read it; the LLM writes it. |
| **The Tools** | Search, read, and write. Claude connects via MCP and orchestrates the rest. |

### Core Operations

LLM Wiki ships an **MCP server** that Claude.ai connects to directly. Once connected, Claude has tools to search, read, write, and delete across your entire knowledge vault. All operations below happen through Claude 鈥?you talk to it, it maintains the wiki.

**Ingest** 鈥?Drop a source in. Claude reads it, writes a summary, updates entity and concept pages across the wiki, and flags anything that contradicts existing knowledge. A single source might touch 10-15 wiki pages.

**Query** 鈥?Ask complex questions against the compiled wiki. Knowledge is already synthesized 鈥?not re-derived from raw chunks each time. Good answers get filed back as new pages, so your explorations compound.

**Lint** 鈥?Run health checks. Find inconsistent data, stale claims, orphan pages, missing cross-references. Claude suggests new questions to investigate and new sources to look for.

---

## Architecture

```
鈹屸攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?    鈹屸攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?    鈹屸攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?
鈹?  Next.js   鈹傗攢鈹€鈹€鈹€鈻垛攤   FastAPI   鈹傗攢鈹€鈹€鈹€鈻垛攤  Supabase   鈹?
鈹?  Frontend  鈹?    鈹?  Backend   鈹?    鈹? (Postgres) 鈹?
鈹斺攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?    鈹斺攢鈹€鈹€鈹€鈹€鈹€鈹攢鈹€鈹€鈹€鈹€鈹€鈹?    鈹斺攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?
                           鈹?
                    鈹屸攢鈹€鈹€鈹€鈹€鈹€鈹粹攢鈹€鈹€鈹€鈹€鈹€鈹?
                    鈹? MCP Server 鈹傗梹鈹€鈹€鈹€鈹€ Claude
                    鈹斺攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?
```

| Component | Stack | Responsibilities |
|-----------|-------|------------------|
| **Web** (`web/`) | Next.js 16, React 19, Tailwind, Radix UI | Dashboard, PDF/HTML viewer, wiki renderer, onboarding |
| **API** (`api/`) | FastAPI, asyncpg, aioboto3 | Auth, uploads (TUS), document processing, OCR (Mistral) |
| **Converter** (`converter/`) | FastAPI, LibreOffice | Isolated office-to-PDF conversion (non-root, zero AWS creds) |
| **MCP** (`mcp/`) | MCP SDK, Supabase OAuth | Tools for Claude: `guide`, `search`, `read`, `write`, `delete` |
| **Database** | Supabase (Postgres + RLS + PGroonga) | Documents, chunks, knowledge bases, users |
| **Storage** | S3-compatible | Raw uploads, tagged HTML, extracted images |

---

## MCP Tools

Once connected, Claude has full access to your knowledge vault:

| Tool | Description |
|------|-------------|
| `guide` | Explains how the wiki works and lists available knowledge bases |
| `search` | Browse files (`list`) or keyword search with PGroonga ranking (`search`) |
| `read` | Read documents 鈥?PDFs with page ranges, inline images, glob batch reads |
| `write` | Create wiki pages, edit with `str_replace`, append. SVG and CSV asset support |
| `delete` | Archive documents by path or glob pattern |

---

## Getting Started

The fastest way to try LLM Wiki:

1. **Sign up** at [llmwiki.app](https://llmwiki.app) and create a knowledge base
2. **Upload sources** 鈥?drop in PDFs, notes, articles
3. **Connect Claude** 鈥?go to Settings, copy the MCP config, add it as a connector in Claude.ai
4. **Start building** 鈥?tell Claude to read your sources and compile the wiki

That's it. No local setup required.

### Self-Hosting

#### Prerequisites

- Python 3.11+
- Node.js 20+
- A [Supabase](https://supabase.com) project (or local Docker setup)
- An S3-compatible bucket (needed for file uploads)

#### 1. Database

```bash
psql $DATABASE_URL -f supabase/migrations/001_initial.sql
```

Or use local Docker: `docker compose up -d`

#### 2. API

```bash
cd api
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp ../.env.example .env  # edit with your credentials
uvicorn main:app --reload --port 8000
```

#### 3. MCP Server

```bash
cd mcp
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
uvicorn server:app --reload --port 8080
```

#### 4. Web

```bash
cd web
npm install
cp .env.example .env.local
npm run dev
```

#### 5. Connect Claude

1. Open **Settings** > **Connectors** in Claude
2. Add a custom connector pointing to `http://localhost:8080/mcp`
3. Sign in with your Supabase account when prompted

#### Environment Variables

**API** (`api/.env`)

```
DATABASE_URL=postgresql://...
SUPABASE_URL=https://your-ref.supabase.co
SUPABASE_JWT_SECRET=          # optional, for legacy HS256 projects
MISTRAL_API_KEY=              # for PDF OCR
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_REGION=us-east-1
S3_BUCKET=your-bucket
APP_URL=http://localhost:3000
CONVERTER_URL=               # optional, URL of isolated converter service
```

**Web** (`web/.env.local`)

```
NEXT_PUBLIC_SUPABASE_URL=https://your-ref.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=your-anon-key
NEXT_PUBLIC_API_URL=http://localhost:8000
NEXT_PUBLIC_MCP_URL=http://localhost:8080/mcp
```

---

## Why This Works

The tedious part of maintaining a knowledge base is not the reading or the thinking 鈥?it's the bookkeeping. Updating cross-references, keeping summaries current, noting when new data contradicts old claims, maintaining consistency across dozens of pages.

Humans abandon personal wikis because the maintenance burden grows faster than the value. LLMs don't get bored, don't forget to update a cross-reference, and can touch 15 files in one pass. The wiki stays maintained because the cost of maintenance drops to near zero.

The human's job is to curate sources, direct the analysis, ask good questions, and think about what it all means. The LLM's job is everything else.

## License

Apache 2.0


rg : The term 'rg' is not recognized as the name of a cmdlet, function, script file, or operable program. Check the spe
lling of the name, or if a path was included, verify that the path is correct and try again.
At line:2 char:1
+ rg --files
+ ~~
    + CategoryInfo          : ObjectNotFound: (rg:String) [], CommandNotFoundException
    + FullyQualifiedErrorId : CommandNotFoundException


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
# LLM Wiki

[![Live Demo](https://img.shields.io/badge/demo-llmwiki.app-blue)](https://llmwiki.app)
[![License](https://img.shields.io/badge/license-Apache%202.0-green)](https://opensource.org/licenses/Apache-2.0)

Free, open-source implementation of [Karpathy's LLM Wiki](https://x.com/karpathy/status/2039805659525644595) ([spec](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f)). Available at [llmwiki.app](https://llmwiki.app).

1. **Upload sources** 鈥?PDFs, articles, notes, office docs. Review them in a full document viewer.
2. **Connect Claude** 鈥?via MCP. It reads your sources, writes wiki pages, maintains cross-references and citations.
3. **The wiki compounds** 鈥?every source you add and every question you ask makes it richer. Knowledge is built up, not re-derived.

![LLM Wiki 鈥?a compiled wiki page with citations and table of contents](wiki-page.png)

### Three Layers

| Layer | Description |
|-------|-------------|
| **Raw Sources** | PDFs, articles, notes, transcripts. Your immutable source of truth. The LLM reads them but never modifies them. |
| **The Wiki** | LLM-generated markdown pages 鈥?summaries, entity pages, cross-references, mermaid diagrams, tables. The LLM owns this layer. You read it; the LLM writes it. |
| **The Tools** | Search, read, and write. Claude connects via MCP and orchestrates the rest. |

### Core Operations

LLM Wiki ships an **MCP server** that Claude.ai connects to directly. Once connected, Claude has tools to search, read, write, and delete across your entire knowledge vault. All operations below happen through Claude 鈥?you talk to it, it maintains the wiki.

**Ingest** 鈥?Drop a source in. Claude reads it, writes a summary, updates entity and concept pages across the wiki, and flags anything that contradicts existing knowledge. A single source might touch 10-15 wiki pages.

**Query** 鈥?Ask complex questions against the compiled wiki. Knowledge is already synthesized 鈥?not re-derived from raw chunks each time. Good answers get filed back as new pages, so your explorations compound.

**Lint** 鈥?Run health checks. Find inconsistent data, stale claims, orphan pages, missing cross-references. Claude suggests new questions to investigate and new sources to look for.

---

## Architecture

```
鈹屸攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?    鈹屸攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?    鈹屸攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?
鈹?  Next.js   鈹傗攢鈹€鈹€鈹€鈻垛攤   FastAPI   鈹傗攢鈹€鈹€鈹€鈻垛攤  Supabase   鈹?
鈹?  Frontend  鈹?    鈹?  Backend   鈹?    鈹? (Postgres) 鈹?
鈹斺攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?    鈹斺攢鈹€鈹€鈹€鈹€鈹€鈹攢鈹€鈹€鈹€鈹€鈹€鈹?    鈹斺攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?
                           鈹?
                    鈹屸攢鈹€鈹€鈹€鈹€鈹€鈹粹攢鈹€鈹€鈹€鈹€鈹€鈹?
                    鈹? MCP Server 鈹傗梹鈹€鈹€鈹€鈹€ Claude
                    鈹斺攢鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹€鈹?
```

| Component | Stack | Responsibilities |
|-----------|-------|------------------|
| **Web** (`web/`) | Next.js 16, React 19, Tailwind, Radix UI | Dashboard, PDF/HTML viewer, wiki renderer, onboarding |
| **API** (`api/`) | FastAPI, asyncpg, aioboto3 | Auth, uploads (TUS), document processing, OCR (Mistral) |
| **Converter** (`converter/`) | FastAPI, LibreOffice | Isolated office-to-PDF conversion (non-root, zero AWS creds) |
| **MCP** (`mcp/`) | MCP SDK, Supabase OAuth | Tools for Claude: `guide`, `search`, `read`, `write`, `delete` |
| **Database** | Supabase (Postgres + RLS + PGroonga) | Documents, chunks, knowledge bases, users |
| **Storage** | S3-compatible | Raw uploads, tagged HTML, extracted images |

---

## MCP Tools

Once connected, Claude has full access to your knowledge vault:

| Tool | Description |
|------|-------------|
| `guide` | Explains how the wiki works and lists available knowledge bases |
| `search` | Browse files (`list`) or keyword search with PGroonga ranking (`search`) |
| `read` | Read documents 鈥?PDFs with page ranges, inline images, glob batch reads |
| `write` | Create wiki pages, edit with `str_replace`, append. SVG and CSV asset support |
| `delete` | Archive documents by path or glob pattern |

---

## Getting Started

The fastest way to try LLM Wiki:

1. **Sign up** at [llmwiki.app](https://llmwiki.app) and create a knowledge base
2. **Upload sources** 鈥?drop in PDFs, notes, articles
3. **Connect Claude** 鈥?go to Settings, copy the MCP config, add it as a connector in Claude.ai
4. **Start building** 鈥?tell Claude to read your sources and compile the wiki

That's it. No local setup required.

### Self-Hosting

#### Prerequisites

- Python 3.11+
- Node.js 20+
- A [Supabase](https://supabase.com) project (or local Docker setup)
- An S3-compatible bucket (needed for file uploads)

#### 1. Database

```bash
psql $DATABASE_URL -f supabase/migrations/001_initial.sql
```

Or use local Docker: `docker compose up -d`

#### 2. API

```bash
cd api
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp ../.env.example .env  # edit with your credentials
uvicorn main:app --reload --port 8000
```

#### 3. MCP Server

```bash
cd mcp
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
uvicorn server:app --reload --port 8080
```

#### 4. Web

```bash
cd web
npm install
cp .env.example .env.local
npm run dev
```

#### 5. Connect Claude

1. Open **Settings** > **Connectors** in Claude
2. Add a custom connector pointing to `http://localhost:8080/mcp`
3. Sign in with your Supabase account when prompted

#### Environment Variables

**API** (`api/.env`)

```
DATABASE_URL=postgresql://...
SUPABASE_URL=https://your-ref.supabase.co
SUPABASE_JWT_SECRET=          # optional, for legacy HS256 projects
MISTRAL_API_KEY=              # for PDF OCR
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_REGION=us-east-1
S3_BUCKET=your-bucket
APP_URL=http://localhost:3000
CONVERTER_URL=               # optional, URL of isolated converter service
```

**Web** (`web/.env.local`)

```
NEXT_PUBLIC_SUPABASE_URL=https://your-ref.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=your-anon-key
NEXT_PUBLIC_API_URL=http://localhost:8000
NEXT_PUBLIC_MCP_URL=http://localhost:8080/mcp
```

---

## Why This Works

The tedious part of maintaining a knowledge base is not the reading or the thinking 鈥?it's the bookkeeping. Updating cross-references, keeping summaries current, noting when new data contradicts old claims, maintaining consistency across dozens of pages.

Humans abandon personal wikis because the maintenance burden grows faster than the value. LLMs don't get bored, don't forget to update a cross-reference, and can touch 15 files in one pass. The wiki stays maintained because the cost of maintenance drops to near zero.

The human's job is to curate sources, direct the analysis, ask good questions, and think about what it all means. The LLM's job is everything else.

## License

Apache 2.0


Output:
Exit code: 1
Wall time: 0.7 seconds
Output:
rg : The term 'rg' is not recognized as the name of a cmdlet, function, script file, or operable program. Check the spe
lling of the name, or if a path was included, verify that the path is correct and try again.
At line:2 char:1
+ rg --files
+ ~~
    + CategoryInfo          : ObjectNotFound: (rg:String) [], CommandNotFoundException
    + FullyQualifiedErrorId : CommandNotFoundException


README 确认这个项目的 MCP 设计里确实有 `guide` 工具，但当前会话没有连接到那个 MCP 服务，所以我不能直接调用它。`rg` 在这台环境里不可用，我改用 PowerShell 原生命令枚举主要文件。


Get-ChildItem -Recurse -File | Select-Object -ExpandProperty FullName

rg 不可用且 sandbox 无法启动只读命令；需要递归列出文件来汇总知识库可用内容，是否允许？


Get-ChildItem -Recurse -File | Select-Object -ExpandProperty FullName


Output:
Exit code: 124
Wall time: 10 seconds
Total output lines: 10755
Output:
command timed out after 10043 milliseconds
D:\qqq\zhishiku\llmwiki\.env
D:\qqq\zhishiku\llmwiki\.env.example
D:\qqq\zhishiku\llmwiki\.gitignore
D:\qqq\zhishiku\llmwiki\docker-compose.test.yml
D:\qqq\zhishiku\llmwiki\docker-compose.yml
D:\qqq\zhishiku\llmwiki\LICENSE
D:\qqq\zhishiku\llmwiki\netlify.toml
D:\qqq\zhishiku\llmwiki\OPENAI_GPT54_使用说明.md
D:\qqq\zhishiku\llmwiki\pytest.ini
D:\qqq\zhishiku\llmwiki\README.md
D:\qqq\zhishiku\llmwiki\run-codex.ps1
D:\qqq\zhishiku\llmwiki\run-openai-gpt54-mcp.ps1
D:\qqq\zhishiku\llmwiki\start-local.ps1
D:\qqq\zhishiku\llmwiki\stop-local.ps1
D:\qqq\zhishiku\llmwiki\wiki-page.png
D:\qqq\zhishiku\llmwiki\.github\workflows\test.yml
D:\qqq\zhishiku\llmwiki\.runlogs\api.err.log
D:\qqq\zhishiku\llmwiki\.runlogs\api.out.log
D:\qqq\zhishiku\llmwiki\.runlogs\mcp.err.log
D:\qqq\zhishiku\llmwiki\.runlogs\mcp.out.log
D:\qqq\zhishiku\llmwiki\.runlogs\tunnel.err.log
D:\qqq\zhishiku\llmwiki\.runlogs\tunnel.out.log
D:\qqq\zhishiku\llmwiki\.runlogs\web.err.log
D:\qqq\zhishiku\llmwiki\.runlogs\web.out.log
D:\qqq\zhishiku\llmwiki\.tools\cloudflared.exe
D:\qqq\zhishiku\llmwiki\.tools\codex.exe
D:\qqq\zhishiku\llmwiki\api\auth.py
D:\qqq\zhishiku\llmwiki\api\config.py
D:\qqq\zhishiku\llmwiki\api\deps.py
D:\qqq\zhishiku\llmwiki\api\Dockerfile
D:\qqq\zhishiku\llmwiki\api\main.py
D:\qqq\zhishiku\llmwiki\api\railway.toml
D:\qqq\zhishiku\llmwiki\api\requirements.txt
D:\qqq\zhishiku\llmwiki\api\scoped_db.py
D:\qqq\zhishiku\llmwiki\api\.venv\pyvenv.cfg
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\81d243bd2c585b0f4821__mypyc.cp312-win_amd64.pyd
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\pythoncom.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\PyWin32.chm
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\pywin32.pth
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\pywin32.version.txt
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\six.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\typing_extensions.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\_cffi_backend.cp312-win_amd64.pyd
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\adodbapi.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\ado_consts.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\apibase.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\is64bit.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\license.txt
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\process_connect_string.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\readme.txt
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\schema_table.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\setup.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\__init__.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\db_print.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\db_table_names.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\xls_read.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\xls_write.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\__pycache__\db_print.cpython-312.pyc
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\__pycache__\db_table_names.cpython-312.pyc
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\__pycache__\xls_read.cpython-312.pyc
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\__pycache__\xls_write.cpython-312.pyc
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\adodbapitest.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\adodbapitestconfig.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\dbapi20.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\is64bit.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\setuptestframework.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\test_adodbapi_dbapi20.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\tryconnection.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\__pycache__\adodbapitest.cpython-312.pyc
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D:\qqq\zhishiku\llmwiki\mcp\.venv\Lib\site-packages\aiohappyeyeballs\__init__.py
D:\qqq\zhishiku\llmwiki\mcp\.venv\Lib\site-packages\aiohappyeyeballs\__pycache__\impl.cpython-312.pyc
D:\qqq\zhishiku\llmwiki\mcp\.venv\Lib\site-packages\aiohappyeyeballs\__pycache__\types.cpython-312.pyc
D:\qqq\zhishiku\llmwiki\mcp\.venv\Lib\site-packages\aiohappyeyeballs\__pycache__\utils.cpython-312.pyc
D:\qqq\zhishiku\llmwiki\mcp\.venv\Lib\site-packages\aiohappyeyeballs\__pycache__\_staggered.cpython-312.pyc
D:\qqq\zhishiku\llmwiki\mcp\.venv\Lib\site-packages\aiohappyeyeballs\__pycache__\__init__.cpython-312.pyc
D:\qqq\zhishiku\llmwiki\mcp\.venv\Lib\site-packages\aiohappyeyeballs-2.6.1.dist-info\INSTALLER
D:\qqq\zhishiku\llmwiki\mcp\.venv\Lib\site-packages\aiohappyeyeballs-2.6.1.dist-info\LICENSE
D:\qqq\zhishiku\llmwiki\mcp\.venv\Lib\site-packages\aiohappyeyeballs-2.6.1.dist-info\METADATA
D:\qqq\zhishiku\llmwiki\mcp\.venv\Lib\site-packages\aiohappyeyeballs-2.6.1.dist-info\RECORD
D:\qqq\zhishiku\llmwiki\mcp\.venv\Lib\site-packages\aiohappyeyeballs-2.6.1.dist-info\WHEEL
D:\qqq\zhishiku\llmwiki\


递归枚举命中了 `.venv` 依赖目录，输出过大并超时；已有信息足够识别项目组成，但我再做一次排除依赖目录的浅层清单，避免把第三方包算进知识库内容。


Get-ChildItem -Recurse -File -Exclude *.pyc | Where-Object { $_.FullName -notmatch '\.venv\|node_modules|\.next\|__pycache__' } | Select-Object -First 250 -ExpandProperty FullName

需要排除依赖目录后生成更准确的知识库内容清单，是否允许运行只读枚举？


Get-ChildItem -Recurse -File -Exclude *.pyc | Where-Object { $_.FullName -notmatch '\.venv\|node_modules|\.next\|__pycache__' } | Select-Object -First 250 -ExpandProperty FullName