originator: Codex Desktop

source: exec

model_provider: openai

text:
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.

branch: master


type: message

role: developer

text:
<permissions instructions>
Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.
Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.
</permissions instructions>

text:
<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>


role: user

text:
<environment_context>
  <cwd>D:\qqq\zhishiku\llmwiki</cwd>
  <shell>powershell</shell>
  <current_date>2026-04-11</current_date>
  <timezone>Asia/Shanghai</timezone>
</environment_context>


timezone: Asia/Shanghai

approval_policy: never

type: danger-full-access

personality: pragmatic

mode: default

reasoning_effort: medium

effort: medium

mode: tokens


text: Use MCP server llmwiki only. Read /Wiki/summary-cn-2026-04-11.md and /Wiki/terms-cn-2026-04-11.md and /Wiki/qa-cn-2026-04-11.md. Output exactly VERIFY_NEW=OK if all readable.


Use MCP server llmwiki only. Read /Wiki/summary-cn-2026-04-11.md and /Wiki/terms-cn-2026-04-11.md and /Wiki/qa-cn-2026-04-11.md. Output exactly VERIFY_NEW=OK if all readable.


output: [{"type":"text","text":"Knowledge base 'llmwiki' not found."}]


output: [{"type":"text","text":"Knowledge base 'llmwiki' not found."}]


output: [{"type":"text","text":"Knowledge base 'llmwiki' not found."}]


output: {"result":"# LLM Wiki — How It Works\n\n默认输出语言：中文（简体）。除非用户明确要求其他语言，否则请使用中文完成问答与 Wiki 页面撰写。\n\nYou are connected to an **LLM Wiki** — a personal knowledge workspace where you compile and maintain a structured wiki from raw source documents.\n\n## Architecture\n\n1. **Raw Sources** (path: `/`) — uploaded documents (PDFs, notes, images, spreadsheets). Source of truth. Read-only.\n2. **Compiled Wiki** (path: `/wiki/`) — markdown pages YOU create and maintain. You own this layer.\n3. **Tools** — `search`, `read`, `write`, `delete` — your interface to both layers.\n\n## Wiki Structure\n\nEvery wiki follows this structure. These categories are not suggestions — they are the backbone of the wiki.\n\n### Overview (`/wiki/overview.md`) — THE HUB PAGE\nAlways exists. This is the front page of the wiki. It must contain:\n- A summary of what this wiki covers and its scope\n- **Source count** and page count (update on every ingest)\n- **Key Findings** — the most important insights across all sources\n- **Recent Updates** — last 5-10 actions (ingests, new pages, revisions)\n\nUpdate the Overview after EVERY ingest or major edit. If you only update one page, it should be this one.\n\n### Concepts (`/wiki/concepts/`) — ABSTRACT IDEAS\nPages for theoretical frameworks, methodologies, principles, themes — anything conceptual.\n- `/wiki/concepts/scaling-laws.md`\n- `/wiki/concepts/attention-mechanisms.md`\n- `/wiki/concepts/self-supervised-learning.md`\n\nEach concept page should: define the concept, explain why it matters in context, cite sources, and cross-reference related concepts and entities.\n\n### Entities (`/wiki/entities/`) — CONCRETE THINGS\nPages for people, organizations, products, technologies, papers, datasets — anything you can point to.\n- `/wiki/entities/transformer.md`\n- `/wiki/entities/openai.md`\n- `/wiki/entities/attention-is-all-you-need.md`\n\nEach entity page should: describe what it is, note key facts, cite sources, and cross-reference related concepts and entities.\n\n### Log (`/wiki/log.md`) — CHRONOLOGICAL RECORD\nAlways exists. Append-only. Records every ingest, major edit, and lint pass. Never delete entries.\n\nFormat — each entry starts with a parseable header:\n```\n## [YYYY-MM-DD] ingest | Source Title\n- Created concept page: [Page Title](concepts/page.md)\n- Updated entity page: [Page Title](entities/page.md)\n- Updated overview with new findings\n- Key takeaway: one sentence summary\n\n## [YYYY-MM-DD] query | Question Asked\n- Created new page: [Page Title](concepts/page.md)\n- Finding: one sentence answer\n\n## [YYYY-MM-DD] lint | Health Check\n- Fixed contradiction between X and Y\n- Added missing cross-reference in Z\n```\n\n### Additional Pages\nYou can create pages outside of concepts/ and entities/ when needed:\n- `/wiki/comparisons/x-vs-y.md` — for deep comparisons\n- `/wiki/timeline.md` — for chronological narratives\n\nBut concepts/ and entities/ are the primary categories. When in doubt, file there.\n\n## Page Hierarchy\n\nWiki pages use a parent/child hierarchy via paths:\n- `/wiki/concepts.md` — parent page (optional; summarizes all concepts)\n- `/wiki/concepts/attention.md` — child page\n\nParent pages summarize; child pages go deep. The UI renders this as an expandable tree.\n\n## Writing Standards\n\n**Wiki pages must be substantially richer than a chat response.** They are persistent, curated artifacts.\n\n### Structure\n- Start with a summary paragraph (no H1 — the title is rendered by the UI)\n- Use `##` for major sections, `###` for subsections\n- One idea per section. Bullet points for facts, prose for synthesis.\n\n### Visual Elements — MANDATORY\n\n**Every wiki page MUST include at least one visual element.** A page with only prose is incomplete.\n\n**Mermaid diagrams** — use for ANY structured relationship:\n- Flowcharts for processes, pipelines, decision trees\n- Sequence diagrams for interactions, timelines\n- Quadrant charts for comparisons, trade-off analyses\n- Entity relationship diagrams for people, companies, concepts\n\n````\n```mermaid\ngraph LR\n    A[Input] --> B[Process] --> C[Output]\n```\n````\n\n**Tables** — use for ANY structured comparison:\n- Feature matrices, pros/cons, timelines, metrics\n- If you're listing 3+ items with attributes, it should be a table\n\n**SVG assets** — for custom visuals Mermaid can't express:\n- Create: `write(command=\"create\", path=\"/wiki/\", title=\"diagram.svg\", content=\"<svg>...</svg>\", tags=[\"diagram\"])`\n- Embed in wiki pages: `![Description](diagram.svg)`\n\n### Citations — REQUIRED\n\nEvery factual claim MUST cite its source via markdown footnotes:\n```\nTransformers use self-attention[^1] that scales quadratically[^2].\n\n[^1]: attention-paper.pdf, p.3\n[^2]: scaling-laws.pdf, p.12-14\n```\n\nRules:\n- Use the FULL source filename — never truncate\n- Add page numbers for PDFs: `paper.pdf, p.3`\n- One citation per claim — don't batch unrelated claims\n- Citations render as hoverable popover badges in the UI\n\n### Cross-References\nLink between wiki pages using standard markdown links to other wiki paths.\n\n## Core Workflows\n\n### Ingest a New Source\n1. Read it: `read(path=\"source.pdf\", pages=\"1-10\")`\n2. Discuss key takeaways with the user\n3. Create or update **concept** pages under `/wiki/concepts/`\n4. Create or update **entity** pages under `/wiki/entities/`\n5. Update `/wiki/overview.md` — source count, key findings, recent updates\n6. Append an entry to `/wiki/log.md`\n7. A single source typically touches 5-15 wiki pages — that's expected\n\n### Answer a Question\n1. `search(mode=\"search\", query=\"term\")` to find relevant content\n2. Read relevant wiki pages and sources\n3. Synthesize with citations\n4. If the answer is valuable, file it as a new wiki page — explorations should compound\n5. Append a query entry to `/wiki/log.md`\n\n### Maintain the Wiki (Lint)\nCheck for: contradictions, orphan pages, missing cross-references, stale claims, concepts mentioned but lacking their own page. Append a lint entry to `/wiki/log.md`.\n\n## Available Knowledge Bases\n\n- **Rao1116's Wiki** (`rao1116s-wiki`) — 9 sources, 2 wiki pages"}


output: [{"type":"text","text":"**Summary Cn 2026 04 11**\nType: md | Tags: GEO, 摘要, 2026-04-11 | Version: 0 | Updated: 2026-04-10\n[View in Supavault](http://localhost:3000/wikis/rao1116s-wiki/Wiki/summary-cn-2026-04-11.md)\n\n---\n\n本页汇总两份已上传来源文档中关于 GEO（生成式引擎优化）的核心信息：一份偏方法论与执行指南，另一份偏中国市场、行业测量标准与治理倡议。整体主题是：随着 AI 搜索与大模型成为新的消费决策入口，品牌优化目标正从传统搜索排名与点击，转向在 AI 答案中的可见度、引用、推荐与正确认知。[^1][^2]\n\n```mermaid\ngraph LR\n    A[AI搜索与大模型入口] --> B[消费者提问]\n    B --> C[AI生成答案]\n    C --> D[品牌被提及/引用/推荐]\n    D --> E[GEO测量与优化]\n    E --> F[持续内容、PR、技术与治理]\n```\n\n## 中文要点摘要\n\n- AI 搜索正在把搜索体验从“链接列表”改写为“答案引擎”，品牌的优化目标应从网页排名扩展为答案中的提及、引用与推荐。[^1]\n- GEO 与 LLMO 的侧重点不同：GEO 更面向带联网检索或 RAG 的 AI 搜索系统，优化抓手包括可引用页面、结构化内容与引用来源生态；LLMO 更偏长期训练数据和品牌心智建设。[^1]\n- 中国报告认为 AI 已成为新的媒介触点和消费决策影响者，GEO 的核心问题是“AI 能否正确认知并推荐品牌”。[^2]\n- 行业测量宜先以品牌可见度为基础 KPI，并结合引用率、正文引用率、品牌 AI 认知指数、品牌 AI 认知份额指数等指标进行诊断与竞品分析。[^3]\n- 高质量 AI 认知不是一次性项目，而是“持续测量 + GEO 优化”的系统性工作，需要品牌、优化服务商、监测第三方、媒体平台与技术方协同。[^4]\n\n## 信息缺口\n\n| 缺口 | 说明 | 建议补充来源 |\n|---|---|---|\n| 平台差异 | 两份资料都强调不同 AI 平台表现不同，但缺少对国内外主流模型在同一问题集下的可比实测数据。 | 品牌自建问题库、多模型监测报告 |\n| 行业垂类基准 | 已有测量指标，但缺少不同品类的成熟基准值与预算投入产出区间。 | 垂类 GEO 案例、第三方监测样本 |\n| 长期因果验证 | 资料提出内容、PR、Schema、UGC 等策略，但缺少长期因果归因数据。 | A/B 测试、优化前后追踪报告 |\n| 风险治理细则 | 报告提出反对信息污染和虚假优化，但具体平台治理机制仍需更细规则。 | 行业标准、平台政策、监管文件 |\n\n[^1]: 《Otterly.AI – GEO Guide》中文版.pdf, p.1-11\n[^2]: CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.2, p.10, p.15\n[^3]: CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.35-40\n[^4]: CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.34, p.44-47\n"}]


output: [{"type":"text","text":"**Terms Cn 2026 04 11**\nType: md | Tags: GEO, 术语, 2026-04-11 | Version: 0 | Updated: 2026-04-10\n[View in Supavault](http://localhost:3000/wikis/rao1116s-wiki/Wiki/terms-cn-2026-04-11.md)\n\n---\n\n本页整理两份来源文档中反复出现的 GEO 与 AI 营销术语，便于后续统一口径。术语解释以文档内定义和上下文为准。[^1][^2]\n\n```mermaid\ngraph TD\n    A[GEO术语体系] --> B[目标: 可见/引用/推荐]\n    A --> C[方法: 内容/PR/Schema/UGC/技术]\n    A --> D[测量: 可见度/引用率/认知指数]\n    A --> E[治理: 真实/科学/公平/协作]\n```\n\n## 中文术语表\n\n| 术语 | 定义 | 来源 |\n|---|---|---|\n| GEO（生成式引擎优化） | 将 SEO 逻辑扩展到 AI 搜索和生成式答案环境，目标是在 AI 答案中被提及、引用和推荐。 | 《Otterly.AI – GEO Guide》中文版.pdf, p.4-6；CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.2 |\n| LLMO（大模型优化） | 面向大模型训练数据和长期品牌认知的优化，通常周期更长、变化更慢。 | 《Otterly.AI – GEO Guide》中文版.pdf, p.2-4 |\n| GAIO（生成式 AI 优化） | 更宽泛的概念，指影响 AI 输出或训练结果的一类方法。 | 《Otterly.AI – GEO Guide》中文版.pdf, p.4 |\n| AI 搜索 | 结合大模型和联网检索或 RAG 的搜索形态，会生成答案并可能附带引用来源。 | 《Otterly.AI – GEO Guide》中文版.pdf, p.3 |\n| 答案引擎 | 相对于传统链接列表，AI 搜索直接生成答案，优化目标转为答案中的出现和被引用。 | 《Otterly.AI – GEO Guide》中文版.pdf, p.3-5 |\n| 品牌可见度 | 消费者提问时，品牌被 AI 大模型推荐的概率；公式为 AI 推荐次数除以总提问次数。 | CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.36 |\n| 引用率 | AI 在分析品牌相关内容时引用特定来源信息的概率。 | CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.37 |\n| 正文引用率 | AI 回答正文部分引用特定来源信息的概率，可作为优化考核 KPI。 | CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.38 |\n| 品牌 AI 认知指数 | 综合衡量品牌是否出现以及出现位置的指标，反映消费者通过大模型“看到”品牌的优先程度。 | CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.39 |\n| 品牌 AI 认知份额指数 | 基于品牌 AI 认知指数衡量品牌在 AI 认知中的竞争地位。 | CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.40 |\n| Prompt 不可见 | GEO 中通常无法直接看到用户真实输入的提示词，只能从 AI 输出和引用来源反推需求。 | 《Otterly.AI – GEO Guide》中文版.pdf, p.6, p.16 |\n| Schema / JSON-LD | 结构化数据方法，用于帮助机器理解组织、产品、文章、评论、FAQ 等实体信息。 | 《Otterly.AI – GEO Guide》中文版.pdf, p.8；CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.45 |\n| LLM Referral | 来自 ChatGPT、Perplexity、Gemini、DeepSeek 等 AI 或 LLM 入口的引荐流量。 | 《Otterly.AI – GEO Guide》中文版.pdf, p.5-6, p.14 |\n| GEO 行业倡议 | 主张以用户价值、真实信息、科学优化、公平竞争和开放协作为行业原则。 | CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.47 |\n\n[^1]: 《Otterly.AI – GEO Guide》中文版.pdf, p.1-28\n[^2]: CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.2, p.34-47\n"}]


output: [{"type":"text","text":"**Qa Cn 2026 04 11**\nType: md | Tags: GEO, 问答, 2026-04-11 | Version: 0 | Updated: 2026-04-10\n[View in Supavault](http://localhost:3000/wikis/rao1116s-wiki/Wiki/qa-cn-2026-04-11.md)\n\n---\n\n本页提炼 8 个面向品牌方、市场团队与 GEO 服务商的高价值问题，并给出简短回答与建议追踪来源。回答基于两份已上传来源文档。[^1][^2]\n\n```mermaid\ngraph LR\n    A[高价值问题] --> B[为什么做GEO]\n    A --> C[怎么衡量]\n    A --> D[怎么优化]\n    A --> E[怎么治理风险]\n```\n\n## 高价值问答\n\n| 问题 | 简明回答 | 建议后续来源 |\n|---|---|---|\n| 1. GEO 为什么会成为品牌的新课题？ | 因为 AI 搜索和大模型正在成为消费决策入口，品牌不仅要被用户搜索到，还要被 AI 正确认知、引用和推荐。[^1][^2] | AI 搜索平台流量报告、消费者 AI 使用行为研究 |\n| 2. GEO 和传统 SEO 最大差异是什么？ | SEO 主要优化网页排名和点击，GEO 优化 AI 答案中的提及、引用、推荐、语义关联和可信来源。[^1] | SEO 与 GEO 同题监测样本、搜索点击变化报告 |\n| 3. GEO 的第一优先级指标应是什么？ | 中国报告建议初期以品牌可见度作为核心 KPI，因为它定义清晰、便于量化，也便于作为优化考核目标。[^3] | 品牌可见度历史监测、竞品问题集数据 |\n| 4. 每个问题需要提问多少次才更稳定？ | 报告建议一个关键问题或关键词每个模型不低于 30 次；非头部品牌可能需要 60 次或更高样本量。[^4] | 秒针营销科学院样本量研究、品牌自有复测数据 |\n| 5. 为什么不能只看 API 抓取的模型回答？ | API 回答可能不能代表真实用户界面中的搜索触发、引用展示、地区差异与视觉权重，因此需要基于真实界面监测。[^1] | 平台 UI 监测截图、账号与设备条件记录 |\n| 6. 哪些内容更容易被 AI 引用？ | 具体、可验证、信息密度高、结构清晰、开头直接回答问题，并包含数据、引用、专家观点或权威来源的内容更有利。[^1] | 官网内容审计、权威媒体稿、行业白皮书、专家访谈 |\n| 7. GEO 是否是一次性项目？ | 不是。两份资料都强调 GEO 应是持续测量、持续优化、持续内容与信源建设的系统工程。[^1][^5] | 月度 GEO 仪表盘、问题库迭代记录、优化前后报告 |\n| 8. GEO 的主要风险是什么？ | 风险包括刷排名、信息污染、虚假传播、诋毁竞品和黑帽操纵；行业倡议要求真实、科学、公平和开放协作。[^6] | 行业规范、平台政策、品牌风险案例库 |\n\n## 建议优先追踪来源\n\n| 来源类型 | 用途 |\n|---|---|\n| 多模型问题库监测 | 跟踪品牌可见度、引用率、正文引用率和竞品差异 |\n| 官网与知识库内容审计 | 检查内容是否结构化、可引用、可验证、可抓取 |\n| 权威媒体与行业报告 | 增强品牌作为可信信源的概率 |\n| UGC 与社区讨论 | 观察真实用户问题、评价与潜在风险点 |\n| 平台政策与行业倡议 | 约束优化边界，避免信息污染与虚假优化 |\n\n[^1]: 《Otterly.AI – GEO Guide》中文版.pdf, p.1-11, p.16-23\n[^2]: CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.2, p.9-15\n[^3]: CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.35-36\n[^4]: CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.41-42\n[^5]: CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.34, p.44-45\n[^6]: CAAC&明略科技-2025年中国GEO行业发展报告-251022(2).pdf, p.47\n"}]