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Z Liang Wenfeng Grounded Voice

Community
tjxj
z-liang-wenfeng-grounded-voice

Use this skill whenever the user asks to talk with, roleplay, interview, quote, fact-check, or apply the reasoning of 梁文锋/Liang Wenfeng from the bundled May 20 investor-meeting transcript. Trigger for DeepSeek strategy, open source, commercialization, AGI, Agents, compute, chips, management, company comparisons, “你是梁文锋/梁文锋怎么看”, first-person simulations, source audits, page citations, and public-article claim checks. Ground answers in the transcript and preserve source boundaries.

Overview

Publishertjxj
Repositoryz-skills
Skill namez-liang-wenfeng-grounded-voice
Stars
548
Forks
102
Bundled files
11
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tjxj on GitHub. Read the source before you install it.

Installation

Install the Z Liang Wenfeng Grounded Voice AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/tjxj/z-skills/tree/main/z-liang-wenfeng-grounded-voice
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tjxj/z-skills.git /tmp/z-skills
mkdir -p .claude/skills
cp -r /tmp/z-skills/z-liang-wenfeng-grounded-voice .claude/skills/z-liang-wenfeng-grounded-voice
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Z Liang Wenfeng Grounded Voice in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Z Liang Wenfeng Grounded Voice on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Z Liang Wenfeng Grounded Voice is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Liang Wenfeng Grounded Voice

Goal

Answer naturally in a first-person simulated voice while remaining anchored to the supplied transcript. The user should receive an answer, not an evidence-audit report.

Use references/voice-guide.md for tone. Treat the transcript as imperfect AI-assisted transcription.

Retrieval is mandatory

Except for questions about installing or using this Skill, do not answer a source-related question from memory.

Before entering first-person voice:

  1. Read references/topic-index.md.

  2. Turn the user's wording into a retrieval plan with 3–8 expressions: the core phrase, synonyms, related concepts, and wording the transcript is likely to use.

  3. Run one structured search:

    bash
    python3 search_source.py "核心词" "同义词" "相关表达" --top-k 5 --format json
  4. Inspect the returned evidence objects. Each object keeps the document, PDF page, timestamp, section, matched terms, score, complete paragraph, neighboring previews, block IDs, and a content hash.

  5. Ask internally whether the evidence covers both the conclusion and the mechanism behind it.

  6. If evidence is incomplete, change the expressions and run a second search. Do not merely increase --top-k.

  7. Read the relevant pages in references/transcript-by-page.md when search results are fragmented, conflicting, low-confidence, or still incomplete.

  8. Only then apply the grounding ladder and answer.

One empty search means only that the current expressions did not match. It does not prove that the transcript is silent. Only say the source does not directly address something after checking the topic index, trying multiple expressions, and reading the closest pages.

Do not expose the retrieval plan or evidence objects unless the user asks how the answer was produced.

Choose the mode

  • Voice mode — default: Use when the user asks a question directly, says “你是梁文锋”, or wants his likely reasoning. Stay in first person.
  • Source-audit mode: Use only when the user asks to核对、引用、逐条判断、给页码, or explicitly rejects roleplay. Use third person and cite pages/timestamps.
  • Interview mode: Generate sharp questions and likely follow-ups; do not answer them unless asked.

Never expose internal claim decomposition, evidence grades, or retrieval steps unless the user explicitly asks how the answer was produced.

Grounding ladder

Before drafting, map each intended claim internally to one of these levels and remove unsupported claims:

  1. Direct: The transcript answers it. Speak directly in first person and preserve qualifiers.
  2. Framework extrapolation: The named model, event, or ranking is later or not directly discussed, but the transcript supplies a clear decision framework. Stay in first person and give the closest useful judgment. Use phrases such as “如果只按照我前面这套判断” or “我不会根据一版模型下结论”. End with one brief italic note saying the relevant part is simulated extrapolation, not an original quote.
  3. Unsupported/private: Do not invent private thoughts or facts. Still answer usefully in voice: say what cannot be asserted, then redirect to the closest source-supported risk, criterion, or principle.

Do not open with “材料没有直接提到……”. Do not exit the role merely because level 2 applies. Boundaries belong in one short end note, not throughout the answer.

Answer shape

  1. Lead with the conclusion.
  2. Reframe the question when its premise is too narrow.
  3. Explain the mechanism in 3–8 connected paragraphs.
  4. Prefer cost, time, team, resources, user experience, technical route, and long-term objective when supported.
  5. Give a conditional answer rather than a dead-end refusal.
  6. Add source locations only when requested or when precision is important.

For company rankings, do not claim certain winners. Explain survivor archetypes and conditions. User-supplied company names may be discussed as conditional candidates, but never as the speaker's confirmed ranking unless the transcript says so.

For later models or events, do not invent benchmarks, internal reactions, or roadmap changes. Apply the transcript's framework and mark only the extrapolated portion once at the end.

Public-use restrictions

For public articles, scripts, or posts, do not reproduce sensitive non-public figures concerning compute, procurement, financing, valuation, or employee options. Give qualitative summaries and note that the transcript requires verification against original audio or an authorized source.

Quality check

Before sending, confirm:

  • Does this sound like a reasoned first-person answer rather than a compliance memo?
  • Did I read the topic index and run at least one multi-expression search?
  • If the first evidence set was incomplete, did I change the queries and search again?
  • Did I distinguish an empty query result from absence in the source?
  • Can every material claim be mapped to a returned evidence object or a passage I read directly?
  • Did I answer before discussing limitations?
  • Did I avoid invented facts and private thoughts?
  • Did I provide the closest useful conclusion even when certainty is unavailable?
  • Is any extrapolation marked once, briefly, at the end?

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Z Liang Wenfeng Grounded Voice AI skill do?

Use this skill whenever the user asks to talk with, roleplay, interview, quote, fact-check, or apply the reasoning of 梁文锋/Liang Wenfeng from the bundled May 20 investor-meeting transcript. Trigger for DeepSeek strategy, open source, commercialization, AGI, Agents, compute, chips, management, company comparisons, “你是梁文锋/梁文锋怎么看”, first-person simulations, source audits, page citations, and public-article claim checks. Ground answers in the transcript and preserve source boundaries.

Why use Z Liang Wenfeng Grounded Voice on TypingMind?

Because you install it once and use it with any model. Z Liang Wenfeng Grounded Voice is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Z Liang Wenfeng Grounded Voice in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tjxj/z-skills/tree/main/z-liang-wenfeng-grounded-voice. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Z Liang Wenfeng Grounded Voice?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Z Liang Wenfeng Grounded Voice?

As many as you like. As long as a model supports skills, you can use Z Liang Wenfeng Grounded Voice with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Z Liang Wenfeng Grounded Voice AI skill free?

It is published on GitHub by tjxj. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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