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Yao Geo Title Optimizer

Community
yaojingang
yao-geo-title-optimizer

Use when Chinese content teams need GEO title candidates, scoring, compliance review, or title-to-article mapping for articles, pages, FAQs, comparisons, and topic hubs.

Overview

Publisheryaojingang
Repositoryyao-geo-skills
Skill nameyao-geo-title-optimizer
Stars
871
Forks
168
Bundled files
17
LicenseMIT
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.

  • 17 bundled files

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

  • Open source

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

Installation

Install the Yao Geo Title Optimizer 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/yaojingang/yao-geo-skills/tree/main/skills/yao-geo-title-optimizer
  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/yaojingang/yao-geo-skills.git /tmp/yao-geo-skills
mkdir -p .claude/skills
cp -r /tmp/yao-geo-skills/skills/yao-geo-title-optimizer .claude/skills/yao-geo-title-optimizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Yao Geo Title Optimizer 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 Yao Geo Title Optimizer 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 Yao Geo Title Optimizer 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.

yao-geo-title-optimizer

Use this skill when the user needs GEO title generation, title optimization, title scoring, title compliance checks, or title-to-article-structure mapping for Chinese content production.

Do not use this skill for plain copyediting, finished article proofreading, brand slogan creation, or generic SEO metadata when the user does not need a GEO title system.

Inputs

  • Core keyword, question set, brand, competitors, target article type, region, and project date.
  • Whether year or month anchors are allowed.
  • Brand knowledge, competitor knowledge, industry dimensions, evidence sources, and compliance banned terms.
  • Target domestic AI platforms, especially DeepSeek, Kimi, Doubao, Yuanbao, and Tongyi Qianwen.
  • Optional real-data inputs: public URLs, local customer documents, exported platform answers, CMS fields, and evidence snapshots produced by scripts/collect_yao_geo_title_evidence.py.

GEO Title Logic

  1. Parse the main entity, user intent, scenario limit, and decision goal.
  2. Select title structures from list, comparison, decision, recommendation, how-to, brand validation, FAQ, and topic-hub types.
  3. Build a systematic analysis layer before writing titles: authoritative references, analysis dimensions, entity-intent matrix, evidence freshness, platform interpretation assumptions, and coverage gaps.
  4. Run real-data readiness checks: public URL reachability, evidence source freshness, unavailable private data, and platform sampling gaps.
  5. Generate varied title candidates that cover decision words, scenario hooks, evaluation dimensions, question wording, and risk-avoidance wording.
  6. Apply brand isolation. Neutral list, comparison, horizontal review, recommendation, and procurement titles must not contain the target brand or competitor names unless the user explicitly asks for branded comparison.
  7. Apply compliance filtering. Do not use unsupported absolute claims or unsupported recency claims such as "best", "latest", "first", "only", "authoritative", "industry standard", "guaranteed inclusion", or equivalent Chinese terms.
  8. Score titles on intent match, entity clarity, differentiation, citation potential, compliance, and freshness.
  9. Map each title to an article structure, evidence blocks, FAQ prompts, platform sampling plan, and publication checks.

Reference framework: Systematic Reporting Framework.

Domestic Platform Adaptation

  • Yuanbao and Doubao: prefer natural Chinese questions and concrete user scenarios.
  • Tongyi Qianwen and Kimi: prefer clear dimensions, evidence/source orientation, and long-form structure.
  • DeepSeek: prefer logical decision titles with explicit judgment chains.
  • Domestic platforms often treat the title, summary, and first paragraph as the primary understanding entry, so titles should expose the main entity, intent, scene, comparison dimension, and supported time anchor.

Time Anchor Rules

  • Year and month anchors require support from the project date or evidence freshness.
  • Do not create false recency. If evidence is not fresh enough, move the date into the evidence table instead of the title.
  • Avoid batch-level title templates that only swap keywords.

Four-Format Output

Always produce the report quartet:

  • Markdown: complete reviewable source.
  • HTML: white background, fixed table layout, explicit print styles, no viewport overflow.
  • Word DOCX: fixed A4 page size, fixed table widths, no right overflow, and no nine-column title candidate tables.
  • PDF: rendered from the HTML print layout, with page margins, repeatable table headers, and breakable long text.

Renderer Contract

The renderer expects a completed report JSON, not a raw keyword brief. Build the report JSON from the user's brief before running scripts/render_yao_geo_title_optimizer.py.

Required report fields:

  • output_stem, report_title, generated_at, project
  • title_candidates, compliance_checks, structure_map, self_review
  • data_source_audit, platform_sampling_plan, reference_frameworks, analysis_dimensions, entity_intent_matrix, coverage_gaps, publication_checklist

Required project fields:

  • name, module, priority, project_date, region, audience
  • target_platforms, article_types, allow_year_anchor, allow_month_anchor

Required title fields:

  • id, title, type, intent, scenario, platform_fit, why_it_works, rewrite_advice, scores
  • scores.intent_match, scores.entity_clarity, scores.differentiation, scores.citation_potential, scores.compliance, scores.freshness

Recommended depth fields:

  • title_pattern_library
  • evidence_sources
  • evidence_snapshot_path
  • platform_adaptation
  • scenario_selection

Real Data Collection

The skill can work with real data when the operator supplies public URLs, local files, or platform exports. For public URLs, run:

bash
python3 scripts/collect_yao_geo_title_evidence.py --input examples/<case>/report_input.json --output examples/<case>/evidence_snapshot.json

This produces a lightweight evidence snapshot with status code, final URL, sampled byte count, SHA-256 sample hash, page title, meta description, H1, and fetch timestamp. It does not bypass authentication, paywalls, robots controls, or platform anti-bot rules.

Domestic AI platform answers are not automatically collected by this script. For DeepSeek, Kimi, Doubao, Yuanbao, and Tongyi Qianwen, use API access or manual exports and record the query, answer, timestamp, account/region, and citation behavior in the report input.

Word Layout Standard

Word is the strictest artifact. Follow these rules:

  • Use A4 portrait with explicit page margins.
  • Keep every table grid width below the usable page width.
  • Render title candidates as per-title cards or narrow key-value tables instead of wide nine-column tables.
  • Insert break opportunities into URLs, long English product names, and long mixed strings.
  • Run a DOCX structural check before completion: parse word/document.xml, compare w:tblGrid/w:gridCol totals against page width minus margins, and fail on any overflow.

PDF/HTML Layout Standard

  • Use table-layout: fixed, overflow-wrap: anywhere, and word-break: break-word.
  • HTML reports must include a sticky navigation menu that follows the page while scrolling and links to the major report sections.
  • Keep print CSS explicit with A4 margins and smaller table typography.
  • Avoid page-width tables with unbreakable URL or product-name cells.
  • For wide analytical tables, allow wrapping and avoid row-level page-break rules that create large blank areas.

Quality Gate

Before returning files, perform self-review and repair:

  • Confirm all four report files exist.
  • Confirm the DOCX is a valid zip package.
  • Confirm the PDF starts with a valid PDF header, can be parsed, and has at least one page.
  • Confirm all DOCX tables fit within the usable page width.
  • Confirm the report includes depth sections for references, analysis dimensions, entity-intent matrix, coverage gaps, and publication checklist.
  • Confirm the report includes data source audit and platform sampling plan.
  • Confirm HTML includes sticky navigation and section anchors.
  • Confirm neutral titles follow brand isolation.
  • Confirm banned or unsupported authority terms are absent.
  • Confirm output language is Simplified Chinese when the user asks for domestic AI platform examples.

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 Yao Geo Title Optimizer AI skill do?

Use when Chinese content teams need GEO title candidates, scoring, compliance review, or title-to-article mapping for articles, pages, FAQs, comparisons, and topic hubs.

Why use Yao Geo Title Optimizer on TypingMind?

Because you install it once and use it with any model. Yao Geo Title Optimizer 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 Yao Geo Title Optimizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yaojingang/yao-geo-skills/tree/main/skills/yao-geo-title-optimizer. 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 Yao Geo Title Optimizer?

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 Yao Geo Title Optimizer?

As many as you like. As long as a model supports skills, you can use Yao Geo Title Optimizer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Yao Geo Title Optimizer AI skill free?

Yes. It is published on GitHub by yaojingang under the MIT license. 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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