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    Hugin-Z/README.md

    Hugin

    AI application & agent engineering — I build tool-using agents for real-world decisions, grounded by external data, explicit state, evidence, and human-visible uncertainty.

    AI 应用 / Agent 工程。做能查真实数据、保留证据与不确定性、最后给出可执行结果的 Agent。

    My background is in GIS, digital twins, and government / enterprise delivery. That makes me care less about how fluent an AI looks, and more about whether its output can be traced, verified, and acted on.

    What I build

    The model is not the source of truth.

    Train schedules come from railway data. Routes come from map services. Project claims come from source documents. Knowledge retrieval comes from the local corpus.

    The model plans and reasons; tools, contracts, and state decide what is trustworthy enough to become an answer.

    I tend to work eval/fixture-first, keep important state explicit, put gates around high-impact decisions, and make documentation match actual runtime behavior.

    Selected work

    • trip-decider — An evidence-constrained travel planning agent. It verifies rail, local transit, prices, place identity, and return constraints before they enter an itinerary; unknown and conflicting facts remain visible instead of being silently filled by the model. Also usable as an itinerary verifier.

    • Evidence Runtime — An evidence-constrained agent workflow for turning source materials into auditable deliverables: evidence readiness → constrained generation → Claim Audit → delivery gate. The writer can be an LLM or a human; unsupported content does not silently become a deliverable.

    • agentic-kb-lite — A lightweight local knowledge base built around ripgrep + an LLM reasoning loop instead of a vector stack. Structure carries part of the retrieval semantics; fixtures keep agent behavior reproducible and inspectable.

    • tender-writer-v4 — A state-machine workflow for Chinese government technical-bid writing. Five auditable stages, explicit human gates, and structured tracking from scoring requirements to draft assembly.

    Other experiments

    • 几境 / Nine Realms — A bilingual, nine-tier model for making the fuzzy idea of “being good at AI” more concrete and testable.

    Earlier domain work includes client-deployed planning-compliance tooling and a Rust/Tauri desktop application for agricultural land-use optimization.

    Verification > generation. If an agent cannot tell what it knows, where it came from, and what still needs checking, I don't consider the workflow finished.

    Pinned Loading

    1. trip-decidertrip-deciderPublic

      Evidence-constrained travel planning agent that verifies real-world facts before they enter an itinerary.

      Python

    2. evidence-runtimeevidence-runtimePublic

      Evidence-constrained agent workflow: project materials → evidence readiness → constrained writing → Claim Audit → gated DOCX.

      Python 1

    3. agentic-kb-liteagentic-kb-litePublic

      基于 ripgrep + LLM 的轻量个人/部门知识库；Lightweight local knowledge base using ripgrep + LLM (agent loop, multi-modal, no vectors, no external API)

      Python 14 2

    4. tender-writer-v4tender-writer-v4Public

      把政府类项目技术标的首轮结构化拆解与初稿组装压缩到分钟级;正式投标仍需人工扩写、校核和审稿。

      Python 8

    5. solution-draftersolution-drafterPublic

      可扩展的中文政企方案文档生成 Skill 框架 · 三层解耦架构 + 五阶段工作流(信息抽取→资料获取→模板填充→内容生成→评审修订)

      Python

    6. jijingjijingPublic

      几境 · AI 修为天梯 / A cultivation ladder for AI fluency

      HTML 1

    , 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
     blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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      Hugin-Z/README.md
      Hugin
      AI application & agent engineering — I build tool-using agents for real-world decisions, grounded by external data, explicit state, evidence, and human-visible uncertainty.
      AI 应用 / Agent 工程。做能查真实数据、保留证据与不确定性、最后给出可执行结果的 Agent。
      My background is in GIS, digital twins, and government / enterprise delivery. That makes me care less about how fluent an AI looks, and more about whether its output can be traced, verified, and acted on.
      What I build
      The model is not the source of truth.
      Train schedules come from railway data. Routes come from map services. Project claims come from source documents. Knowledge retrieval comes from the local corpus.
      The model plans and reasons; tools, contracts, and state decide what is trustworthy enough to become an answer.
      I tend to work eval/fixture-first, keep important state explicit, put gates around high-impact decisions, and make documentation match actual runtime behavior.
      Selected work
      • trip-decider — An evidence-constrained travel planning agent. It verifies rail, local transit, prices, place identity, and return constraints before they enter an itinerary; unknown and conflicting facts remain visible instead of being silently filled by the model. Also usable as an itinerary verifier.
      • Evidence Runtime — An evidence-constrained agent workflow for turning source materials into auditable deliverables: evidence readiness → constrained generation → Claim Audit → delivery gate. The writer can be an LLM or a human; unsupported content does not silently become a deliverable.
      • agentic-kb-lite — A lightweight local knowledge base built around ripgrep + an LLM reasoning loop instead of a vector stack. Structure carries part of the retrieval semantics; fixtures keep agent behavior reproducible and inspectable.
      • tender-writer-v4 — A state-machine workflow for Chinese government technical-bid writing. Five auditable stages, explicit human gates, and structured tracking from scoring requirements to draft assembly.
      Other experiments
      • 几境 / Nine Realms — A bilingual, nine-tier model for making the fuzzy idea of “being good at AI” more concrete and testable.
      Earlier domain work includes client-deployed planning-compliance tooling and a Rust/Tauri desktop application for agricultural land-use optimization.
      Verification > generation.
If an agent cannot tell what it knows, where it came from, and what still needs checking, I don't consider the workflow finished.
      
Pinned
Loading
      1. trip-decidertrip-deciderPublic
        
Evidence-constrained travel planning agent that verifies real-world facts before they enter an itinerary.

        Python
      2. evidence-runtimeevidence-runtimePublic
        
Evidence-constrained agent workflow: project materials → evidence readiness → constrained writing → Claim Audit → gated DOCX.

        Python
1

      3. agentic-kb-liteagentic-kb-litePublic
        
基于 ripgrep + LLM 的轻量个人/部门知识库；Lightweight local knowledge base using ripgrep + LLM (agent loop, multi-modal, no vectors, no external API)

        Python
14

2

      4. tender-writer-v4tender-writer-v4Public
        
把政府类项目技术标的首轮结构化拆解与初稿组装压缩到分钟级;正式投标仍需人工扩写、校核和审稿。

        Python
8

      5. solution-draftersolution-drafterPublic
        
可扩展的中文政企方案文档生成 Skill 框架 · 三层解耦架构 + 五阶段工作流(信息抽取→资料获取→模板填充→内容生成→评审修订)

        Python
      6. jijingjijingPublic
        
几境 · AI 修为天梯 / A cultivation ladder for AI fluency

        HTML
1

      
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

      Skip to content
      View Hugin-Z's full-sized avatar
      6
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 · 1
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        Hugin-Z/README.md
        Hugin
        AI application & agent engineering — I build tool-using agents for real-world decisions, grounded by external data, explicit state, evidence, and human-visible uncertainty.
        AI 应用 / Agent 工程。做能查真实数据、保留证据与不确定性、最后给出可执行结果的 Agent。
        My background is in GIS, digital twins, and government / enterprise delivery. That makes me care less about how fluent an AI looks, and more about whether its output can be traced, verified, and acted on.
        What I build
        The model is not the source of truth.
        Train schedules come from railway data. Routes come from map services. Project claims come from source documents. Knowledge retrieval comes from the local corpus.
        The model plans and reasons; tools, contracts, and state decide what is trustworthy enough to become an answer.
        I tend to work eval/fixture-first, keep important state explicit, put gates around high-impact decisions, and make documentation match actual runtime behavior.
        Selected work
        • trip-decider — An evidence-constrained travel planning agent. It verifies rail, local transit, prices, place identity, and return constraints before they enter an itinerary; unknown and conflicting facts remain visible instead of being silently filled by the model. Also usable as an itinerary verifier.
        • Evidence Runtime — An evidence-constrained agent workflow for turning source materials into auditable deliverables: evidence readiness → constrained generation → Claim Audit → delivery gate. The writer can be an LLM or a human; unsupported content does not silently become a deliverable.
        • agentic-kb-lite — A lightweight local knowledge base built around ripgrep + an LLM reasoning loop instead of a vector stack. Structure carries part of the retrieval semantics; fixtures keep agent behavior reproducible and inspectable.
        • tender-writer-v4 — A state-machine workflow for Chinese government technical-bid writing. Five auditable stages, explicit human gates, and structured tracking from scoring requirements to draft assembly.
        Other experiments
        • 几境 / Nine Realms — A bilingual, nine-tier model for making the fuzzy idea of “being good at AI” more concrete and testable.
        Earlier domain work includes client-deployed planning-compliance tooling and a Rust/Tauri desktop application for agricultural land-use optimization.
        Verification > generation.
If an agent cannot tell what it knows, where it came from, and what still needs checking, I don't consider the workflow finished.
        
Pinned
Loading
        1. trip-decidertrip-deciderPublic
          
Evidence-constrained travel planning agent that verifies real-world facts before they enter an itinerary.

          Python
        2. evidence-runtimeevidence-runtimePublic
          
Evidence-constrained agent workflow: project materials → evidence readiness → constrained writing → Claim Audit → gated DOCX.

          Python
1

        3. agentic-kb-liteagentic-kb-litePublic
          
基于 ripgrep + LLM 的轻量个人/部门知识库；Lightweight local knowledge base using ripgrep + LLM (agent loop, multi-modal, no vectors, no external API)

          Python
14

2

        4. tender-writer-v4tender-writer-v4Public
          
把政府类项目技术标的首轮结构化拆解与初稿组装压缩到分钟级;正式投标仍需人工扩写、校核和审稿。

          Python
8

        5. solution-draftersolution-drafterPublic
          
可扩展的中文政企方案文档生成 Skill 框架 · 三层解耦架构 + 五阶段工作流(信息抽取→资料获取→模板填充→内容生成→评审修订)

          Python
        6. jijingjijingPublic
          
几境 · AI 修为天梯 / A cultivation ladder for AI fluency

          HTML
1

        
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

        Skip to content
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        6
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 · 1
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          Hugin-Z/README.md
          Hugin
          AI application & agent engineering — I build tool-using agents for real-world decisions, grounded by external data, explicit state, evidence, and human-visible uncertainty.
          AI 应用 / Agent 工程。做能查真实数据、保留证据与不确定性、最后给出可执行结果的 Agent。
          My background is in GIS, digital twins, and government / enterprise delivery. That makes me care less about how fluent an AI looks, and more about whether its output can be traced, verified, and acted on.
          What I build
          The model is not the source of truth.
          Train schedules come from railway data. Routes come from map services. Project claims come from source documents. Knowledge retrieval comes from the local corpus.
          The model plans and reasons; tools, contracts, and state decide what is trustworthy enough to become an answer.
          I tend to work eval/fixture-first, keep important state explicit, put gates around high-impact decisions, and make documentation match actual runtime behavior.
          Selected work
          • trip-decider — An evidence-constrained travel planning agent. It verifies rail, local transit, prices, place identity, and return constraints before they enter an itinerary; unknown and conflicting facts remain visible instead of being silently filled by the model. Also usable as an itinerary verifier.
          • Evidence Runtime — An evidence-constrained agent workflow for turning source materials into auditable deliverables: evidence readiness → constrained generation → Claim Audit → delivery gate. The writer can be an LLM or a human; unsupported content does not silently become a deliverable.
          • agentic-kb-lite — A lightweight local knowledge base built around ripgrep + an LLM reasoning loop instead of a vector stack. Structure carries part of the retrieval semantics; fixtures keep agent behavior reproducible and inspectable.
          • tender-writer-v4 — A state-machine workflow for Chinese government technical-bid writing. Five auditable stages, explicit human gates, and structured tracking from scoring requirements to draft assembly.
          Other experiments
          • 几境 / Nine Realms — A bilingual, nine-tier model for making the fuzzy idea of “being good at AI” more concrete and testable.
          Earlier domain work includes client-deployed planning-compliance tooling and a Rust/Tauri desktop application for agricultural land-use optimization.
          Verification > generation.
If an agent cannot tell what it knows, where it came from, and what still needs checking, I don't consider the workflow finished.
          
Pinned
Loading
          1. trip-decidertrip-deciderPublic
            
Evidence-constrained travel planning agent that verifies real-world facts before they enter an itinerary.

            Python
          2. evidence-runtimeevidence-runtimePublic
            
Evidence-constrained agent workflow: project materials → evidence readiness → constrained writing → Claim Audit → gated DOCX.

            Python
1

          3. agentic-kb-liteagentic-kb-litePublic
            
基于 ripgrep + LLM 的轻量个人/部门知识库；Lightweight local knowledge base using ripgrep + LLM (agent loop, multi-modal, no vectors, no external API)

            Python
14

2

          4. tender-writer-v4tender-writer-v4Public
            
把政府类项目技术标的首轮结构化拆解与初稿组装压缩到分钟级;正式投标仍需人工扩写、校核和审稿。

            Python
8

          5. solution-draftersolution-drafterPublic
            
可扩展的中文政企方案文档生成 Skill 框架 · 三层解耦架构 + 五阶段工作流(信息抽取→资料获取→模板填充→内容生成→评审修订)

            Python
          6. jijingjijingPublic
            
几境 · AI 修为天梯 / A cultivation ladder for AI fluency

            HTML
1

          
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

          Skip to content
          View Hugin-Z's full-sized avatar
          6
followers
 · 1
following

            
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            Hugin-Z/README.md
            Hugin
            AI application & agent engineering — I build tool-using agents for real-world decisions, grounded by external data, explicit state, evidence, and human-visible uncertainty.
            AI 应用 / Agent 工程。做能查真实数据、保留证据与不确定性、最后给出可执行结果的 Agent。
            My background is in GIS, digital twins, and government / enterprise delivery. That makes me care less about how fluent an AI looks, and more about whether its output can be traced, verified, and acted on.
            What I build
            The model is not the source of truth.
            Train schedules come from railway data. Routes come from map services. Project claims come from source documents. Knowledge retrieval comes from the local corpus.
            The model plans and reasons; tools, contracts, and state decide what is trustworthy enough to become an answer.
            I tend to work eval/fixture-first, keep important state explicit, put gates around high-impact decisions, and make documentation match actual runtime behavior.
            Selected work
            • trip-decider — An evidence-constrained travel planning agent. It verifies rail, local transit, prices, place identity, and return constraints before they enter an itinerary; unknown and conflicting facts remain visible instead of being silently filled by the model. Also usable as an itinerary verifier.
            • Evidence Runtime — An evidence-constrained agent workflow for turning source materials into auditable deliverables: evidence readiness → constrained generation → Claim Audit → delivery gate. The writer can be an LLM or a human; unsupported content does not silently become a deliverable.
            • agentic-kb-lite — A lightweight local knowledge base built around ripgrep + an LLM reasoning loop instead of a vector stack. Structure carries part of the retrieval semantics; fixtures keep agent behavior reproducible and inspectable.
            • tender-writer-v4 — A state-machine workflow for Chinese government technical-bid writing. Five auditable stages, explicit human gates, and structured tracking from scoring requirements to draft assembly.
            Other experiments
            • 几境 / Nine Realms — A bilingual, nine-tier model for making the fuzzy idea of “being good at AI” more concrete and testable.
            Earlier domain work includes client-deployed planning-compliance tooling and a Rust/Tauri desktop application for agricultural land-use optimization.
            Verification > generation.
If an agent cannot tell what it knows, where it came from, and what still needs checking, I don't consider the workflow finished.
            
Pinned
Loading
            1. trip-decidertrip-deciderPublic
              
Evidence-constrained travel planning agent that verifies real-world facts before they enter an itinerary.

              Python
            2. evidence-runtimeevidence-runtimePublic
              
Evidence-constrained agent workflow: project materials → evidence readiness → constrained writing → Claim Audit → gated DOCX.

              Python
1

            3. agentic-kb-liteagentic-kb-litePublic
              
基于 ripgrep + LLM 的轻量个人/部门知识库；Lightweight local knowledge base using ripgrep + LLM (agent loop, multi-modal, no vectors, no external API)

              Python
14

2

            4. tender-writer-v4tender-writer-v4Public
              
把政府类项目技术标的首轮结构化拆解与初稿组装压缩到分钟级;正式投标仍需人工扩写、校核和审稿。

              Python
8

            5. solution-draftersolution-drafterPublic
              
可扩展的中文政企方案文档生成 Skill 框架 · 三层解耦架构 + 五阶段工作流(信息抽取→资料获取→模板填充→内容生成→评审修订)

              Python
            6. jijingjijingPublic
              
几境 · AI 修为天梯 / A cultivation ladder for AI fluency

              HTML
1

            
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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              Hugin-Z/README.md
              Hugin
              AI application & agent engineering — I build tool-using agents for real-world decisions, grounded by external data, explicit state, evidence, and human-visible uncertainty.
              AI 应用 / Agent 工程。做能查真实数据、保留证据与不确定性、最后给出可执行结果的 Agent。
              My background is in GIS, digital twins, and government / enterprise delivery. That makes me care less about how fluent an AI looks, and more about whether its output can be traced, verified, and acted on.
              What I build
              The model is not the source of truth.
              Train schedules come from railway data. Routes come from map services. Project claims come from source documents. Knowledge retrieval comes from the local corpus.
              The model plans and reasons; tools, contracts, and state decide what is trustworthy enough to become an answer.
              I tend to work eval/fixture-first, keep important state explicit, put gates around high-impact decisions, and make documentation match actual runtime behavior.
              Selected work
              • trip-decider — An evidence-constrained travel planning agent. It verifies rail, local transit, prices, place identity, and return constraints before they enter an itinerary; unknown and conflicting facts remain visible instead of being silently filled by the model. Also usable as an itinerary verifier.
              • Evidence Runtime — An evidence-constrained agent workflow for turning source materials into auditable deliverables: evidence readiness → constrained generation → Claim Audit → delivery gate. The writer can be an LLM or a human; unsupported content does not silently become a deliverable.
              • agentic-kb-lite — A lightweight local knowledge base built around ripgrep + an LLM reasoning loop instead of a vector stack. Structure carries part of the retrieval semantics; fixtures keep agent behavior reproducible and inspectable.
              • tender-writer-v4 — A state-machine workflow for Chinese government technical-bid writing. Five auditable stages, explicit human gates, and structured tracking from scoring requirements to draft assembly.
              Other experiments
              • 几境 / Nine Realms — A bilingual, nine-tier model for making the fuzzy idea of “being good at AI” more concrete and testable.
              Earlier domain work includes client-deployed planning-compliance tooling and a Rust/Tauri desktop application for agricultural land-use optimization.
              Verification > generation.
If an agent cannot tell what it knows, where it came from, and what still needs checking, I don't consider the workflow finished.
              
Pinned
Loading
              1. trip-decidertrip-deciderPublic
                
Evidence-constrained travel planning agent that verifies real-world facts before they enter an itinerary.

                Python
              2. evidence-runtimeevidence-runtimePublic
                
Evidence-constrained agent workflow: project materials → evidence readiness → constrained writing → Claim Audit → gated DOCX.

                Python
1

              3. agentic-kb-liteagentic-kb-litePublic
                
基于 ripgrep + LLM 的轻量个人/部门知识库；Lightweight local knowledge base using ripgrep + LLM (agent loop, multi-modal, no vectors, no external API)

                Python
14

2

              4. tender-writer-v4tender-writer-v4Public
                
把政府类项目技术标的首轮结构化拆解与初稿组装压缩到分钟级;正式投标仍需人工扩写、校核和审稿。

                Python
8

              5. solution-draftersolution-drafterPublic
                
可扩展的中文政企方案文档生成 Skill 框架 · 三层解耦架构 + 五阶段工作流(信息抽取→资料获取→模板填充→内容生成→评审修订)

                Python
              6. jijingjijingPublic
                
几境 · AI 修为天梯 / A cultivation ladder for AI fluency

                HTML
1

              
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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                Hugin-Z/README.md
                Hugin
                AI application & agent engineering — I build tool-using agents for real-world decisions, grounded by external data, explicit state, evidence, and human-visible uncertainty.
                AI 应用 / Agent 工程。做能查真实数据、保留证据与不确定性、最后给出可执行结果的 Agent。
                My background is in GIS, digital twins, and government / enterprise delivery. That makes me care less about how fluent an AI looks, and more about whether its output can be traced, verified, and acted on.
                What I build
                The model is not the source of truth.
                Train schedules come from railway data. Routes come from map services. Project claims come from source documents. Knowledge retrieval comes from the local corpus.
                The model plans and reasons; tools, contracts, and state decide what is trustworthy enough to become an answer.
                I tend to work eval/fixture-first, keep important state explicit, put gates around high-impact decisions, and make documentation match actual runtime behavior.
                Selected work
                • trip-decider — An evidence-constrained travel planning agent. It verifies rail, local transit, prices, place identity, and return constraints before they enter an itinerary; unknown and conflicting facts remain visible instead of being silently filled by the model. Also usable as an itinerary verifier.
                • Evidence Runtime — An evidence-constrained agent workflow for turning source materials into auditable deliverables: evidence readiness → constrained generation → Claim Audit → delivery gate. The writer can be an LLM or a human; unsupported content does not silently become a deliverable.
                • agentic-kb-lite — A lightweight local knowledge base built around ripgrep + an LLM reasoning loop instead of a vector stack. Structure carries part of the retrieval semantics; fixtures keep agent behavior reproducible and inspectable.
                • tender-writer-v4 — A state-machine workflow for Chinese government technical-bid writing. Five auditable stages, explicit human gates, and structured tracking from scoring requirements to draft assembly.
                Other experiments
                • 几境 / Nine Realms — A bilingual, nine-tier model for making the fuzzy idea of “being good at AI” more concrete and testable.
                Earlier domain work includes client-deployed planning-compliance tooling and a Rust/Tauri desktop application for agricultural land-use optimization.
                Verification > generation.
If an agent cannot tell what it knows, where it came from, and what still needs checking, I don't consider the workflow finished.
                
Pinned
Loading
                1. trip-decidertrip-deciderPublic
                  
Evidence-constrained travel planning agent that verifies real-world facts before they enter an itinerary.

                  Python
                2. evidence-runtimeevidence-runtimePublic
                  
Evidence-constrained agent workflow: project materials → evidence readiness → constrained writing → Claim Audit → gated DOCX.

                  Python
1

                3. agentic-kb-liteagentic-kb-litePublic
                  
基于 ripgrep + LLM 的轻量个人/部门知识库；Lightweight local knowledge base using ripgrep + LLM (agent loop, multi-modal, no vectors, no external API)

                  Python
14

2

                4. tender-writer-v4tender-writer-v4Public
                  
把政府类项目技术标的首轮结构化拆解与初稿组装压缩到分钟级;正式投标仍需人工扩写、校核和审稿。

                  Python
8

                5. solution-draftersolution-drafterPublic
                  
可扩展的中文政企方案文档生成 Skill 框架 · 三层解耦架构 + 五阶段工作流(信息抽取→资料获取→模板填充→内容生成→评审修订)

                  Python
                6. jijingjijingPublic
                  
几境 · AI 修为天梯 / A cultivation ladder for AI fluency

                  HTML
1

                
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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                6
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 · 1
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                  Hugin-Z/README.md
                  Hugin
                  AI application & agent engineering — I build tool-using agents for real-world decisions, grounded by external data, explicit state, evidence, and human-visible uncertainty.
                  AI 应用 / Agent 工程。做能查真实数据、保留证据与不确定性、最后给出可执行结果的 Agent。
                  My background is in GIS, digital twins, and government / enterprise delivery. That makes me care less about how fluent an AI looks, and more about whether its output can be traced, verified, and acted on.
                  What I build
                  The model is not the source of truth.
                  Train schedules come from railway data. Routes come from map services. Project claims come from source documents. Knowledge retrieval comes from the local corpus.
                  The model plans and reasons; tools, contracts, and state decide what is trustworthy enough to become an answer.
                  I tend to work eval/fixture-first, keep important state explicit, put gates around high-impact decisions, and make documentation match actual runtime behavior.
                  Selected work
                  • trip-decider — An evidence-constrained travel planning agent. It verifies rail, local transit, prices, place identity, and return constraints before they enter an itinerary; unknown and conflicting facts remain visible instead of being silently filled by the model. Also usable as an itinerary verifier.
                  • Evidence Runtime — An evidence-constrained agent workflow for turning source materials into auditable deliverables: evidence readiness → constrained generation → Claim Audit → delivery gate. The writer can be an LLM or a human; unsupported content does not silently become a deliverable.
                  • agentic-kb-lite — A lightweight local knowledge base built around ripgrep + an LLM reasoning loop instead of a vector stack. Structure carries part of the retrieval semantics; fixtures keep agent behavior reproducible and inspectable.
                  • tender-writer-v4 — A state-machine workflow for Chinese government technical-bid writing. Five auditable stages, explicit human gates, and structured tracking from scoring requirements to draft assembly.
                  Other experiments
                  • 几境 / Nine Realms — A bilingual, nine-tier model for making the fuzzy idea of “being good at AI” more concrete and testable.
                  Earlier domain work includes client-deployed planning-compliance tooling and a Rust/Tauri desktop application for agricultural land-use optimization.
                  Verification > generation.
If an agent cannot tell what it knows, where it came from, and what still needs checking, I don't consider the workflow finished.
                  
Pinned
Loading
                  1. trip-decidertrip-deciderPublic
                    
Evidence-constrained travel planning agent that verifies real-world facts before they enter an itinerary.

                    Python
                  2. evidence-runtimeevidence-runtimePublic
                    
Evidence-constrained agent workflow: project materials → evidence readiness → constrained writing → Claim Audit → gated DOCX.

                    Python
1

                  3. agentic-kb-liteagentic-kb-litePublic
                    
基于 ripgrep + LLM 的轻量个人/部门知识库；Lightweight local knowledge base using ripgrep + LLM (agent loop, multi-modal, no vectors, no external API)

                    Python
14

2

                  4. tender-writer-v4tender-writer-v4Public
                    
把政府类项目技术标的首轮结构化拆解与初稿组装压缩到分钟级;正式投标仍需人工扩写、校核和审稿。

                    Python
8

                  5. solution-draftersolution-drafterPublic
                    
可扩展的中文政企方案文档生成 Skill 框架 · 三层解耦架构 + 五阶段工作流(信息抽取→资料获取→模板填充→内容生成→评审修订)

                    Python
                  6. jijingjijingPublic
                    
几境 · AI 修为天梯 / A cultivation ladder for AI fluency

                    HTML
1