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demo-6-article-kg

Demo skill that web-fetches two Decoding AI knowledge-graph articles, has the agent itself distill them into a typed entity/relation graph, and renders an interactive dark-themed force-directed KG into one self-contained kg.html — no graph library, no CDN.

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价格未确认★ 347 GitHub Stars目录更新于 · 2026年9月3日agent-skill

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The hardcore one: turn two live articles into a knowledge graph you can play with in the browser. You do all of it — you are the extractor (no NLP library) and you are the renderer (no graph library, no CDN, no framework). Two artifacts, one page that works the instant it lands.

Work the steps in order. Do not skip ahead to the HTML.

Step 0 — paths and plan

Both artifacts go in the output directory named in the Output default line at the very end of these instructions — normally .decode/outputs/, but a destination the human named always wins. Resolve it once, call it <OUT>, and use <OUT> everywhere below:

<OUT>/graph.json — the extraction
<OUT>/kg.html — the page

Put the pipeline in todo_write as five items (fetch, extract, render, verify, report) and write the real <OUT> path into the first one so you never re-derive it.

Step 1 — fetch the two sources

One web_fetch per URL (the tool returns Markdown):

Keep the title and body prose. Ignore navigation, subscribe buttons, CTAs, comments, footers and trailing "read more" lists. If a fetch fails or comes back truncated, say so plainly and work with what you have — never invent content you did not fetch.

Step 2 — extract one merged graph

Distill ONE graph across both articles: an entity discussed in both articles is ONE node, not two. Write it to <OUT>/graph.json:

{
  "nodes": [
    {
      "id": "Ontology",
      "type": "concept",
      "desc": "one sentence in your own words"
    },
    { "id": "Neo4j", "type": "tool", "desc": "one sentence in your own words" }
  ],
  "edges": [{ "source": "Ontology", "target": "KG drift", "label": "prevents" }]
}

20–35 nodes. The key ideas, not every noun.
type is exactly one of concept / tool / pattern / problem.
desc is one sentence of your own synthesis, not a quote.
Edges are directed; label is a short verb phrase (prevents, stores, queries).
Every source and target matches a node id exactly — ids are case-sensitive.
No orphans: every node has at least one edge.

Before moving on, re-read what you wrote and confirm the node count is in range, every edge endpoint exists, and no node is orphaned. Fixing it here is cheap; fixing it after the page is written is not.

Step 3 — write the page

One write call produces a finished file. There is no second pass. The data goes in as a literal — the actual { "nodes": [...], "edges": [...] } you just wrote to graph.json, copied in full. Never emit {{GRAPH}}, <DATA>, /* graph here */, ..., or any token you intend to substitute later, and never write an unexpanded template string. If you catch yourself planning to "fill in the data next", stop and write the whole file instead.

Author <OUT>/kg.html in this order — each section complete before the next:

<head>: <title>, and one <style> block. No external stylesheet, script, or font.
<header>: the page title, the two article URLs as links, and the type legend (a colored dot plus the type name, four of them).
An empty <svg> filling the viewport, and an empty detail card <div> positioned on the right.
One <script> block, in this order: the data, the model, the drawing, the simulation, the interactions, the start.

What goes in the script, in order

Data. const GRAPH = followed by the literal object. This is the first statement in the block.

Model. Map each node to an object carrying x, y, vx, vy and degree. Seed positions on a circle around the viewport center — never all at one point, or the repulsion divides by zero. Resolve every edge's source/target string to its node object once, up front, so the loop never searches by id. Count degree while you do it. Build a neighbor set per node id for the hover step.

Drawing. Create the SVG elements once, before the loop starts: a <line> and a small <text> per edge, and a <g> per node holding a <circle> and a <text> label. Keep references to them. The loop only updates coordinates and classes — it never creates or destroys elements. Order the groups edges → edge labels → nodes so nodes sit on top. Radius is 6 + min(9, degree * 1.4); fill is the node's type color.

Simulation. A hand-rolled force simulation in vanilla JS — no library, no framework. One tick() applying three forces, then integrating:

Repulsion, every node pair: magnitude 5000 / distanceSquared, pushing apart along the line between them. Clamp distanceSquared to a minimum of 1 and jitter coincident nodes.
Springs, along each edge: pull proportional to (distance - 110) * 0.01.
Centering: nudge each node toward the viewport center by 0.0015 of its offset.
Integrate: multiply velocity by 0.86 damping, add to position, clamp inside the viewport with a margin so nothing hides under the header.

Scale all forces by an alpha that starts at 1 and decays ~0.5% per tick toward a floor of about 0.06 — that floor is what keeps the layout gently alive instead of frozen. Run ~250 ticks before the first paint so the page opens settled, then requestAnimationFrame a loop of tick + draw.

Interactions.

Drag: pointerdown on a node pins it and captures the pointer; pointermove sets its position and zeroes its velocity; pointerup unpins and releases. Bump alpha on grab so the graph re-settles around it.
Hover: highlight the node and its edges, dim every non-neighbor, and fill the detail card with the node's type, id, desc, and its relations — outgoing as label → target, incoming as source label →. Clear it on leave, but not mid-drag.

Design. Dark, minimal, no framework: CSS variables for the palette and the four type colors, a system font stack, full-viewport canvas, sticky translucent header, the detail card as a fixed rounded panel, a subtle glow on the hovered node, and dimming via opacity.

Step 4 — verify

One bash call. All four must hold:

python3 -m json.tool <OUT>/graph.json > /dev/null && echo "json ok"
grep -c 'src="http' <OUT>/kg.html                        # 0 — nothing loaded from the network
grep -Ec '\{\{|<DATA>|graph here|\.\.\.' <OUT>/kg.html   # 0 — no leftover placeholder
grep -c 'const GRAPH' <OUT>/kg.html                      # 1 — the data is inlined

Then confirm the page carries the whole graph: count "id": occurrences in kg.html and in graph.json and check they match. Any mismatch means the data was truncated — rewrite the file in full, do not patch it.

Step 5 — report

Node count by type, edge count, the three most-connected entities, one cross-article relation that surprised you, and the command to open it:

open <OUT>/kg.html

查看来源 ↗

文件元数据

name: demo-6-article-kg
description: Demo skill that web-fetches two Decoding AI knowledge-graph articles, has the agent itself distill them into a typed entity/relation graph, and renders an interactive dark-themed force-directed KG into one self-contained kg.html — no graph library, no CDN.

查看原始文本

---
name: demo-6-article-kg
description: Demo skill that web-fetches two Decoding AI knowledge-graph articles, has the agent itself distill them into a typed entity/relation graph, and renders an interactive dark-themed force-directed KG into one self-contained kg.html — no graph library, no CDN.
---

The hardcore one: turn two live articles into a knowledge graph you can play with in the browser.
You do all of it — you are the extractor (no NLP library) and you are the renderer (no graph
library, no CDN, no framework). Two artifacts, one page that works the instant it lands.

Work the steps in order. Do not skip ahead to the HTML.

## Step 0 — paths and plan

Both artifacts go in the output directory named in the **Output default** line at the very end of
these instructions — normally `.decode/outputs/`, but a destination the human named always wins.
Resolve it once, call it `<OUT>`, and use `<OUT>` everywhere below:

- `<OUT>/graph.json` — the extraction
- `<OUT>/kg.html` — the page

Put the pipeline in `todo_write` as five items (fetch, extract, render, verify, report) and write
the real `<OUT>` path into the first one so you never re-derive it.

## Step 1 — fetch the two sources

One `web_fetch` per URL (the tool returns Markdown):

- https://www.decodingai.com/p/understanding-neo4j-graph-agent-memory-system
- https://www.decodingai.com/p/ship-a-knowledge-graph-ontology-in-5-minutes

Keep the title and body prose. Ignore navigation, subscribe buttons, CTAs, comments, footers and
trailing "read more" lists. If a fetch fails or comes back truncated, say so plainly and work with
what you have — never invent content you did not fetch.

## Step 2 — extract one merged graph

Distill ONE graph across both articles: an entity discussed in both articles is ONE node, not
two. Write it to `<OUT>/graph.json`:

```json
{
  "nodes": [
    {
      "id": "Ontology",
      "type": "concept",
      "desc": "one sentence in your own words"
    },
    { "id": "Neo4j", "type": "tool", "desc": "one sentence in your own words" }
  ],
  "edges": [{ "source": "Ontology", "target": "KG drift", "label": "prevents" }]
}
```

- **20–35 nodes.** The key ideas, not every noun.
- `type` is exactly one of `concept` / `tool` / `pattern` / `problem`.
- `desc` is one sentence of your own synthesis, not a quote.
- Edges are directed; `label` is a short verb phrase (`prevents`, `stores`, `queries`).
- Every `source` and `target` matches a node `id` exactly — ids are case-sensitive.
- No orphans: every node has at least one edge.

Before moving on, re-read what you wrote and confirm the node count is in range, every edge endpoint
exists, and no node is orphaned. Fixing it here is cheap; fixing it after the page is written is not.

## Step 3 — write the page

**One `write` call produces a finished file.** There is no second pass. The data goes in as a
literal — the actual `{ "nodes": [...], "edges": [...] }` you just wrote to `graph.json`, copied in
full. Never emit `{{GRAPH}}`, `<DATA>`, `/* graph here */`, `...`, or any token you intend to
substitute later, and never write an unexpanded template string. If you catch yourself planning to
"fill in the data next", stop and write the whole file instead.

Author `<OUT>/kg.html` in this order — each section complete before the next:

1. `<head>`: `<title>`, and one `<style>` block. No external stylesheet, script, or font.
2. `<header>`: the page title, the two article URLs as links, and the type legend (a colored dot
   plus the type name, four of them).
3. An empty `<svg>` filling the viewport, and an empty detail card `<div>` positioned on the right.
4. One `<script>` block, in this order: the data, the model, the drawing, the simulation, the
   interactions, the start.

### What goes in the script, in order

**Data.** `const GRAPH = ` followed by the literal object. This is the first statement in the block.

**Model.** Map each node to an object carrying `x`, `y`, `vx`, `vy` and `degree`. Seed positions on
a circle around the viewport center — never all at one point, or the repulsion divides by zero.
Resolve every edge's `source`/`target` string to its node object once, up front, so the loop never
searches by id. Count degree while you do it. Build a neighbor set per node id for the hover step.

**Drawing.** Create the SVG elements once, before the loop starts: a `<line>` and a small `<text>`
per edge, and a `<g>` per node holding a `<circle>` and a `<text>` label. Keep references to them.
The loop only updates coordinates and classes — it never creates or destroys elements. Order the
groups edges → edge labels → nodes so nodes sit on top. Radius is `6 + min(9, degree * 1.4)`; fill
is the node's type color.

**Simulation.** A hand-rolled force simulation in vanilla JS — no library, no framework. One
`tick()` applying three forces, then integrating:

- _Repulsion_, every node pair: magnitude `5000 / distanceSquared`, pushing apart along the line
  between them. Clamp `distanceSquared` to a minimum of 1 and jitter coincident nodes.
- _Springs_, along each edge: pull proportional to `(distance - 110) * 0.01`.
- _Centering_: nudge each node toward the viewport center by `0.0015` of its offset.
- Integrate: multiply velocity by `0.86` damping, add to position, clamp inside the viewport with a
  margin so nothing hides under the header.

Scale all forces by an `alpha` that starts at 1 and decays ~0.5% per tick toward a floor of about
0.06 — that floor is what keeps the layout gently alive instead of frozen. Run ~250 ticks before the
first paint so the page opens settled, then `requestAnimationFrame` a loop of tick + draw.

**Interactions.**

- _Drag_: `pointerdown` on a node pins it and captures the pointer; `pointermove` sets its position
  and zeroes its velocity; `pointerup` unpins and releases. Bump `alpha` on grab so the graph
  re-settles around it.
- _Hover_: highlight the node and its edges, dim every non-neighbor, and fill the detail card with
  the node's type, id, `desc`, and its relations — outgoing as `label → target`, incoming as
  `source label →`. Clear it on leave, but not mid-drag.

**Design.** Dark, minimal, no framework: CSS variables for the palette and the four type colors, a
system font stack, full-viewport canvas, sticky translucent header, the detail card as a fixed
rounded panel, a subtle glow on the hovered node, and dimming via opacity.

## Step 4 — verify

One `bash` call. All four must hold:

```bash
python3 -m json.tool <OUT>/graph.json > /dev/null && echo "json ok"
grep -c 'src="http' <OUT>/kg.html                        # 0 — nothing loaded from the network
grep -Ec '\{\{|<DATA>|graph here|\.\.\.' <OUT>/kg.html   # 0 — no leftover placeholder
grep -c 'const GRAPH' <OUT>/kg.html                      # 1 — the data is inlined
```

Then confirm the page carries the whole graph: count `"id":` occurrences in `kg.html` and in
`graph.json` and check they match. Any mismatch means the data was truncated — rewrite the file in
full, do not patch it.

## Step 5 — report

Node count by type, edge count, the three most-connected entities, one cross-article relation that
surprised you, and the command to open it:

```bash
open <OUT>/kg.html
```

查看并核实来源

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许可证: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: decodingai-magazine/building-a-coding-agent-from-scratch-course
许可证: Apache-2.0
版本: 1.0.0
最近 GitHub 推送: 2026年9月2日
目录更新于: 2026年9月3日
技能指令路径: .decode/skills/demo-6-article-kg/SKILL.md @ 6ee643fbfeb1

版本来自目录元数据，使用前请核实来源发布记录。

质量

69/100

有潜力

信任

63/100

仅限沙盒

审计

76/100

需审查

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Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
结果: —

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      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "coding-agents",
      "agent-skill"
    ],
    "known_risks": [
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 76,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 69,
    "label": "Promising"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Coding agents",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
  ],
  "agent_contract": {
    "task_input": "Use demo-6-article-kg in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 71/100 Manual review",
      "Audit: 76/100 Needs review",
      "Safety: 32/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "decodingai-magazine-demo-6-article-kg (demo-6-article-kg)",
      "install_command": "npx skills add decodingai-magazine/building-a-coding-agent-from-scratch-course --skill demo-6-article-kg",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "decodingai-magazine-demo-6-article-kg",
      "task": "Use demo-6-article-kg in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/decodingai-magazine-demo-6-article-kg",
    "api": "https://www.openagentskill.com/api/agent/skills/decodingai-magazine-demo-6-article-kg",
    "audit": "https://www.openagentskill.com/skills/decodingai-magazine-demo-6-article-kg/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=decodingai-magazine-demo-6-article-kg&task=Use%20demo-6-article-kg%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20demo-6-article-kg%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20demo-6-article-kg%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/decodingai-magazine-demo-6-article-kg/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/decodingai-magazine-demo-6-article-kg"
  }
}

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创作者: decodingai-magazine
来源: decodingai-magazine/building-a-coding-agent-from-scratch-course
收录方: OpenAgentSkill 社区索引

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