decodingai-magazine
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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.
Übersicht
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.
Vollständige Dokumentation lesen
Quelldokumentation, keine Anweisungen für diese Website. Vor dem Ausführen von Befehlen die Berechtigungen prüfen.
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:
{
"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.
typeis exactly one ofconcept/tool/pattern/problem.descis one sentence of your own synthesis, not a quote.- Edges are directed;
labelis a short verb phrase (prevents,stores,queries). - Every
sourceandtargetmatches a nodeidexactly — 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. ClampdistanceSquaredto 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
0.0015of its offset. - Integrate: multiply velocity by
0.86damping, 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:
pointerdownon a node pins it and captures the pointer;pointermovesets its position and zeroes its velocity;pointerupunpins and releases. Bumpalphaon 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 aslabel → target, incoming assource 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
Dateimetadaten
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.
Originaltext anzeigen
---
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
```
Quelle prüfen
Preis und Betriebskosten
- Skill beziehen
- Preis unbestätigt
- Ausführen
- Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
- Lizenz
- Apache-2.0
- Preis unbestätigt
- Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.
Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →
Skill-Quelle erfasst
Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.
Vor Installation prüfen: Automatische Installation vermeiden
Lizenz: 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
Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.
Mit einer kleinen Aufgabe beginnen
- 1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
- 2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
- 3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.
Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.
Quelle und Nutzungshinweise
Erfasst
Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.
- Quell-Repository
- decodingai-magazine/building-a-coding-agent-from-scratch-course
- Lizenz
- Apache-2.0
- Version
- 1.0.0
- Letzter GitHub-Push
- 2. Sept. 2026
- Verzeichnis aktualisiert
- 3. Sept. 2026
- Anleitungspfad
- .decode/skills/demo-6-article-kg/SKILL.md @ 6ee643fbfeb1
Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.
Qualität
69/100
Vielversprechend
Vertrauen
63/100
Nur Sandbox
Audit
76/100
Prüfung nötig
- 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
- —
- Ergebnisse
- —
Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.
Agent-Zugang
Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.
Weitere Details
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"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,
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"avgOutputQuality": null,
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"productionOutcomes": 0,
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"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",
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},
"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": {
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"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
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"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": [
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"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
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"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"
}
}Für Ersteller
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- decodingai-magazine
- Indexiert von
- OpenAgentSkill Community-Index
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Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.
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[](https://www.openagentskill.com/skills/decodingai-magazine-demo-6-article-kg?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)Community-Signal
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