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.
Ringkasan
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.
Baca dokumentasi lengkap
Dokumentasi sumber, bukan instruksi untuk situs ini. Periksa izin sebelum menjalankan perintah.
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
Metadata berkas
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.
Lihat teks asli
---
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
```
Tinjau sumber
Harga dan biaya penggunaan
- Dapatkan skill
- Harga belum dikonfirmasi
- Jalankan
- Persyaratan belum dikonfirmasi. Periksa biaya agen, API, dan layanan di sumbernya.
- Lisensi
- Apache-2.0
- Harga belum dikonfirmasi
- Harga belum dikonfirmasi. Tautan sumber dan instalasi yang ada tetap tersedia.
Gratis diperoleh bukan berarti gratis dijalankan. Harga bukan penilaian keamanan. Kirim informasi harga →
Sumber skill tercatat
Jalur instruksi telah dicatat. Ini bukan uji eksekusi, jaminan keamanan, atau sertifikasi kompatibilitas.
Tinjau sebelum memasang: Hindari pemasangan otomatis
Lisensi: 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
Daftar alat adalah petunjuk metadata, bukan kompatibilitas teruji. Prompt adalah saran.
Mulai dengan tugas kecil
- 1Baca sumber dan pastikan masukan, keluaran, dependensi, serta izin.
- 2Minta rencana dari agent. Setujui pengaturan dan biaya sebelum uji terisolasi.
- 3Periksa hasil dan berkas yang berubah. Laporkan hanya yang dijalankan dan simpan revisi sumber.
Periksa dependensi, kunci API, dan biaya layanan pihak ketiga pada sumber. Repositori publik tidak berarti semua layanan gratis.
Sumber dan catatan penggunaan
Terindeks
Metadata dan tinjauan bersifat saran. Popularitas, penemuan sumber, dan keberhasilan eksekusi adalah fakta berbeda.
- Repositori sumber
- decodingai-magazine/building-a-coding-agent-from-scratch-course
- Lisensi
- Apache-2.0
- Versi
- 1.0.0
- Push GitHub terakhir
- 2 Sep 2026
- Direktori diperbarui
- 3 Sep 2026
- Jalur instruksi
- .decode/skills/demo-6-article-kg/SKILL.md @ 6ee643fbfeb1
Versi dilaporkan dalam metadata direktori; periksa rilis sumber.
Kualitas
69/100
Menjanjikan
Kepercayaan
63/100
Hanya sandbox
Audit
76/100
Perlu ditinjau
- 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
- —
- Hasil
- —
Menyalin bukan memasang. Jumlah instalasi memerlukan laporan berhasil dan bukan jaminan kualitas menyeluruh.
Akses agent
API Registry menyediakan sinyal keputusan, kepercayaan, audit, use case, dan pemasangan tanpa mengikis UI.
Detail lainnya
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"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"
}
}Untuk kreator
Sumber listing
Diindeks Registry
Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.
- Kreator
- decodingai-magazine
- Diindeks oleh
- Indeks komunitas OpenAgentSkill
Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.
Klaim skill iniKlaim pemilik
Klaim listing skill ini
Listing Diindeks Registry ini dikaitkan dengan decodingai-magazine, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.
Kit berbagi
Kit backlink kreator
Tambahkan badge bukti ke README Anda
Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.
[](https://www.openagentskill.com/skills/decodingai-magazine-demo-6-article-kg?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/decodingai-magazine-demo-6-article-kg?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/decodingai-magazine-demo-6-article-kg/audit)
[](https://www.openagentskill.com/skills/decodingai-magazine-demo-6-article-kg?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)Sinyal komunitas
Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.