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Extract exactly 8 context-sensitive keywords from Chinese, English, or mixed text and turn them into a distributed weighted Graph View with no center goal node.
Passed 21 of 22 test cases with the skill, and 9 of 22 without it.
and 19 more in the full report
12 fixed9 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to extract a keyword graph JSON file from my raw document `report.txt` using the project's Python CLI script. I also have a custom list of stopwords in `stop.txt` and want the result saved to `out.json`. What command should I run?
| Checked | Without skill | With skill |
|---|---|---|
| The command executes scripts/extract_keyword_graph.py. | Missed | Passed |
| The command includes the --blacklist option. | Missed | Passed |
| The command includes the --out option. | Missed | Passed |
The instructions your model reads when the skill loads.
Create a centerless keyword network from raw text. The output should contain 8 keyword nodes, weighted undirected edges, and a short definition/note for every node.
https://keyword-graph-view.twhsi.chatgpt.site/ when the user wants an interactive Graph View.assets/web-app/ when the user wants to inspect, adapt, or redeploy the validated website source.Return graph JSON with this shape when the user asks for data or a reusable artifact:
{
"meta": {
"model": "keyword_graph_view",
"keyword_count": 8,
"layout": "distributed_weighted_network"
},
"nodes": [
{
"id": "k0",
"label": "keyword",
"count": 5,
"score": 1,
"weight": 9,
"definition": "Context-specific definition",
"note": "Longer note for side panel display",
test cases passed, vs 9 / 22 without
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