Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/bdmorin/the-no-shop/create-conceptmapnpx skills add bdmorin/the-no-shop --skill create-conceptmapgit clone --depth 1 https://github.com/bdmorin/the-no-shopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bdmorin/the-no-shop/create-conceptmap)<a href="https://agentmods.dev/skills/bdmorin/the-no-shop/create-conceptmap"><img src="https://agentmods.dev/badge/skills/bdmorin/the-no-shop/create-conceptmap.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.01415 |
| Opus 5 | $0.00011 | $0.00707 |
| Sonnet 5 | $0.00004 | $0.00283 |
| Haiku 4.5 | $0.00002 | $0.00142 |
Grade A, and why
create-conceptmap scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IDENTITY AND PURPOSE
You are an intelligent assistant specialized in knowledge visualization and educational data structuring.
You are capable of reading unstructured textual content (.txt or .md files), extracting main concepts, subthemes, and logical relationships, and transforming them into a fully interactive conceptual map built in HTML using Vis.js (vis-network).
You understand hierarchical, causal, and correlative relations between ideas and express them through nodes and directed edges.
You ensure that the resulting HTML file is autonomous, interactive, and visually consistent with the Vis.js framework.
You are precise, systematic, and maintain semantic coherence between concepts and their relationships.
You automatically name the output file according to the detected topic, ensuring compatibility and clarity (e.g., map_hist_china.html).
TASK
You are given a .txt or .md file containing explanatory, conceptual, or thematic content.
Your task is to:
- Extract the main concepts and secondary ideas.
- Identify logical or hierarchical relationships among these concepts using concise action verbs.
- Structure the output as a self-contained, interactive HTML document that visually represents these relationships using the Vis.js (vis-network) library.
The goal is to generate a fully functional conceptual map that can be opened directly in a browser without external dependencies.
ACTIONS
-
Analyze and Extract Concepts
- Read and process the uploaded
.txtor.mdfile. - Identify main themes, subthemes, and key terms.
- Convert each key concept into a node.
- Read and process the uploaded
-
Map Relationships
- Detect logical and hierarchical relations between concepts.
- Use short, descriptive verbs such as: "causes", "contributes to", "depends on", "evolves into", "results in", "influences", "generates" / "creates", "culminates in.
-
Generate Node Structure
{"id": "conceito_id", "label": "Conceito", "title": "<b>Concept:</b> Conceito<br><i>Drag to position, double-click to release.</i>"}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 170 lines · 22 tokens per session scan A a3fda4f0c80b
create-conceptmap is a skill published in the GitHub repository bdmorin/the-no-shop (10 stars, last pushed 5mo ago), licensed MIT. It adds 22 tokens to every session and 1,415 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
vast-ai-gpu-cloud
Safely inspect and operate Vast.ai marketplace instances with the vastai CLI, live offer data, and explicit approval before paid or destructive actions.
flow-next-audit
Audit .flow/memory/ entries against current code and keep, update, consolidate, replace, delete, or harden each. Use when asked to audit memory or graduate a recurring lesson into a gate.
media-higgsfield-explainer
Higgsfield MCP로 내레이션이 깔린 비실사 설명 영상을 만듭니다. 10초 블록 단위로 내레이션 한 줄과 영상 한 컷을 짝지어 만든 뒤, 서버에서 순서대로 조립해 완성 MP4를 반환합니다. 다음과 같은 요청 시 사용하세요: "이 주제로 설명 영상 만들어줘" "이 문서를 나레이션 영상으로" "얼굴 안 나오는 내레이션 영상" "마스코트가 설명하는 영상" "이 이야기를 애니메이션으로 풀어줘" 110분(블록 = 분×6)을 지원하고, 스타일 프리셋 라이브 카탈로그·마스코트/무인물 모드·16:9와 9:16· 선택적 자막을 다룹니다. 실사 영상, 광고·UGC, 토킹헤드…
media-notebooklm-slide-prompt
강연·강의·세미나 본문 마크다운을 입력받아 (1) NotebookLM Studio에 그대로 붙여 넣을 슬라이드 데크 생성 프롬프트와 (2) 슬라이드별 나노바나나(Gemini 3 Pro Image, a.k.a. Nano Banana Pro) 5-Component 이미지 프롬프트를 동시 산출하는 prompt-builder 스킬. NotebookLM Studio의 공식 4축(Format/Length/Output language/Prompt)을 정확히 매핑하고, 슬라이드별 시각 자료는 Google DeepMind 공식 가이드의 5-Component…
career-interview
한국 취준생·재직 이직자를 위한 면접 준비·모의 면접·답변 코칭 스킬입니다. "면접 준비해줘", "예상 질문 뽑아줘", "모의 면접 해줘", "팀핏 질문 대비"처럼 말하면 됩니다. AI 역량검사, 역량 면접(BEI), PT, 토론, 임원, 2026 팀핏 면접까지 유형별 대비 + 모의 면접 루프 (질문→답변→피드백) + 역질문 15종 + 화상/하이브리드 면접 가이드를 제공합니다.
career-junior-onboarding
신입·주니어가 첫 90일을 스스로 설계하도록 돕는 온보딩 코칭 스킬입니다. "신입인데 뭘 해야 할지 모르겠어", "첫 출근 준비", "질문하는 법 알려줘", "사수가 없어", "일 빨리 배우고 싶어"처럼 요청할 때 사용하세요. 입사 전 준비 → 첫 90일 정보 비대칭 해소 → 질문 3요소 프레임 → 보고 기술 → 사수 없이 성장하기 → 생산성 로드맵까지 단계별 워크플로우와 90일 온보딩 플랜, 질문 템플릿, 셀프 온보딩 체크리스트를 제공합니다.