Building Lightweight Notepad AI Plugins: Add Tables, Summaries and Smart Edits

Build a lightweight Notepad AI plugin: tables, summaries, smart edits (step-by-step)

Hook: If your team struggles to ship small, reliable AI features into text editors—because prompts live in Slack, there’s no versioning, and production integration is messy—this guide shows how to build a low-dependency Notepad-style plugin in 2026 that adds tables, summarization and smart edits with clear governance and minimal runtime weight.

The problem in 2026 and why now

By late 2025 and into 2026, teams stopped asking whether editor integrations were possible and started asking how to make them reliable, auditable and lightweight. Advances in quantized models, local WASM runtimes, and structured AI endpoints mean you can build offline-capable or hybrid editor plugins with small bundles and clear prompts. But without a standard approach you’ll end up with brittle prompt logic, duplicated templates and UX that frustrates users.

This article walks you through a practical, production-minded approach: a minimal plugin architecture, code examples using plain JavaScript, prompt templates, a small UI overlay for tables, streaming-friendly summarization, and robust smart edit patterns that support undo and audit logs.

What you’ll ship (scope)

• Insert and edit simple tables (CSV ↔ Markdown ↔ rendered grid) inside a Notepad-like editor.
• Context-aware summarization for selections or entire documents.
• Smart edit actions (rewrite, simplify, expand, explain) that apply safe patches with undo.
• A low-dependency JS plugin that works with a standard plugin API (manifest, command registration, editor selection) and can call either a remote LLM endpoint or a local WASM fallback.

2026 trends to leverage

• Stable small instruction-tuned models and WASM runtimes for low-latency inference on-device.
• Function-calling/structured-output endpoints (late 2024–2026) that simplify reliable edits and table generation.
• Standardized plugin APIs (manifest + commands + overlays) across lightweight editors—ideal for single-file plugins.
• Vector stores and cheap embedding services enabling fast context retrieval for summarization and RAG-based edits.

Design constraints and principles

• Minimal runtime: No heavy bundlers or frameworks; ESM + small polyfills only.
• Pluggable inference: Support remote API calls but keep a small local fallback (WASM) optional.
• Prompt governance: Store templates with version metadata, tests and changelogs.
• Safe UX: Always produce reversible edits and provide undo + a preview diff.

Step 1 — Plan the UX flows

Sketch three simple flows:

1. Insert Table: User places caret or selects CSV → command “Insert Table” → overlay shows a small grid preview → user confirms → table inserts as Markdown.
2. Summarize Selection/Doc: User selects text or invokes “Summarize Document” → plugin streams progress → summary inserted in a block or copied to clipboard.
3. Smart Edit: User selects text → chooses transform (Simplify/Explain/Refactor) → plugin shows preview and confidence/trace → apply to replace selection.

Step 2 — Minimal plugin manifest

Most lightweight editors accept a small manifest describing commands and UI entry points. Use a JSON manifest like this (adapt to your editor):

// plugin-manifest.json
  {
    "name": "notepad-ai-tools",
    "version": "0.1.0",
    "commands": [
      {"id": "insertTable", "title": "Insert Table"},
      {"id": "summarize", "title": "Summarize"},
      {"id": "smartEdit", "title": "Smart Edit"}
    ],
    "permissions": ["activeDocument", "clipboard"]
  }
  

Step 3 — Core runtime (vanilla JS)

Keep a single small entry file. This example uses a hypothetical editor API with registerCommand and getSelection/replaceSelection primitives. No frameworks.

// main.js (ESM)
  import { promptStore } from './prompts.js'; // tiny JSON prompts

  const API_URL = 'https://api.example.ai/v1/generate';

  function register() {
    editor.registerCommand('insertTable', onInsertTable);
    editor.registerCommand('summarize', onSummarize);
    editor.registerCommand('smartEdit', onSmartEdit);
  }

  async function onInsertTable(ctx) {
    const selection = editor.getSelection() || '';
    const csv = selection.trim();
    const preview = parseCsvToMarkdown(csv) || '| Column |
|---|
| |';
    const OK = await showPreviewOverlay(preview, 'Insert table');
    if (OK) editor.replaceSelection(preview);
  }

  async function onSummarize(ctx) {
    const text = editor.getSelection() || editor.getDocumentText();
    const prompt = promptStore.get('summarize.v1');
    const result = await callAiInfer({ prompt: renderPrompt(prompt, { text }) });
    await showPreviewOverlay(result.output, 'Summary');
  }

  async function onSmartEdit(ctx) {
    const text = editor.getSelection();
    const transform = await showTransformPicker();
    const prompt = promptStore.get('smartedit.v1');
    const response = await callAiInfer({ prompt: renderPrompt(prompt, { text, transform }) });
    const patch = response.output; // structured
    await showDiffAndApply(text, patch);
  }

  register();
  

Notes

• renderPrompt is a tiny templating function that injects variables into chosen prompt templates.
• showPreviewOverlay is a minimal DOM overlay with Confirm/Cancel and supports streaming text.

Step 4 — Summarization: streaming, extractive vs. abstractive

Summarization is a common friction point. Provide two options:

• Extractive: Return key sentences or bullets selected from the original text for fidelity.
• Abstractive: Return a concise rewrite in natural language for readability.

Use a prompt template with clear system instructions. Favor structured output so the plugin can parse and display bullets safely.

// prompts.js (snippet)
  export const promptStore = new Map([
    ['summarize.v1', {
      id: 'summarize.v1',
      description: 'Two-mode summarizer: extractive or abstractive',
      template: `You are a concise summarizer. Mode: {{mode}}. Input:

{{text}}

If mode is extractive, return JSON {"type":"extractive","bullets":[...]}. If abstractive, return {"type":"abstractive","summary":"..."}.`
    }]
  ]);
  

Call the AI with streaming to show progress and reduce perceived latency. Example fetch with SSE/streaming:

// callAiInfer - minimal streaming
  async function callAiInfer({ prompt }) {
    const res = await fetch(API_URL, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json', 'Authorization': `Bearer ${INFER_KEY}` },
      body: JSON.stringify({ model: 'tiny-instruct-2026', prompt, stream: true })
    });

    // Read stream and append to UI progressively
    const reader = res.body.getReader();
    let text = '';
    while (true) {
      const { done, value } = await reader.read();
      if (done) break;
      text += new TextDecoder().decode(value);
      showStreamFragment(text);
    }
    return { output: safeParseStructured(text) };
  }
  

Step 5 — Tables: parse, render, edit

Users expect frictionless table insertion. The minimal feature set:

• Parse CSV or pasted tabular text.
• Render a Markdown table for insertion, and show a lightweight grid preview for editing.
• Offer basic row/column add/remove in the overlay.

// parseCsvToMarkdown (simple)
  function parseCsvToMarkdown(csv) {
    if (!csv) return null;
    const rows = csv.split(/\r?\n/).map(r => r.split(/,|\t/).map(c => c.trim()));
    const header = rows[0] || ['Column1'];
    const sep = header.map(() => '---');
    const lines = [ `| ${header.join(' | ')} |`, `| ${sep.join(' | ')} |` ];
    for (let i = 1; i < rows.length; i++) lines.push(`| ${rows[i].join(' | ')} |`);
    return lines.join('\n');
  }
  

For the grid overlay, use simple DOM table elements and inline CSS (no library). Keep it optional for mobile or small screens.

Step 6 — Smart edits with structured outputs

Smart edits must be reliable. Use structured outputs from the AI so the plugin receives a JSON describing edits rather than freeform text. Example schema:

{
    "ops": [
      {"type": "replace", "range": {"start": 30, "end": 78}, "text": "simplified sentence"},
      {"type": "insert", "position": 120, "text": "// explanation"}
    ],
    "metadata": {"model":"tiny-instruct-2026","prompt_version":"smartedit.v1"}
  }
  

Applying the patch:

async function showDiffAndApply(originalText, patchJson) {
    const preview = renderPatchPreview(originalText, patchJson.ops);
    const OK = await showPreviewOverlay(preview, 'Smart Edit Preview');
    if (!OK) return;
    // apply ops in reverse order to keep indexes safe
    let doc = originalDocumentText();
    for (const op of patchJson.ops.slice().reverse()) {
      if (op.type === 'replace') doc = doc.slice(0, op.range.start) + op.text + doc.slice(op.range.end);
      else if (op.type === 'insert') doc = doc.slice(0, op.position) + op.text + doc.slice(op.position);
    }
    editor.setDocumentText(doc);
    logAudit({ action: 'smartEdit', patch: patchJson });
  }
  

Step 7 — Prompt library, versioning and tests

Store prompts as small JSON files with metadata, examples and a test harness. Example prompt file layout:

// prompts/smartedit.v1.json
  {
    "id": "smartedit.v1",
    "title": "Smart Edit (concise)",
    "model": "tiny-instruct-2026",
    "template": "{{instruction}}\n\nInput:\n{{text}}",
    "examples": [
      {"in":"The cat was sat on the mat.", "out":"The cat sat on the mat."}
    ],
    "version": "2026-01-01",
    "changelog": ["initial release"]
  }
  

Automate prompt regression testing in CI: run examples through a mocked or small in-process model and assert outputs match allowed patterns. Keep a changelog and require a prompt review before updating production prompt versions. If you need guidance on writing robust prompt templates, see AEO-Friendly Content Templates.

Step 8 — Low-dependency packaging and optional local inference

Keep the plugin small:

• Use native ESM and ship a tiny polyfill only for older hosts.
• Avoid heavy UI frameworks—DOM APIs suffice for overlays and previews.
• Optional local inference: provide a WASM adapter that loads a quantized model when the user has resources. This is useful for offline or privacy-sensitive environments (on-device AI).

Feature-detection pattern:

async function chooseInference() {
    if (window.WASM_LLM_AVAILABLE && userPrefersLocal()) {
      return startLocalWasmRunner();
    }
    return { type: 'remote', url: API_URL };
  }
  

For hybrid deployment patterns and choosing when to run locally vs remote, see practical patterns for hybrid edge workflows: Hybrid Edge Workflows for Productivity Tools (2026).

Step 9 — UX: latency, undo, accessibility

Small UX details make or break adoption:

• Show progress: Streaming text or a progress spinner with estimated time.
• Preview + Diff: Always show changes before applying and allow a single-click undo.
• Keyboard first: Commands should be bound to shortcuts and available in command palette.
• Accessibility: Overlay controls must be keyboard and screen-reader accessible.

Step 10 — Testing, telemetry and governance

Implement at least three types of tests:

• Unit: Parser and patch application logic.
• Prompt regression: Run canned inputs and assert structured outputs.
• Integration: Mock the editor API and AI endpoint to validate end-to-end flows.

Telemetry and audit logs (store locally by default and optionally send to your team server):

function logAudit(entry) {
    const record = { ts: Date.now(), user: currentUser(), entry };
    localStorage.audit = JSON.stringify([...(JSON.parse(localStorage.audit || '[]')), record]);
    // Optionally POST to team telemetry endpoint with consent
  }
  

For guidance on storage cost tradeoffs when keeping telemetry and audit logs, see a CTO’s guide to storage costs: A CTO’s Guide to Storage Costs.

Design rule: keep an immutable record of prompt version and output hash with every applied edit.

Security, privacy and cost considerations

• Sanitize user input and avoid sending secrets to third parties.
• Provide an explicit privacy setting: remote-only, local-only, or hybrid. For enterprise and hiring flows, consider security guidance such as Security & Privacy for Career Builders.
• Batch and truncate context to control cost when calling remote APIs.
• Use streaming to reduce time-to-first-byte and improve perceived performance (low-latency streaming patterns).

Future-proofing for 2026+

Expect these practical changes through 2026:

• More capable compact models on-device—plan for an easy local switch (on-device AI).
• Wider support for structured function calls from LLMs—use them to get robust JSON edits instead of fragile string parsing (edge-first/structured endpoints).
• Embedding-first summarization with tiny vector stores for context-aware summaries (automating metadata & embeddings).
• Standardized plugin manifests across editors, making porting trivial.

Actionable checklist (copy to your project)

• Create manifest and register 3 commands (table, summarize, smart edit).
• Implement the core JS runtime (register, selection API handlers).
• Add prompt store JSON files with version and examples.
• Implement streaming callAiInfer with error/retry and cost guardrails.
• Build a tiny overlay for previews and diff + undo.
• Write unit tests for CSV parser and patch applier; add prompt regression tests to CI (see micro-app case studies: Micro Apps Case Studies).
• Provide a local WASM fallback and a feature toggle for privacy-conscious users.

Example: Summarize command (end-to-end)

Putting pieces together—this pseudocode shows the full request flow, preview and audit.

async function onSummarize() {
    const text = editor.getSelection() || editor.getDocumentText();
    const mode = await showModePicker(['extractive','abstractive']);
    const prompt = promptStore.get('summarize.v1').template;
    const inference = await chooseInference();
    const response = await callAiInfer({ inference, prompt: renderPrompt(prompt, { text, mode }) });
    await showPreviewOverlay(response.outputText || JSON.stringify(response.output, null, 2), 'Summary');
    logAudit({ action: 'summarize', promptId: 'summarize.v1', promptVersion: '2026-01-10' });
  }
  

Key takeaways

• Build small: prefer ESM + native DOM over heavy frameworks for editor plugins.
• Use structured AI outputs for reliable table generation and smart edits; always show previews.
• Provide a local inference path for privacy and offline; keep remote as fallback for heavy tasks (hybrid edge workflows).
• Version prompts, run regression tests, and log audits for governance.
• Design UX around latency and reversibility—users must be in control.

Conclusion & call-to-action

In 2026 you can ship powerful editor features like tables, summarization and safe smart edits without a huge dependency footprint. The pattern is clear: small runtime, structured AI outputs, prompt governance and a frictionless preview-first UX. Use the checklist and code samples above to prototype in a day and harden with tests and audit logs for production.

Next step: Clone the starter repo, swap in your preferred inference endpoint (remote or WASM), and run the prompt regression tests in CI. If you want a ready-made prompt library and hosted governance tools, grab the notepad-ai-tools starter kit on promptly.cloud or join our community to get a production-ready template and CI pipeline.

Related Reading

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