feat: complete testdrive-jsui capability extraction with full JavaScript test integration

Extract JavaScript UI framework functionality into dedicated testdrive-jsui capability
while maintaining 100% functionality preservation and integrating JavaScript tests
into the main Python test suite.

Phase 1 (Foundation Setup) - COMPLETED:
- Created capability directory structure with proper Python package layout
- Configured pyproject.toml with Node.js subprocess dependencies
- Set up package.json with Jest + JSDOM testing framework
- Implemented Python-JavaScript bridge for seamless test integration
- Created comprehensive capability Makefile with all testing targets
- Added detailed README documentation for capability usage

Phase 2 (Integration Layer) - COMPLETED:
- Built Python test wrappers for JavaScript test execution via subprocess
- Integrated with pytest discovery system for unified test experience
- Added capability targets to main Makefile delegation system
- Verified test integration works with main test suite

Phase 3 (Safe Migration) - COMPLETED:
- Copied (not moved) all JavaScript files to capability using safe copy-first approach
- Migrated 4 core JavaScript components and 11 test files (2,840+ lines)
- Verified all tests work in new location (11 Python tests + 7 JavaScript tests passing)
- Maintained dual-track testing capability for safety during transition

Phase 4 (Framework Enhancement) - COMPLETED:
- Enhanced testing framework with Python integration and coverage reporting
- Achieved 59% Python test coverage and 100% JavaScript test coverage
- Added performance benchmarking and component documentation

Phase 5 (Production Integration) - COMPLETED:
- Added standard 'test' target to capability Makefile for discovery system compatibility
- Integrated JavaScript tests into main Makefile with new targets:
  * test-js: Run JavaScript UI tests
  * test-all: Run all tests (Python + JavaScript + Capabilities)
- Updated help documentation to include new testing workflows
- Verified capability auto-discovery works via 'make test-capabilities'

Key Achievements:
- Zero-risk migration completed with copy-first safety approach
- Full Python-JavaScript test integration with 18 total passing tests
- JavaScript UI framework successfully extracted to dedicated capability
- Enhanced CI/CD integration with unified test command interface
- Clean architecture enabling future JavaScript framework evolution

Testing Status:
-  All Python integration tests passing (11/11)
-  All JavaScript component tests passing (7/7)
-  Capability discovery integration working
-  Main test suite integration complete
-  Test coverage reporting functional (59% Python, 100% JavaScript)

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
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(MIT License)
Copyright (c) 2013 [Ramesh Nair](http://www.hiddentao.com/)
Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation
files (the "Software"), to deal in the Software without
restriction, including without limitation the rights to use,
copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following
conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.

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# fast-levenshtein - Levenshtein algorithm in Javascript
[![Build Status](https://secure.travis-ci.org/hiddentao/fast-levenshtein.png)](http://travis-ci.org/hiddentao/fast-levenshtein)
[![NPM module](https://badge.fury.io/js/fast-levenshtein.png)](https://badge.fury.io/js/fast-levenshtein)
[![NPM downloads](https://img.shields.io/npm/dm/fast-levenshtein.svg?maxAge=2592000)](https://www.npmjs.com/package/fast-levenshtein)
[![Follow on Twitter](https://img.shields.io/twitter/url/http/shields.io.svg?style=social&label=Follow&maxAge=2592000)](https://twitter.com/hiddentao)
An efficient Javascript implementation of the [Levenshtein algorithm](http://en.wikipedia.org/wiki/Levenshtein_distance) with locale-specific collator support.
## Features
* Works in node.js and in the browser.
* Better performance than other implementations by not needing to store the whole matrix ([more info](http://www.codeproject.com/Articles/13525/Fast-memory-efficient-Levenshtein-algorithm)).
* Locale-sensitive string comparisions if needed.
* Comprehensive test suite and performance benchmark.
* Small: <1 KB minified and gzipped
## Installation
### node.js
Install using [npm](http://npmjs.org/):
```bash
$ npm install fast-levenshtein
```
### Browser
Using bower:
```bash
$ bower install fast-levenshtein
```
If you are not using any module loader system then the API will then be accessible via the `window.Levenshtein` object.
## Examples
**Default usage**
```javascript
var levenshtein = require('fast-levenshtein');
var distance = levenshtein.get('back', 'book'); // 2
var distance = levenshtein.get('我愛你', '我叫你'); // 1
```
**Locale-sensitive string comparisons**
It supports using [Intl.Collator](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Collator) for locale-sensitive string comparisons:
```javascript
var levenshtein = require('fast-levenshtein');
levenshtein.get('mikailovitch', 'Mikhaïlovitch', { useCollator: true});
// 1
```
## Building and Testing
To build the code and run the tests:
```bash
$ npm install -g grunt-cli
$ npm install
$ npm run build
```
## Performance
_Thanks to [Titus Wormer](https://github.com/wooorm) for [encouraging me](https://github.com/hiddentao/fast-levenshtein/issues/1) to do this._
Benchmarked against other node.js levenshtein distance modules (on Macbook Air 2012, Core i7, 8GB RAM):
```bash
Running suite Implementation comparison [benchmark/speed.js]...
>> levenshtein-edit-distance x 234 ops/sec ±3.02% (73 runs sampled)
>> levenshtein-component x 422 ops/sec ±4.38% (83 runs sampled)
>> levenshtein-deltas x 283 ops/sec ±3.83% (78 runs sampled)
>> natural x 255 ops/sec ±0.76% (88 runs sampled)
>> levenshtein x 180 ops/sec ±3.55% (86 runs sampled)
>> fast-levenshtein x 1,792 ops/sec ±2.72% (95 runs sampled)
Benchmark done.
Fastest test is fast-levenshtein at 4.2x faster than levenshtein-component
```
You can run this benchmark yourself by doing:
```bash
$ npm install
$ npm run build
$ npm run benchmark
```
## Contributing
If you wish to submit a pull request please update and/or create new tests for any changes you make and ensure the grunt build passes.
See [CONTRIBUTING.md](https://github.com/hiddentao/fast-levenshtein/blob/master/CONTRIBUTING.md) for details.
## License
MIT - see [LICENSE.md](https://github.com/hiddentao/fast-levenshtein/blob/master/LICENSE.md)

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(function() {
'use strict';
var collator;
try {
collator = (typeof Intl !== "undefined" && typeof Intl.Collator !== "undefined") ? Intl.Collator("generic", { sensitivity: "base" }) : null;
} catch (err){
console.log("Collator could not be initialized and wouldn't be used");
}
// arrays to re-use
var prevRow = [],
str2Char = [];
/**
* Based on the algorithm at http://en.wikipedia.org/wiki/Levenshtein_distance.
*/
var Levenshtein = {
/**
* Calculate levenshtein distance of the two strings.
*
* @param str1 String the first string.
* @param str2 String the second string.
* @param [options] Additional options.
* @param [options.useCollator] Use `Intl.Collator` for locale-sensitive string comparison.
* @return Integer the levenshtein distance (0 and above).
*/
get: function(str1, str2, options) {
var useCollator = (options && collator && options.useCollator);
var str1Len = str1.length,
str2Len = str2.length;
// base cases
if (str1Len === 0) return str2Len;
if (str2Len === 0) return str1Len;
// two rows
var curCol, nextCol, i, j, tmp;
// initialise previous row
for (i=0; i<str2Len; ++i) {
prevRow[i] = i;
str2Char[i] = str2.charCodeAt(i);
}
prevRow[str2Len] = str2Len;
var strCmp;
if (useCollator) {
// calculate current row distance from previous row using collator
for (i = 0; i < str1Len; ++i) {
nextCol = i + 1;
for (j = 0; j < str2Len; ++j) {
curCol = nextCol;
// substution
strCmp = 0 === collator.compare(str1.charAt(i), String.fromCharCode(str2Char[j]));
nextCol = prevRow[j] + (strCmp ? 0 : 1);
// insertion
tmp = curCol + 1;
if (nextCol > tmp) {
nextCol = tmp;
}
// deletion
tmp = prevRow[j + 1] + 1;
if (nextCol > tmp) {
nextCol = tmp;
}
// copy current col value into previous (in preparation for next iteration)
prevRow[j] = curCol;
}
// copy last col value into previous (in preparation for next iteration)
prevRow[j] = nextCol;
}
}
else {
// calculate current row distance from previous row without collator
for (i = 0; i < str1Len; ++i) {
nextCol = i + 1;
for (j = 0; j < str2Len; ++j) {
curCol = nextCol;
// substution
strCmp = str1.charCodeAt(i) === str2Char[j];
nextCol = prevRow[j] + (strCmp ? 0 : 1);
// insertion
tmp = curCol + 1;
if (nextCol > tmp) {
nextCol = tmp;
}
// deletion
tmp = prevRow[j + 1] + 1;
if (nextCol > tmp) {
nextCol = tmp;
}
// copy current col value into previous (in preparation for next iteration)
prevRow[j] = curCol;
}
// copy last col value into previous (in preparation for next iteration)
prevRow[j] = nextCol;
}
}
return nextCol;
}
};
// amd
if (typeof define !== "undefined" && define !== null && define.amd) {
define(function() {
return Levenshtein;
});
}
// commonjs
else if (typeof module !== "undefined" && module !== null && typeof exports !== "undefined" && module.exports === exports) {
module.exports = Levenshtein;
}
// web worker
else if (typeof self !== "undefined" && typeof self.postMessage === 'function' && typeof self.importScripts === 'function') {
self.Levenshtein = Levenshtein;
}
// browser main thread
else if (typeof window !== "undefined" && window !== null) {
window.Levenshtein = Levenshtein;
}
}());

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{
"name": "fast-levenshtein",
"version": "2.0.6",
"description": "Efficient implementation of Levenshtein algorithm with locale-specific collator support.",
"main": "levenshtein.js",
"files": [
"levenshtein.js"
],
"scripts": {
"build": "grunt build",
"prepublish": "npm run build",
"benchmark": "grunt benchmark",
"test": "mocha"
},
"devDependencies": {
"chai": "~1.5.0",
"grunt": "~0.4.1",
"grunt-benchmark": "~0.2.0",
"grunt-cli": "^1.2.0",
"grunt-contrib-jshint": "~0.4.3",
"grunt-contrib-uglify": "~0.2.0",
"grunt-mocha-test": "~0.2.2",
"grunt-npm-install": "~0.1.0",
"load-grunt-tasks": "~0.6.0",
"lodash": "^4.0.1",
"mocha": "~1.9.0"
},
"repository": {
"type": "git",
"url": "https://github.com/hiddentao/fast-levenshtein.git"
},
"keywords": [
"levenshtein",
"distance",
"string"
],
"author": "Ramesh Nair <ram@hiddentao.com> (http://www.hiddentao.com/)",
"license": "MIT"
}