407 lines
16 KiB
Python
407 lines
16 KiB
Python
#!/usr/bin/env python3
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# /// script
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# requires-python = ">=3.9"
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# ///
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"""
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Generate an interactive HTML skill conversion comparison report.
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Measures original and rebuilt skill directories, combines with LLM-generated
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analysis (cuts, retained content, verdict), and renders a self-contained
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HTML report showing the stark before/after comparison.
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Usage:
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python3 generate-convert-report.py <original-path> <rebuilt-path> <analysis-json> [-o output.html] [--open]
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"""
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from __future__ import annotations
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import argparse
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import html as html_lib
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import json
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import platform
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import subprocess
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import sys
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from datetime import datetime, timezone
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from pathlib import Path
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def measure_skill(skill_path: Path) -> dict:
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"""Measure a skill directory or single file for lines, words, chars, sections, files."""
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total_lines = 0
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total_words = 0
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total_chars = 0
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total_sections = 0
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md_file_count = 0
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non_md_file_count = 0
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if skill_path.is_file():
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md_files = [skill_path]
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else:
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md_files = sorted(skill_path.rglob('*.md'))
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for f in md_files:
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content = f.read_text(encoding='utf-8')
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lines = content.splitlines()
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total_lines += len(lines)
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total_words += sum(len(line.split()) for line in lines)
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total_chars += len(content)
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total_sections += sum(1 for line in lines if line.startswith('## '))
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md_file_count += 1
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if skill_path.is_dir():
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for f in skill_path.rglob('*'):
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if f.is_file() and f.suffix != '.md':
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non_md_file_count += 1
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return {
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'lines': total_lines,
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'words': total_words,
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'chars': total_chars,
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'sections': total_sections,
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'files': md_file_count + non_md_file_count,
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'estimated_tokens': int(total_words * 1.3),
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}
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def calculate_reductions(original: dict, rebuilt: dict) -> dict:
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"""Calculate percentage reductions for each metric."""
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reductions = {}
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for key in ('lines', 'words', 'chars', 'sections', 'estimated_tokens'):
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orig_val = original.get(key, 0)
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new_val = rebuilt.get(key, 0)
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if orig_val > 0:
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reductions[key] = f'{round((1 - new_val / orig_val) * 100)}%'
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else:
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reductions[key] = 'N/A'
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return reductions
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def build_report_data(original_metrics: dict, rebuilt_metrics: dict,
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analysis: dict, reductions: dict) -> dict:
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"""Assemble the full report data structure."""
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return {
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'meta': {
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'skill_name': analysis.get('skill_name', 'Unknown'),
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'original_source': analysis.get('original_source', ''),
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'timestamp': datetime.now(timezone.utc).isoformat(),
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},
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'metrics': {
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'original': original_metrics,
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'rebuilt': rebuilt_metrics,
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},
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'reductions': reductions,
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'cuts': analysis.get('cuts', []),
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'retained': analysis.get('retained', []),
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'verdict': analysis.get('verdict', ''),
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}
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# ── HTML Template ──────────────────────────────────────────────────────────────
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HTML_TEMPLATE = r"""<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>BMad Method · Skill Conversion: SKILL_NAME</title>
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<style>
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:root {
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--bg: #0d1117; --surface: #161b22; --surface2: #21262d; --border: #30363d;
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--text: #e6edf3; --text-muted: #8b949e; --text-dim: #6e7681;
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--critical: #f85149; --high: #f0883e; --medium: #d29922; --low: #58a6ff;
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--strength: #3fb950; --accent: #58a6ff; --accent-hover: #79c0ff;
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--purple: #a371f7;
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--font: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif;
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--mono: ui-monospace, SFMono-Regular, "SF Mono", Menlo, Consolas, monospace;
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}
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@media (prefers-color-scheme: light) {
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:root {
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--bg: #ffffff; --surface: #f6f8fa; --surface2: #eaeef2; --border: #d0d7de;
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--text: #1f2328; --text-muted: #656d76; --text-dim: #8c959f;
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--critical: #cf222e; --high: #bc4c00; --medium: #9a6700; --low: #0969da;
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--strength: #1a7f37; --accent: #0969da; --accent-hover: #0550ae;
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--purple: #8250df;
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}
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}
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* { margin: 0; padding: 0; box-sizing: border-box; }
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body { font-family: var(--font); background: var(--bg); color: var(--text); line-height: 1.5; padding: 2rem; max-width: 900px; margin: 0 auto; }
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h1 { font-size: 1.5rem; margin-bottom: 0.25rem; }
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.subtitle { color: var(--text-muted); font-size: 0.85rem; margin-bottom: 1.5rem; }
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.hero { text-align: center; padding: 2rem 1rem; margin-bottom: 1.5rem; border: 1px solid var(--border); border-radius: 0.75rem; background: var(--surface); }
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.hero-pct { font-size: 4rem; font-weight: 800; color: var(--strength); line-height: 1; }
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.hero-label { font-size: 1.1rem; color: var(--text-muted); margin-top: 0.25rem; }
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.hero-sub { font-size: 0.9rem; color: var(--text-dim); margin-top: 0.5rem; }
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.metrics-table { width: 100%; border-collapse: collapse; margin: 1.5rem 0; }
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.metrics-table th { text-align: left; padding: 0.5rem 0.75rem; border-bottom: 2px solid var(--border); font-size: 0.8rem; text-transform: uppercase; letter-spacing: 0.05em; color: var(--text-muted); }
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.metrics-table td { padding: 0.5rem 0.75rem; border-bottom: 1px solid var(--border); font-size: 0.95rem; }
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.metrics-table .num { font-family: var(--mono); text-align: right; }
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.metrics-table .reduction { font-weight: 700; color: var(--strength); text-align: right; }
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.bar-cell { width: 30%; }
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.bar-container { display: flex; height: 1.25rem; border-radius: 0.25rem; overflow: hidden; background: color-mix(in srgb, var(--critical) 15%, transparent); }
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.bar-rebuilt { background: var(--strength); border-radius: 0.25rem 0 0 0.25rem; transition: width 0.3s; }
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.section { border: 1px solid var(--border); border-radius: 0.5rem; margin: 0.75rem 0; overflow: hidden; }
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.section-header { display: flex; align-items: center; gap: 0.75rem; padding: 0.75rem 1rem; background: var(--surface); cursor: pointer; user-select: none; }
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.section-header:hover { background: var(--surface2); }
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.section-header .arrow { font-size: 0.7rem; transition: transform 0.15s; color: var(--text-muted); width: 1rem; }
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.section-header.open .arrow { transform: rotate(90deg); }
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.section-header .label { font-weight: 600; flex: 1; }
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.section-body { display: none; }
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.section-body.open { display: block; }
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.cut-item { padding: 0.75rem 1rem; border-top: 1px solid var(--border); }
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.cut-item:hover { background: var(--surface); }
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.cut-category { font-weight: 600; font-size: 0.95rem; }
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.cut-desc { font-size: 0.85rem; color: var(--text-muted); margin-top: 0.25rem; }
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.cut-examples { margin-top: 0.5rem; padding-left: 1.25rem; }
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.cut-examples li { font-size: 0.85rem; color: var(--text-dim); padding: 0.1rem 0; }
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.badge { display: inline-flex; align-items: center; padding: 0.15rem 0.5rem; border-radius: 2rem; font-size: 0.75rem; font-weight: 600; margin-right: 0.5rem; }
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.badge-high { background: color-mix(in srgb, var(--critical) 20%, transparent); color: var(--critical); }
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.badge-medium { background: color-mix(in srgb, var(--medium) 20%, transparent); color: var(--medium); }
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.badge-low { background: color-mix(in srgb, var(--low) 20%, transparent); color: var(--low); }
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.retained-item { padding: 0.5rem 1rem; border-top: 1px solid var(--border); }
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.retained-category { font-weight: 600; font-size: 0.9rem; color: var(--strength); }
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.retained-desc { font-size: 0.85rem; color: var(--text-muted); }
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.verdict { margin-top: 1.5rem; padding: 1.25rem; border: 1px solid var(--border); border-radius: 0.5rem; background: var(--surface); font-size: 1rem; line-height: 1.6; color: var(--text); font-style: italic; }
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.verdict::before { content: "Bottom line: "; font-weight: 700; font-style: normal; color: var(--purple); }
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.footer { margin-top: 2rem; padding-top: 1rem; border-top: 1px solid var(--border); font-size: 0.8rem; color: var(--text-dim); text-align: center; }
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</style>
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</head>
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<body>
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<div style="color:var(--purple);font-size:0.8rem;font-weight:600;letter-spacing:0.05em;text-transform:uppercase;margin-bottom:0.25rem">BMad Method</div>
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<h1>Skill Conversion: <span id="skill-name"></span></h1>
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<div class="subtitle" id="subtitle"></div>
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<div class="hero" id="hero"></div>
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<table class="metrics-table">
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<thead>
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<tr>
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<th>Metric</th>
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<th class="num">Original</th>
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<th class="num">Rebuilt</th>
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<th class="num">Reduction</th>
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<th class="bar-cell">Comparison</th>
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</tr>
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</thead>
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<tbody id="metrics-body"></tbody>
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</table>
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<div id="cuts-section"></div>
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<div id="retained-section"></div>
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<div class="verdict" id="verdict"></div>
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<div class="footer">
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Generated by <strong>BMad Workflow Builder</strong> · <code>--convert</code>
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</div>
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<script>
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const DATA = JSON.parse(document.getElementById('report-data').textContent);
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function esc(s) {
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if (!s) return '';
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const d = document.createElement('div');
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d.textContent = String(s);
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return d.innerHTML;
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}
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function fmt(n) { return typeof n === 'number' ? n.toLocaleString() : String(n); }
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function init() {
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const m = DATA.meta;
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document.getElementById('skill-name').textContent = m.skill_name;
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const parts = [m.original_source, m.timestamp ? m.timestamp.split('T')[0] : ''].filter(Boolean);
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document.getElementById('subtitle').textContent = parts.join(' \u2022 ');
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// Hero — overall token reduction
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const tokenRed = DATA.reductions.estimated_tokens || DATA.reductions.words || '0%';
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const origTok = DATA.metrics.original.estimated_tokens || 0;
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const newTok = DATA.metrics.rebuilt.estimated_tokens || 0;
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document.getElementById('hero').innerHTML =
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'<div class="hero-pct">' + esc(tokenRed) + '</div>' +
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'<div class="hero-label">leaner</div>' +
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'<div class="hero-sub">' + fmt(origTok) + ' tokens \u2192 ' + fmt(newTok) + ' tokens</div>';
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// Metrics table
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var rows = [
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['Lines', 'lines'], ['Words', 'words'], ['Characters', 'chars'],
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['Sections', 'sections'], ['Files', 'files'], ['Est. Tokens', 'estimated_tokens']
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];
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var tbody = '';
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rows.forEach(function(r) {
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var label = r[0], key = r[1];
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var orig = DATA.metrics.original[key] || 0;
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var rebuilt = DATA.metrics.rebuilt[key] || 0;
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var reduction = DATA.reductions[key] || (key === 'files' ? '' : 'N/A');
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var pct = orig > 0 ? (rebuilt / orig * 100) : 0;
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tbody += '<tr>';
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tbody += '<td>' + label + '</td>';
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tbody += '<td class="num">' + fmt(orig) + '</td>';
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tbody += '<td class="num">' + fmt(rebuilt) + '</td>';
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tbody += '<td class="reduction">' + (reduction || '') + '</td>';
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tbody += '<td class="bar-cell"><div class="bar-container">';
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tbody += '<div class="bar-rebuilt" style="width:' + pct.toFixed(1) + '%"></div>';
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tbody += '</div></td>';
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tbody += '</tr>';
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});
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document.getElementById('metrics-body').innerHTML = tbody;
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renderCuts();
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renderRetained();
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// Verdict
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var v = DATA.verdict || '';
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if (v) document.getElementById('verdict').appendChild(document.createTextNode(v));
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else document.getElementById('verdict').style.display = 'none';
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}
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function renderCuts() {
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var cuts = DATA.cuts || [];
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if (!cuts.length) return;
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var html = '<div class="section"><div class="section-header open" onclick="toggle(this)">';
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html += '<span class="arrow">▶</span>';
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html += '<span class="label">What Was Cut (' + cuts.length + ' categories)</span>';
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html += '</div><div class="section-body open">';
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cuts.forEach(function(cut) {
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html += '<div class="cut-item">';
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var sev = cut.severity || 'medium';
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html += '<span class="badge badge-' + sev + '">' + esc(sev) + '</span>';
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html += '<span class="cut-category">' + esc(cut.category) + '</span>';
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html += '<div class="cut-desc">' + esc(cut.description) + '</div>';
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if (cut.examples && cut.examples.length) {
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html += '<ul class="cut-examples">';
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cut.examples.forEach(function(ex) { html += '<li>' + esc(ex) + '</li>'; });
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html += '</ul>';
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}
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html += '</div>';
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});
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html += '</div></div>';
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document.getElementById('cuts-section').innerHTML = html;
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}
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function renderRetained() {
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var items = DATA.retained || [];
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if (!items.length) return;
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var html = '<div class="section"><div class="section-header open" onclick="toggle(this)">';
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html += '<span class="arrow">▶</span>';
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html += '<span class="label">What Survived (' + items.length + ' categories)</span>';
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html += '</div><div class="section-body open">';
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items.forEach(function(r) {
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html += '<div class="retained-item">';
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html += '<div class="retained-category">' + esc(r.category) + '</div>';
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html += '<div class="retained-desc">' + esc(r.description) + '</div>';
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html += '</div>';
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});
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html += '</div></div>';
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document.getElementById('retained-section').innerHTML = html;
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}
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function toggle(el) {
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el.classList.toggle('open');
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el.nextElementSibling.classList.toggle('open');
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}
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init();
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</script>
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</body>
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</html>"""
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def generate_html(report_data: dict) -> str:
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"""Inject report data into the HTML template."""
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data_json = json.dumps(report_data, indent=None, ensure_ascii=False)
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data_tag = f'<script id="report-data" type="application/json">{data_json}</script>'
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html = HTML_TEMPLATE.replace(
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'<script>\nconst DATA',
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f'{data_tag}\n<script>\nconst DATA',
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)
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skill_name = report_data.get('meta', {}).get('skill_name', 'Unknown')
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html = html.replace('SKILL_NAME', html_lib.escape(skill_name))
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return html
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def main() -> int:
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parser = argparse.ArgumentParser(
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description='Generate an interactive HTML skill conversion comparison report',
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)
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parser.add_argument(
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'original_path',
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type=Path,
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help='Path to original skill (directory or single .md file)',
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)
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parser.add_argument(
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'rebuilt_path',
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type=Path,
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help='Path to rebuilt skill directory',
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)
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parser.add_argument(
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'analysis_json',
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type=Path,
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help='Path to LLM-generated convert-analysis.json',
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)
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parser.add_argument(
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'--output', '-o',
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type=Path,
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help='Output HTML file path (default: <analysis-dir>/convert-report.html)',
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)
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parser.add_argument(
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'--open',
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action='store_true',
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help='Open the HTML report in the default browser',
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)
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args = parser.parse_args()
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# Validate inputs
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for label, path in [('Original', args.original_path),
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('Rebuilt', args.rebuilt_path),
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('Analysis', args.analysis_json)]:
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if not path.exists():
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print(f'Error: {label} path not found: {path}', file=sys.stderr)
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return 2
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# Measure both skills
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original_metrics = measure_skill(args.original_path)
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rebuilt_metrics = measure_skill(args.rebuilt_path)
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reductions = calculate_reductions(original_metrics, rebuilt_metrics)
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# Load LLM analysis
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analysis = json.loads(args.analysis_json.read_text(encoding='utf-8'))
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# Build report data
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report_data = build_report_data(
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original_metrics, rebuilt_metrics, analysis, reductions,
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)
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# Save structured report data alongside analysis
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report_data_path = args.analysis_json.parent / 'convert-report-data.json'
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report_data_path.write_text(
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json.dumps(report_data, indent=2, ensure_ascii=False),
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encoding='utf-8',
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)
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# Generate HTML
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html = generate_html(report_data)
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output_path = args.output or (args.analysis_json.parent / 'convert-report.html')
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output_path.write_text(html, encoding='utf-8')
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# Summary to stdout
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print(json.dumps({
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'html_report': str(output_path),
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'original': original_metrics,
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'rebuilt': rebuilt_metrics,
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'reductions': reductions,
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}))
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if args.open:
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system = platform.system()
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if system == 'Darwin':
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subprocess.run(['open', str(output_path)])
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elif system == 'Linux':
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subprocess.run(['xdg-open', str(output_path)])
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elif system == 'Windows':
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subprocess.run(['start', str(output_path)], shell=True)
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return 0
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if __name__ == '__main__':
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sys.exit(main())
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