analyze-url

Analyze URL

Scrape URL → deep technical and strategic analysis.

Entry

/analyze-url https://example.com

¬U → → DP(B)to get one.

Step 1 — Locate Plugin

PLUGIN_ROOT=$(find ~/projects -maxdepth 4 -path "*/web-intel/pyproject.toml" -print -quit 2>/dev/null | xargs dirname)
if [ -z "$PLUGIN_ROOT" ]; then
  echo "ERROR: web-intel plugin not found. Install: claude plugin install web-intel"
  exit 1
fi

First Use

First invocation in session only:

cd "$PLUGIN_ROOT" && uv run python scripts/doctor.py

• exit 1 → show output, stop, guide install. Optional warnings → inform user, continue.
• Skip on subsequent invocations.

Step 2 — Scrape

cd "$PLUGIN_ROOT" && SSL_CERT_FILE=/etc/ssl/certs/ca-certificates.crt REQUESTS_CA_BUNDLE=/etc/ssl/certs/ca-certificates.crt uv run python scripts/scraper.py "$URL"

Step 3 — Deep Analysis

SaaS / Product URLs

1. Product overview — what, audience, value prop
2. Tech stack signals — frameworks, infra, CDN, visible APIs
3. Architecture patterns — SPA vs SSR, API-first, microservices signals
4. Business model — pricing, freemium, enterprise, self-serve
5. Competitive positioning — market category, differentiation, moat
6. Growth signals — community size, hiring, funding, integrations
7. Strengths (3-5 bullets) | Weaknesses (3-5 bullets)

GitHub Repos

1. Project health — stars, forks, recent activity, contributor count
2. Architecture — language, framework, project structure
3. Code quality signals — CI/CD, tests, linting, type safety
4. Documentation — README quality, docs site, examples
5. Community — issues, PRs, discussions activity
6. Notable patterns worth adopting

YouTube Videos

Run full video analysis pipeline (tempfile per ${CLAUDE_PLUGIN_ROOT}/../shared/references/tempfile-convention.md):

TMPDIR=$(mktemp -d -t "web-intel-video-analysis-XXXXXX")
trap 'rm -rf "$TMPDIR"' EXIT
ANALYSIS="$TMPDIR/analysis.json"
cd "$PLUGIN_ROOT" && SSL_CERT_FILE=/etc/ssl/certs/ca-certificates.crt uv run python scripts/video_analyzer.py "$URL" --output "$ANALYSIS"

Pipeline: scrape metadata + transcript → download video (yt-dlp, 1080p max) → extract frames at 1fps (ffmpeg) → auto-detect best local VLM by GPU VRAM (qwen3-vl via Ollama) → batch-describe frames → output JSON.

Read "$ANALYSIS" → analyze:

1. Video overview — title, channel, duration, views, engagement rate
2. Content analysis — thesis, structure, rhetorical techniques (from transcript)
3. Visual analysis — techniques (3D/2D/photography), palette, composition, transitions (from frames)
4. Production quality — animation, visual variety, text/typography, pacing
5. Scene breakdown — visual timeline mapped to narration
6. Strengths (3-5) | Weaknesses (3-5)

video_analyzer fails (¬GPU ∨ ¬Ollama) → fall back to transcript-only via standard scraper.

Articles / Content

1. Thesis — main argument or claim
2. Evidence quality — data, sources, methodology
3. Implications — what this means for the field
4. Counter-arguments — what the author missed
5. Actionable insights

Step 4 — Present

Output structured markdown with clear headers. Include confidence (high|medium|low) for inferred info. success: false → fall back to WebFetch.

$ARGUMENTS