anylens

Any link, any lens. Paste a URL — a conference talk, a product demo, a dense PDF, an article — and get it back as understanding shaped for who's asking, with every claim linked to the exact second or page it came from.

Agents can read text. anylens lets them read everything else — and lets you check their work.

Code

analyze(url)  →  one cached Understanding  →  lens(source, "student")
                                           →  lens(source, "spec")
                                           →  lens(source, "uiux")   …

Why not just ask a chatbot to summarize it?

A summary is a dead end. You can't verify it, you can't get a different cut of it without paying for it again, it's blind to what was on screen, and no agent can act on it. anylens fixes those four things:

Verifiable — every claim carries its timestamp or page. stated is never blurred with inferred. Missing analysis is named, not hidden.
Many readers, one extraction — the expensive pass runs once and is cached forever; a second lens is cheap and instant.
Reads what was shown — on-screen code, slides, UI states, demos, not just the words. A silent ten-second screen recording still produces a full analysis.
Built for agents too — the spec lens turns any source into implementable requirements with acceptance criteria, over MCP.
Knows how it was made — for video, a production pass captures the music, the sound design, the shot rhythm, the motion and typography, and turns it into a recipe you (or a generator) can rebuild from. Content lenses let an agent know; the blueprint lens lets it make.

Lenses

Lens	For	Gives you
`student`	learning without watching	layered explainer, concept map, flashcards
`developer`	building from it	code shown on screen, decisions, implementation steps
`uiux`	design study	screens, flows, interaction patterns
`researcher`	citable notes	claims + evidence, every one anchored
`teardown`	competitive analysis	features (shown vs claimed), flows, positioning
`spec`	coding agents	requirements, acceptance criteria, ambiguities
`blueprint`	remaking it	music, sound design, shot rhythm, motion, typography — as a production recipe
`design`	"make it look like this"	measured colour tokens, spacing and type rhythm, components — as paste-ready CSS
`ask`	one specific question	an anchored answer — or an honest "the source doesn't answer this"
`raw`	agents, debugging	the Understanding Object itself — no LLM, no cost

Lenses are single markdown files in src/lenses/. Adding one is adding a file — no code.

Beyond lenses: anylens can learn a reusable style from analyzed videos — the pacing, structure, look, motion, and sound as portable rules, every rule carrying the timestamp it was learned from — then plan new work in that style (style_extract / style_apply over MCP, or "Learn this style" in the web UI).

Quickstart

Prerequisites: Bun 1.3+, yt-dlp, and ffmpeg (brew install yt-dlp ffmpeg on macOS).

git clone https://github.com/Slowper/anylens && cd anylens
bun install

Keys — run bun run web and paste them on the Your keys page (they're validated as you save), or create ~/.anylens/env (mode 600) yourself:

Code

GOOGLE_API_KEY=...      # required: analysis, structuring, lens rendering
GROQ_API_KEY=...        # optional: Whisper transcripts for sources without captions
ANTHROPIC_API_KEY=...   # optional: run the writing on Claude instead
OPENAI_API_KEY=...      # optional: run the writing on an OpenAI model instead

Get them at aistudio.google.com and console.groq.com. Nothing leaves your machine except the calls you make to those APIs. The text layer is provider-agnostic: set ANYLENS_TEXT_MODEL=anthropic (or groq, or openai/<model>) to choose who writes the lenses and answers — the media passes stay on Gemini, which is what it's genuinely best at.

Try it — from the terminal:

server.ts

bun bin/anylens.ts "https://www.youtube.com/watch?v=..."              # student explainer
bun bin/anylens.ts ./screenshot.png --lens design --export css        # tokens you can paste
bun bin/anylens.ts ~/Desktop/demo.mov --lens blueprint                # files on your machine work too
bun bin/anylens.ts "<url>" --lens spec                                # any lens
bun bin/anylens.ts "<url>" --ask "does this cover error handling?"    # one anchored answer
bun bin/anylens.ts "<url>" --clip "where they demo the CLI"           # cut that segment to mp4
bun bin/anylens.ts "<url>" --export anki                              # flashcard deck (or markdown/json/html)
bun bin/anylens.ts "<url>" --audio                                    # narrate it to an mp3
bun bin/anylens.ts "<url>" --open                                     # render the page and open it

Analysis takes a few minutes the first time and is cached forever after, so every later lens on the same link returns instantly. Pass a source_id instead of a URL to re-lens something already analyzed.

Or use the web UI:

bun run web        # → http://127.0.0.1:4517 — paste a link, pick a lens

Use it from an agent — any MCP client

anylens speaks standard MCP over stdio, so Claude Code, Codex, Cursor, Gemini CLI, and anything else MCP-capable can use it the same way.

# Claude Code
claude mcp add --scope user anylens -- bun /absolute/path/to/anylens/bin/anylens.ts mcp

toml

# Codex CLI — ~/.codex/config.toml
[mcp_servers.anylens]
command = "bun"
args = ["/absolute/path/to/anylens/bin/anylens.ts", "mcp"]

json

// Gemini CLI (~/.gemini/settings.json), Cursor (~/.cursor/mcp.json), or any other client
{ "mcpServers": { "anylens": { "command": "bun", "args": ["/absolute/path/to/anylens/bin/anylens.ts", "mcp"] } } }

Once published to npm, npx anylens mcp replaces the path form everywhere.

Tools: analyze(url) → status(job_id) → lens(source_id, lens) · ask(source_id, question) · clip(source_id, query) · export(source_id, format) · audio(source_id) · style_extract / style_apply / style_list · lenses(). lens returns { data, markdown } — typed JSON for the agent, readable markdown for the human, plus a full HTML page with include_html. ask answers from the cache without re-analyzing.

What it reads

Video (YouTube, X, LinkedIn, Vimeo, Twitch, Loom, direct files — anything yt-dlp reaches), audio, PDFs (including scanned and figure-heavy ones, read page by page with vision), web articles, and images.

Full inventory: docs/FEATURES.md · Where it's going: docs/ROADMAP.md · Architecture and invariants: AI_INDEX.md

Contributing

New lenses are the easiest and most valuable contribution — see CONTRIBUTING.md. The codebase is deliberately small files with one concern each, so a change rarely touches more than one place.

MIT licensed.