ModLens is a vision plugin that lets text-only coding agents analyze images pasted into chat and return structured evidence such as OCR, layout, and semantic information. It is designed for DeepSeek Harness and provides visual input to models that cannot read images directly. The catalogue skill and instruction support using this plugin.
Borrowing it
Nothing to install: this file belongs to liustack/modlens. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/liustack/modlens/main/AGENTS.mdgit clone --depth 1 https://github.com/liustack/modlensWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/liustack/modlens/agents-md)<a href="https://agentmods.dev/instructions/liustack/modlens/agents-md"><img src="https://agentmods.dev/badge/instructions/liustack/modlens/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/liustack/modlens/agents-md"><img src="https://agentmods.dev/badge/instructions/liustack/modlens/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01760 | $0.01760 |
| Opus 5.5 | $0.00704 | $0.00704 |
| Sonnet 5.5 | $0.00352 | $0.00352 |
| Haiku 4.5 | $0.00176 | $0.00176 |
Grade A, and why
modlens AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Overview (for AI Agent)
Goal
Provide the modlens CLI tool that converts image sources (local path or remote URL) into structured text evidence for non-vision LLM workflows.
Scope
The contract: an image the user hands over is read once, and that read leaves text later turns can quote. The image is in the conversation because the user pasted it, dropped a path, or gave a URL.
Do not add:
- a camera, screenshot capture, or hotkeys
- CDP, or holding a browser session
- computer-use (screen plus keyboard and mouse)
- a pixel toolbox (grounding, crop, pixel-diff, reconstructing a UI)
- bounding boxes or confidence scores (dropped from the schema on purpose)
Visual parsing is the only job. Web search and page fetching live in modsearch.
Technical Approach
- Six vision providers behind one interface (
src/providers/index.ts). Subprocess providers implementbuildInvocation+parseOutput(antigravity-cli, claude-cli, kimi-cli); in-process API providers implementexecute(gemini-api, openai, anthropic).antigravity-cliis the zero-config default, andkimi-cliruns only when named, since it spends a subscription. - Schema-enforced JSON output wherever the backend allows:
--json-schemaon the Claude and Antigravity CLIs (kimi-cli has no such flag and uses the template),responseJsonSchemaon gemini-api, a forced tool call on anthropic. The openai route uses a template-instance prompt (weak gateways echo raw schemas back) plus shape validation that fails loudly. - Layered config: CLI flags >
~/.modlens/config.json(managed bymodlens config init/set/show, 0600, masked rendering) > built-ins. Since 3.17.0 a provider's settings come from one source, whole: the file when it mentions that provider, the bound environment variables (GEMINI_API_KEY,GEMINI_BASE_URL,OPENAI_API_KEY,OPENAI_BASE_URL,ANTHROPIC_API_KEY,ANTHROPIC_BASE_URL) when it does not. They used to merge field by field, and an endpoint and a key are one credential, so drawing halves from two places built a pairing that existed in neither.apiKey(file or env) accepts a comma-separated list and rotates after auth, rate-limit, or quota failures. Quota cooldown state lives in~/.modlens/state.json.MODLENS_MODEL,MODLENS_HARNESSand the proxy conventions are unaffected. - Vendor knobs pass through, they are not modelled:
<provider>.extraBody(or--extra-body) deep-merges a JSON object into the request body of the three API providers, which is how thinking gets turned off. No per-vendor table lives in the code, because the spelling differs per gateway and a wrong guess either 400s or is ignored silently. The fields carrying the image, the prompt, and the schema are reserved (src/util/extraBody.ts). - Paste recovery across harnesses:
modlens recover-pastepulls pasted image bytes out of local session storage (pastes never hit a regular temp file). It supports Claude Code and Pi (JSONL transcripts) and OpenCode (SQLite), detects Codex and defers to its on-disk temp files, and scopes to the harness it runs inside via process ancestry. Exact targeting via--session, else newest-image-timestamp scanning. Storage layouts are each harness's internals, so treat this as best-effort. - Single responsibility: visual parsing only. Web search and page fetching live in
modsearch.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 102 lines · 1,760 tokens per session scan A 6ee3be1fda80
modlens AGENTS.md is an instructions file published in the GitHub repository liustack/modlens (4,537 stars, last pushed 4d ago), licensed MIT. It adds 1,760 tokens to every session, about $0.0070 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deno copilot-instructions.md
Copilot instructions for denoland/deno, covering deno development guide for github copilot, network access, high level overview, key directories and key files to understand first.