screen-ocr-operator

screen-ocr-operator is a skill for Claude Code, Codex from qiannianhuanxiang/DSHA. It costs 0 tokens per session (1,121 once invoked), scanned A, original, MIT.

A workflow for controlling an Android device through screenshots, screen text recognition, and ADB, a command-line tool for sending actions to Android. It plans several actions together and checks the screen at important points.

In plain words
What is it for?
Use it to tap controls, enter text, press keys, open Android screens, inspect the current app, and complete multi-step screen tasks.
Why use it?
It reduces repeated screenshot and recognition steps while still verifying that major actions produced the expected screen.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to tap controls, enter text, press keys, open Android screens, inspect the current app, and complete multi-step screen tasks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/qiannianhuanxiang/dsha/screen-ocr-operator
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add qiannianhuanxiang/DSHA --skill screen-ocr-operator
Clone the repo
git clone --depth 1 https://github.com/qiannianhuanxiang/DSHA

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for screen-ocr-operator

README.md
[![agentmods](https://agentmods.dev/badge/skills/qiannianhuanxiang/dsha/screen-ocr-operator/github.svg)](https://agentmods.dev/skills/qiannianhuanxiang/dsha/screen-ocr-operator)
Your own site
<a href="https://agentmods.dev/skills/qiannianhuanxiang/dsha/screen-ocr-operator"><img src="https://agentmods.dev/badge/skills/qiannianhuanxiang/dsha/screen-ocr-operator/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.

agentmods 80×15 button for screen-ocr-operator

Your own site · 80×15
<a href="https://agentmods.dev/skills/qiannianhuanxiang/dsha/screen-ocr-operator"><img src="https://agentmods.dev/badge/skills/qiannianhuanxiang/dsha/screen-ocr-operator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,121 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.01121
Opus 5 $0.00000 $0.00561
Sonnet 5 $0.00000 $0.00224
Haiku 4.5 $0.00000 $0.00112

Measured 12d ago against content hash 8cec4cabad81, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

screen-ocr-operator 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 12d 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.

agent-skills/screen-ocr-operator/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Screen OCR Operator (Commander Mode)

Act as the commander: you issue ADB commands, the vision model looks at screenshots and returns a concrete operation plan. The goal is to finish screen tasks with the fewest possible screenshot/OCR round-trips.

Roles

  • Commander (you): capture screenshots, call the vision model, execute ADB taps/input, verify.
  • Eyes (vision model): OCR the screen, identify UI elements, return coordinates and steps.
  • Hands (ADB): input tap, input text, input keyevent, am start, etc.

Key optimization rules

  1. Ask the model for a complete plan, not a single action.
    • Give the model the task + current screenshot.
    • Ask it to return a JSON list of actions with coordinates:
      {
        "actions": [
          {"type": "tap", "x": 630, "y": 1090},
          {"type": "text", "value": "hello"},
          {"type": "key", "key": "ENTER"}
        ],
        "verify": "expected screen after actions"
      }
      
  2. One model call per meaningful milestone.
    • Don't screenshot + OCR after every tap.
    • Only re-ask when the next step depends on a changed screen or after a risky action.
  3. Use Android APIs for deterministic checks first.
    • dumpsys window | grep mCurrentFocus → which app is foreground.
    • dumpsys input_method | grep mInputShown → is keyboard open.
    • uiautomator dump → native UI nodes/bounds when available.
    • These are faster and more reliable than OCR for state checks.
  4. Shrink screenshots before sending to the model.
    • Capture full screen, then downscale to max width ~720px and save as JPEG quality ~80.
    • Smaller payload = much faster API round-trip.
  5. Batch ADB commands.
    • Combine independent shell commands into one adb shell invocation.
    • Keep sleeps short (sleep 0.51) and only wait when the UI actually needs time.
  6. Verify at the end (or at major checkpoints).
    • Final screenshot + one model confirmation is usually enough.
    • For long tasks, verify after each phase, not after every tap.

Read the full file on GitHub · 103 lines

Changes

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.

  1. 12d ago First seen · 103 lines · 0 tokens per session scan A 8cec4cabad81

Subscribe to this mod's changes

screen-ocr-operator is a skill published in the GitHub repository qiannianhuanxiang/DSHA (392 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,121 tokens. 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.

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