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.
npx skills add appergb/desktop-agent-ops --skill desktop-agent-opsgit clone --depth 1 https://github.com/appergb/desktop-agent-opsWrote 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/skills/appergb/desktop-agent-ops/desktop-agent-ops)<a href="https://agentmods.dev/skills/appergb/desktop-agent-ops/desktop-agent-ops"><img src="https://agentmods.dev/badge/skills/appergb/desktop-agent-ops/desktop-agent-ops.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.05098 |
| Opus 5 | $0.00019 | $0.02549 |
| Sonnet 5 | $0.00008 | $0.01020 |
| Haiku 4.5 | $0.00004 | $0.00510 |
Grade A, and why
desktop-agent-ops 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 7d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- desktop-agent-ops — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 426 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This is the detailed reference manual. For the quick operations guide, see
desktop-agent-ops.md.
Desktop Agent Ops — Detailed Reference Manual
Table of Contents
- Tool Priority
- Auto-setup Gate
- Core Execution Loop
- Smart Targeting with Four-Layer Fallback
- Failure Recovery
- Generalization: How to Apply This to ANY App
- Text Input and Send Rules
- DPI / HiDPI / Retina
- CLI Reference
- Workflow Examples
- Reference Documents
- Scope
- Hard Rules
- Custom Workflows
Tool Priority
Use this skill ONLY as Priority 3 — after MCP servers/structured APIs (Priority 1) and native CLI/AppleScript (Priority 2). See desktop-agent-ops.md section 1 for the full decision framework.
Rule: Never use screen OCR to do what a structured API can do.
Auto-setup Gate
Run first_run_setup.py --check at session start. If not ready, run first_run_setup.py to auto-install all dependencies. Then set $PY. See desktop-agent-ops.md section 2 for details.
Core Execution Loop
FOCUS → LOCATE (accessibility/OCR) → BOUNDS-CHECK → MOVE → READBACK → EXECUTE → VERIFY
CRITICAL RULE: Click coordinates MUST come from accessibility or OCR output — NEVER from visual estimation of screenshots. Models frequently confuse left/right and misjudge pixel distances. Structured accessibility and OCR output return exact pixel coordinates.
CRITICAL RULE: Move → Readback → Click. Before every click, move the cursor first, read back mouse-position, verify the offset is ≤ 5px, then click. Never click without readback.
CRITICAL RULE: Re-locate before every click in multi-step tasks. Window positions, dialog states, and UI layouts change between steps. Never reuse coordinates from a previous step.
What ships with it
60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 528 B
- CHANGELOG.md 17 KB
- desktop-agent-ops.md 17 KB
- references/app-names.md 9.4 KB
- references/app-wechat-desktop.md 3.3 KB
- references/app-wechat-macos.md 158 B
- references/app-wechat-windows.md 160 B
- references/chat-app-macos.md 3.5 KB
- references/cleanup-rules.md 1.2 KB
- references/collaboration-rules.md 1.4 KB
- references/coordinate-reconstruction.md 3.0 KB
- references/custom-workflows.md 4.9 KB
- references/eval-scenarios.md 2.7 KB
- references/example-cases.md 18 KB
- references/market-precision-targeting-gap-analysis.md 21 KB
- references/operation-patterns.md 5.5 KB
- references/platform-linux.md 2.0 KB
- references/platform-macos.md 3.9 KB
- references/platform-windows.md 4.2 KB
- references/precise-targeting.md 4.0 KB
- references/reproducible-setup.md 3.2 KB
- references/target-providers.md 4.7 KB
- references/validation-patterns.md 3.4 KB
- references/workflow.md 4.0 KB
- scripts/accessibility_provider.py 3.0 KB runs code
- scripts/ax_provider.py 7.6 KB runs code
- scripts/cleanup_task.py 1.0 KB runs code
- scripts/click_and_verify.py 4.6 KB runs code
- scripts/desktop_ops.py 15 KB runs code
- scripts/dispatch_agent.py 21 KB runs code
- scripts/doctor.py 5.5 KB runs code
- scripts/first_run_setup.py 25 KB runs code
- scripts/input_runtime.py 1.4 KB runs code
- scripts/linux_atspi_provider.py 5.5 KB runs code
- scripts/local_agent.py 26 KB runs code
- scripts/ocr_text.py 14 KB runs code
- scripts/permission_bootstrap.py 9.1 KB runs code
- scripts/platform_probe.py 2.8 KB runs code
- scripts/pointer_runtime.py 5.0 KB runs code
- scripts/region_diff.py 3.2 KB runs code
- scripts/resolve_python.py 1.7 KB runs code
- scripts/runtime_support.py 448 B runs code
- scripts/screen_runtime.py 5.4 KB runs code
- scripts/secret_scanner.py 7.7 KB runs code
- scripts/smoke_test.py 3.2 KB runs code
- scripts/target_provider_chain.py 8.4 KB runs code
- scripts/target_report.py 2.1 KB runs code
- scripts/target_resolver.py 3.3 KB runs code
- scripts/target_runtime.py 3.5 KB runs code
- scripts/targeting.py 3.2 KB runs code
- scripts/task_context.py 5.4 KB runs code
- scripts/task_paths.py 972 B runs code
- scripts/template_match.py 4.3 KB runs code
- scripts/text_runtime.py 7.4 KB runs code
- scripts/vision_ocr.py 8.8 KB runs code
- scripts/window_backends.py 14 KB runs code
- scripts/window_kernel.py 2.0 KB runs code
- scripts/window_regions.py 2.3 KB runs code
- scripts/windows_uia_provider.py 7.5 KB runs code
- scripts/workflow_executor.py 4.7 KB runs code
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.
- 7d ago First seen · 426 lines · 38 tokens per session scan A ff6cbabbccd4
desktop-agent-ops is a skill published in the GitHub repository appergb/desktop-agent-ops (17 stars, last pushed 5mo ago), licensed MIT. It adds 38 tokens to every session and 5,098 once invoked, about $0.0002 per session on Opus 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 skills, from other repositories
orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
pinchtab-mcp
Use this skill when a task requires browser automation through PinchTab's MCP server connected to a remote browser instance. Covers navigation, element interaction, data extraction, form filling, multi-step flows, and session management via MCP tools.
peekaboo
Capture and automate macOS UI with the Peekaboo CLI.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.