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computer-use-agents

Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.

Quality

74%

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./cli-tool/components/skills/ai-research/computer-use-agents/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body presents three genuinely useful patterns (agent loop, sandboxing, Anthropic implementation) with concrete code and security checklists, but it is let down by truncated code blocks, a Sharp Edges table filled with placeholder 'Issue' entries instead of real content, and the absence of any validation workflow or bundle-file structure. It reads as a promising draft that was cut off mid-generation.

Suggestions

Complete the truncated code blocks: ComputerUseAgent.execute_action ends mid-f-string ('f"scrolled {dir'), the sandboxing Python wrapper ends at 'from dataclasses im', and AnthropicComputerUse._handle_computer_action stops after opening the screenshot file — finishing these is the single biggest actionability gap.

Replace the placeholder Sharp Edges table rows ('| Issue | critical | ## ... |') with the actual issues, their severity, and concrete solutions, e.g. dropdown/scrollbar manipulation difficulty, runaway loops, token cost of repeated screenshots, and pause-pattern detectability.

Move the Dockerfile and docker-compose.yml into bundle files (e.g. references/sandbox.Dockerfile, references/docker-compose.yml) referenced one level deep, and add explicit validation checkpoints such as verifying the sandbox blocks network egress and runs as non-root before launching the agent.

DimensionReasoningScore

Conciseness

The body is mostly code and checklists rather than padded prose, but includes trivia Claude already knows ('Claude 3.5 Sonnet was the first frontier model to offer computer use') and time-sensitive version identifiers ('claude-sonnet-4-20250514', 'computer_20251124') outside any deprecated/old-patterns section, which the guidelines say should penalize conciseness.

3 / 5

Actionability

There is substantial concrete code (agent class, Dockerfile, docker-compose), but two of the three Python blocks are truncated mid-line ('f"scrolled {dir', 'from dataclasses im') and the Sharp Edges table rows are placeholder text, so the guidance is incomplete rather than executable — matching the score-3 anchor of concrete-but-incomplete, below 4 (not copy-paste ready) and above 2 (real code structure exists).

3 / 5

Workflow Clarity

The perception-reasoning-action loop and the 5-point isolation checklist provide a defined sequence, but there are no validation or verification checkpoints anywhere for operations that act on live systems, capping this at 3 per the rubric's destructive/risky-operation rule.

3 / 5

Progressive Disclosure

No bundle files exist, and roughly 200 lines of infrastructure config (a full Dockerfile and docker-compose.yml that cannot be used from inside markdown) are inlined, though the per-pattern section headers and 'When to use' lines provide real structure — matching the score-3 anchor of content that should be separate sitting inline, rather than the unstructured score-2 anchor.

3 / 5

Total

12

/

20

Passed

Description

88%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong: it states concrete capabilities, names the three relevant ecosystems, flags the security focus, and closes with an explicit 'Use when:' trigger list in exactly the format the rubric rewards. The only weaknesses are slightly unnatural phrasing in one trigger term and a few missing common synonyms.

DimensionReasoningScore

Specificity

The description lists four concrete actions ('viewing screens, moving cursors, clicking buttons, and typing text') plus explicit ecosystem coverage ('Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives') and focus areas ('sandboxing, security... vision-based control'), matching the score-5 anchor of multiple specific actions with comprehensive coverage rather than the minor-gaps profile of score 4.

5 / 5

Completeness

It explicitly answers both 'what' ('Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers...') and 'when' with a concrete trigger list ('Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation'), mirroring the score-5 anchor example's structure; score 4 would require the 'when' to be less explicit than it is.

5 / 5

Trigger Term Quality

'computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation' gives good keyword coverage with natural terms like 'computer use' and 'GUI automation', but 'screen control AI' is awkward phrasing and common variants users would actually say (browser automation, RPA, pyautogui, Operator) are missing — matching score 4's 'a few natural terms missing' rather than score 5's synonym-level breadth.

4 / 5

Distinctiveness Conflict Risk

The niche is clear with distinctive triggers ('computer use', 'vision-based agent'), but 'GUI automation' and 'desktop automation agent' carry minor overlap with generic automation/scripting and browser-automation skills, matching score 4's 'minor overlap risk with closely related skills' rather than score 5's minimal-conflict profile.

4 / 5

Total

18

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository

davila7/claude-code-templates

Path

cli-tool/components/skills/ai-research/computer-use-agents/SKILL.md

Reviewed

Table of Contents

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