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CD detox-skill

Generates Detox E2E tests for React Native apps in JavaScript. Gray-box testing framework with automatic synchronization. Supports local simulators/emulators and TestMu AI cloud. Use when user mentions "Detox", "React Native test", "element(by.id())", "device.launchApp". Triggers on: "Detox", "React Native E2E", "React Native test", "element(by.id)", "device.launchApp".

LambdaTest/agent-skills Agent Skills author: LambdaTest 3 files body ≈ 1 856 tokens Open the sourcegithub.com↗ analyzed 34 h ago

Generates Detox E2E tests for React Native apps in JavaScript.

As a process D 44/100 · Unfinished process — weak spots: steps, result and completion, inputs and preconditions

GeneratorSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
84/100
safety, quality, tests
Safety 60%
80
Quality 40%
90
Run on models
none yet
Process rating
D
44/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 4

    ✓ No critical or high findings

    Medium and low: 4
    • medium Exfiltration net-credential-use SKILL.md:159
      Credential used in a network call (verify the destination is the intended service)
      curl -u "$LT_USERNAME:$LT_ACCESS_KEY" \
    • medium Exfiltration net-credential-use SKILL.md:166
      Credential used in a network call (verify the destination is the intended service)
      curl -u "$LT_USERNAME:$LT_ACCESS_KEY" \
    • medium Exfiltration net-credential-use SKILL.md:193
      Credential used in a network call (verify the destination is the intended service)
      curl -u "$LT_USERNAME:$LT_ACCESS_KEY" \
    • medium Exfiltration net-credential-use SKILL.md:198
      Credential used in a network call (verify the destination is the intended service)
      curl -u "$LT_USERNAME:$LT_ACCESS_KEY" \

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "languages"

    Process rating: all ten parameters 44/100

    • 0Steps. Prose only: no discrete steps
    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1856 tokens
    • 100Running it twice. No mutating operations

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 372: enough signal without eating the budget
    • +4Structure: 16 headings
    • +4Has examples (10 code blocks)
    • +4Reference files are cited in the instructions (1 of 2)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.