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AB linkedin-automation-login

LinkedIn job-sprint automation — verify/refresh login in the dedicated Playwright profile, then run the Easy Apply automation. Use when resuming or starting a LinkedIn application sprint.

ClawHub Agent Skills author: Umer Khalid v1.0.11 MIT-0 3 files body ≈ 2 076 tokens Open the sourceclawhub.ai↗ analyzed 12 h ago

LinkedIn job-sprint automation — verify/refresh login in the dedicated Playwright profile, then run the Easy Apply automation.

As a process B 70/100 · Nearly there — weak spots: consistency, running it twice

AnalyzerPlaywrightOperations and projectsSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
70/100
Nearly there
Running it twice w 4
30
Consistency w 8
40
Tools and files w 18
60
the three weakest of ten parameters · all ten

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 · 0

    ✓ No critical or high findings

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

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 70/100

    • 30Running it twice. 7 mutating operations with no state check
    • 40Consistency. Frontmatter name (linkedin-automation-login) differs from the folder (linkedin-easy-apply)
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 15 steps, 2 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Execution cost. Instruction body is 2076 tokens
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (7 tags): a typed call is more reliable

    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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 187: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 15 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    The skill is transparent about automating LinkedIn applications, but it depends on a separately installed runtime that can act through your logged-in LinkedIn session and is not included in the reviewed package.
    LLM: suspicious (medium) · 16 Sept 2026