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AB jd-interview-prep-helper

Help users prepare for job interviews by analyzing job descriptions and company information. Use this skill when the user wants to prepare for an interview, provides a job description (JD) and company name, or mentions interview preparation. Make sure to use this skill whenever the user asks about interview prep, mentions a specific company and JD, or wants to prepare for a technical interview.

ClawHub Agent Skills author: 史纯涛 v1.0.0 MIT-0 4 files body ≈ 1 058 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 79/100 · Nearly there — weak spots: running it twice, progress reporting

AnalyzerPeople and hiringInfrastructuretype 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
79/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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 79/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 65 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1058 tokens
    • low 14 top-level sections: this looks like several domains in one skill
    • medium 3 test cases, all positive: not one "should refuse" or "should ask first"

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 397: enough signal without eating the budget
    • +4Structure: 31 headings
    • +3Step-by-step instructions: 65 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)

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

    External checks

    ClawHub: clean
    This is a coherent interview-preparation helper that uses search, reading a provided JD, and saving a markdown guide in ways that match its stated purpose.
    LLM: benign (high) · VirusTotal: · 29 May 2026