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AC Job Search MCP

Search for jobs across LinkedIn, Indeed, Glassdoor, ZipRecruiter, Google Jobs, Bayt, Naukri, and BDJobs using the JobSpy MCP server.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 4 000 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Search for jobs across LinkedIn, Indeed, Glassdoor, ZipRecruiter, Google Jobs, Bayt, Naukri, and BDJobs using the JobSpy MCP server.

As a process C 58/100 · Has gaps — weak spots: result and completion, consistency, progress reporting

ProcedureAI and agentstype 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Consistency w 8
40
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: skill-library-mcp

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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "slug"

    Process rating: all ten parameters 58/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (Job Search MCP) differs from the folder (job-search-mcp)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 62 steps, 1 vague phrases
    • 100Execution cost. Instruction body is 4000 tokens
    • 100Running it twice. Mutating operations check current state
    • low 12 top-level sections: this looks like several domains in one skill

    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)
    • +3Output format is not stated: the model decides each time
    • -213 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 132: enough signal without eating the budget
    • +4Structure: 41 headings
    • +3Step-by-step instructions: 62 items
    • +4Has examples (23 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.