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AB ahrefs

Complete Ahrefs API integration for SEO analysis. Covers Site Explorer (domains, backlinks, rankings), Keywords Explorer (search volume, difficulty, SERP analysis), Rank Tracker (position monitoring), Site Audit (technical SEO), SERP Overview, Batch Analysis, and Brand Radar. Use for any SEO-related queries including keyword research, backlink analysis, competitor intelligence, technical audits, and rank tracking.

modbender/skill-library-mcp Agent Skills author: modbender MIT 14 files body ≈ 2 860 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Complete Ahrefs API integration for SEO analysis.

As a process B 65/100 · Nearly there — weak spots: when it triggers, progress reporting

IntegrationMarketingtype 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
96/100
safety, quality, tests
Safety 60%
99
Quality 40%
91
Run on models
none yet
Process rating
B
65/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Failures and branches w 10
50
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration read-dotenv SKILL.md:349
      Reads a .env file (documentation of a security skill)
      $env:AHREFS_API_TOKEN = (Get-Content ~/.openclaw/workspace/.env -Raw | Select-String "AHREFS_API_TOKEN=([^\r\n]+)" | ForEach-Object { $_.Matches.Groups[1].Value })
      security skill

    Files scanned: 14. 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 65/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 122 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2860 tokens
    • 100Running it twice. No mutating operations
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 417: enough signal without eating the budget
    • +4Structure: 49 headings
    • +3Step-by-step instructions: 122 items
    • +3Output format is stated explicitly
    • +4Has examples (21 code blocks)
    • +4Reference files are cited in the instructions (1 of 7)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.