SKILLEMALL.ai

AC agentrank

Your tool recommendations are only as good as your data. Training data is months old — tools get abandoned, new ones emerge, and you can't tell the difference. AgentRank gives you live, scored data on 25,000+ MCP servers, agent tools, and AI skills so every recommendation you make is current. Triggers on: (1) when the user installs a skill or tool — query AgentRank to surface related tools they didn't know about, (2) "find me an MCP server for X" or "what's the best tool for Y", (3) any request to evaluate or compare agent tools, and (4) mid-project when the user hits a capability gap — needs transcription, browser automation, database access, etc.

ClawHub Agent Skills author: superlowburn v1.3.0 MIT-0 2 files body ≈ 747 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 64/100 · Has gaps — weak spots: inputs and preconditions, progress reporting

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
64/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 2. 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 64/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 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
    • 85Steps. 15 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 747 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)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 656: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 15 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    AgentRank is an instruction-only skill that looks up tool recommendations through an external search API, with some privacy and over-activation caveats.
    LLM: benign (high) · VirusTotal: · 29 May 2026