SKILLEMALL.ai

AC clawrank

Agent performance scoring system for OpenClaw agents. 7 dimensions scored 0-10, crab-themed tiers, evidence-based, with trajectory tracking. Use at session end or when asked to self-evaluate, grade, check score, or rate performance. Integrates with agent-sync for peer review. Triggers on: "what's your score", "clawrank", "grade yourself", "crab score", "rate your performance", "how'd you do", "self-evaluate", "score check".

ClawHub Agent Skills author: JoeyCacciatore3 v1.0.0 MIT-0 2 files body ≈ 1 101 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 60/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, consistency

IntegrationAI 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
60/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
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 60/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 40Consistency. Frontmatter name (clawrank) differs from the folder (nate-clawrank)
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 40 steps
    • 100Execution cost. Instruction body is 1101 tokens

    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 8 example trigger phrases
    • +3Description length 427: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 40 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)

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

    External checks

    ClawHub: clean
    This is a markdown-only self-scoring rubric; its main risk is accidental activation or optional score tracking, not harmful system access.
    LLM: benign (high) · VirusTotal: · 29 May 2026