SKILLEMALL.ai

AC surgical-contributor

Deliver small, high-signal contributions by finding and fixing one real pain point with the narrowest safe change, adding regression protection, and writing a maintainer-friendly PR summary. Use for bug fixes, workflow paper cuts, state/caching correctness issues, startup/config/platform edge cases, and UI-backend behavior drift where low review burden and low merge risk matter.

ClawHub Agent Skills author: Andy Arijs v1.0.2 MIT-0 4 files body ≈ 1 161 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmentData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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: 4. 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 58/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 85Steps. 75 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1161 tokens
    • low 12 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 381: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 75 items
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This is a straightforward workflow skill for making small, tested repository fixes, with no evidence of hidden execution, credential access, persistence, or data exfiltration.
    LLM: benign (high) · VirusTotal: · 29 May 2026