SKILLEMALL.ai

AB brainforge-autoresearch

Use when user wants to optimize, improve, benchmark, or evaluate a skill's prompt. Triggers on "optimize skill", "improve skill prompt", "benchmark skill", "eval skill", "run autoresearch", "tune prompt", "prompt optimization", "skill evaluation", "A/B test prompt", "find best prompt", "auto-improve skill". Runs automated prompt experiments using the Karpathy autoresearch pattern.

ClawHub Agent Skills author: ZHANG Ning v0.2.5 MIT-0 9 files body ≈ 1 821 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 71/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

AnalyzerGitHubAI 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%
93
Run on models
none yet
Process rating
B
71/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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: 9. 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 71/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 16 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1821 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

    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 11 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 383: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: clean
    This skill does what it claims: it uses LLM APIs to test and rewrite skill prompts, with local result files and backups, but users should treat runs as sending prompt data to a provider and modifying the target prompt file.
    LLM: benign (high) · VirusTotal: · 29 May 2026