SKILLEMALL.ai

AA weiliaozi-skill

Structured analysis using the Wei Liaozi five-lens framework — a synthesis of pre-Qin strategic thought drawing on Huangdi (rational analysis), Jiang Ziya (civil-military governance), Sun Wu (adaptive strategy), Wu Qi (discipline and command), and Duke Huan of Qi (stratified gains). Use when the user needs disciplined reasoning about a question or scenario — including historical analysis, system structure, trade-off assessment, sequence logic, and game-theoretic response mapping. Framework: Essence -> Conditions -> Gains-Losses -> Sequence -> Opponent. Think in order, no skipping steps. Respond in the same language as the user's question.

ClawHub Agent Skills author: Ethan Wilkins v2.2.0 MIT-0 9 files body ≈ 3 938 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process A 89/100 · Runs to the end — weak spots: progress reporting

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
A
89/100
Runs to the end
Progress reporting w 2
0
Result and completion w 14
60
Inputs and preconditions w 11
70
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

    • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Structured analysis using the Wei Liaozi five-lens framework — a s… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
    • note frontmatter-key unknown frontmatter key "language"
    • note frontmatter-key unknown frontmatter key "run_as"
    • note frontmatter-key unknown frontmatter key "allowed_tools"

    Process rating: all ten parameters 89/100

    • 0Progress reporting. Says nothing while it works
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 127 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 7 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3938 tokens
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 15 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)
    • +4No input/output examples
    • +2Single-language instructions
    • +3Description length 646: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 127 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (2 of 2)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

    External checks

    ClawHub: clean
    The available evidence shows a prompt-only dual-use analysis concern, but no artifact-backed evidence of hidden execution, data theft, persistence, or destructive behavior.
    LLM: benign (medium) · VirusTotal: · 4 Jul 2026