SKILLEMALL.ai

AC multi-angle-thinking

A deep thinking engine that first gathers real global data via web search, then analyzes through 11 lenses (philosophical, ethical, practical, historical, psychological, systems, financial, technological, competitive, environmental, counterfactual), then builds a full realistic simulation with real characters — delivering narrative + findings + real surprises. Use this skill whenever the user wants to think deeply, explore multiple angles, challenge assumptions, surface hidden biases, uncover counterintuitive conclusions, or simulate what would really happen. Trigger on: "analyze this", "think deeply about", "challenge my thinking", "what am I missing", "simulate this", "what would really happen", "فكر معي في", "حلل هذا", "ما الزوايا التي لم أفكر فيها", "ما النتائج غير المتوقعة", "جرب هذا", "ماذا سيحدث لو", or any complex idea needing deep analysis. Only invoke on explicit user request — do not self-trigger on simple or conversational queries.

ClawHub Agent Skills author: hasanen groof v1.0.1 MIT-0 9 files · 1 script body ≈ 2 030 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
50/100
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: 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 50/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (multi-angle-thinking) differs from the folder (thinking-engine)
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 40 steps
    • 100Execution cost. Instruction body is 2030 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 14 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 957: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -218 emoji in the instructions: noise for the model
    • -42 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 12 example trigger phrases
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 40 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

    External checks

    ClawHub: clean
    This skill is a disclosed deep-analysis helper with optional reminder hooks; its main risk is over-triggering web-search-based analysis, not hidden malware or data theft.
    LLM: benign (high) · VirusTotal: · 29 May 2026