SKILLEMALL.ai

AA canvas-debate

Adversarial debate between a YC Partner (challenger) and a Business Strategist (defender) to stress-test and improve a business model. Uses two independent subagents with separated contexts to reduce confirmation bias. Produces a battle-tested canvas + debate report. Use when asked to "debate my business model", "博弈分析", "red team my canvas", "stress test my business", "对抗分析", "挑战我的商业模式", or when the user wants a more rigorous business model evaluation than a single-pass canvas.

ClawHub Agent Skills author: qianen6 v1.1.0 MIT-0 2 files body ≈ 3 851 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process A 83/100 · Runs to the end — weak spots: progress reporting

AnalyzerData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
98
Quality 40%
92
Run on models
none yet
Process rating
A
83/100
Runs to the end
Progress reporting w 2
0
Result and completion w 14
60
When it triggers w 12
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Risky intent intent-offensive-security SKILL.md:8
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
      Use when asked to "debate my business model", "博弈分析", "red team my canvas",
      quoted
    • low Risky intent intent-offensive-security SKILL.md:35
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | **Red Team** | Up to 10 rounds, full convergence | Entering new markets, high-stakes decisions |

    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 83/100

    • 0Progress reporting. Says nothing while it works
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Failures and branches. 6 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 47 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3851 tokens
    • 100Running it twice. Mutating operations check current state
    • 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)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 482: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 47 items
    • +3Output format is stated explicitly
    • +4Has examples (8 code blocks)

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

    External checks

    ClawHub: clean
    This skill is a disclosed business-model debate workflow that reads project context and writes expected report files, with no evidence of hidden execution or data exfiltration.
    LLM: benign (high) · VirusTotal: · 29 May 2026