SKILLEMALL.ai

BA what-if-scenario-builder

Structured methodology for constructing rigorous "what if?" scenarios combining foresight frameworks (Shell, Schwartz, Manoa, Intuitive Logics), deep analytical tools (CLA, Cross-Impact, Morphological, Counterfactual History, Futures Wheels, Analogy Search), stress-testing techniques (Pre-Mortem, Red Teaming, Base Rate Check), and quantitative patterns (Monte Carlo, Agent-Based, Backcasting). Includes Divergence Depth selection, Temporal Cascade framing, and Agency Uncertainty principles. Use whenever the user asks "what if?", wants to explore hypotheticals, build scenarios, analyze alternatives, stress-test plans, or imagine how things could be different. Also trigger on: scenario planning, contingency analysis, alternative futures, speculative reasoning, "imagine if", "suppose that", thought experiments, futurecasting, counterfactual — even without explicit "what if."

ClawHub Agent Skills author: DarkD v2.3.0 MIT-0 1 file body ≈ 19 055 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process A 86/100 · Runs to the end — weak spots: execution cost, progress reporting

ProcedureResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
95
Quality 40%
77
Run on models
none yet
Process rating
A
86/100
Runs to the end
Progress reporting w 2
0
Execution cost w 6
10
When it triggers w 12
70
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 5

✓ No critical or high findings

Medium and low: 5
  • low Risky intent intent-offensive-security SKILL.md:7
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    (Pre-Mortem, Red Teaming, Base Rate Check), and quantitative patterns (Monte Carlo, Agent-Based,
  • low Risky intent intent-offensive-security SKILL.md:806
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    #### 15. Red Teaming / Adversarial Thinking
  • low Risky intent intent-offensive-security SKILL.md:816
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    Red teaming is not cynicism — it is intellectual hygiene. A scenario that survives aggressive red
  • low Risky intent intent-offensive-security SKILL.md:823
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    Red Team challenges:
  • low Risky intent intent-offensive-security SKILL.md:839
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    Red Team conclusion: The scenario is plausible but depends on three things breaking right

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 19055 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 86/100

  • 0Progress reporting. Says nothing while it works
  • 10Execution cost. Instruction body is 19055 tokens: crowds the task out of the window
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 209 steps
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 19 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 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

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 882: 120–800 characters recommended
  • -221 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +4Structure: 39 headings
  • +3Step-by-step instructions: 209 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)

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

External checks

ClawHub: clean
An instruction-only scenario-building methodology whose requested resources and runtime instructions align with its stated purpose and do not ask for extra credentials or system access.
LLM: benign (high) · VirusTotal: · 19 May 2026