SKILLEMALL.ai

AC game-theory-strategist

Analyze strategic interactions and calculate optimal decision paths using Game Theory. Trigger on: strategic planning, negotiation tactics, pricing wars, competitive analysis, conflict of interest, or any scenario where outcomes depend on multiple agents. Also trigger for Nash Equilibrium, dominant strategies, backward induction, Pareto optimality, mechanism design, Bayesian games, Prisoner's Dilemma, salary negotiation, co-founder disputes, inheritance conflicts, career pivots, household coordination. If someone asks what should I do knowing my competitor will react or how do I negotiate or I have a conflict - use this skill. Produces dark-themed visual analysis: payoff matrix, Nash equilibrium, optimal strategy, action phases, strategic verdict.

ClawHub Agent Skills author: David Escobar v1.0.1 MIT-0 7 files body ≈ 1 306 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 64/100 · Has gaps — weak spots: inputs and preconditions, running it twice, progress reporting

AnalyzerSales and CRMtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
C
64/100
Has gaps
Inputs and preconditions w 11
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
    • 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: 6. 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 64/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 65Failures and branches. 3 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 41 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1306 tokens
    • low No test case covers injection arriving through data

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 757: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 41 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: clean
    This is a coherent game-theory coaching skill with local-only helper scripts, but users should treat its negotiation advice as heuristic and potentially sensitive in personal or high-stakes disputes.
    LLM: benign (high) · 28 May 2026