SKILLEMALL.ai

AC incentive-design

Activate when: user asks why a team keeps doing the wrong thing despite training; user is designing compensation, bonuses, or commissions; user says 'people are gaming the metric' or 'our OKRs aren't working'; user wants to fix a performance management system; user asks what incentives are driving a behavior; user is drafting contracts or platform rules to shape behavior. Do NOT activate when: the situation is clearly individual misconduct with no systemic pattern; user wants psychological persuasion tactics rather than structural system design. More: deciqai.com/c/incentive-design

ClawHub Agent Skills author: deciqAI v1.0.5 MIT-0 5 files body ≈ 1 763 tokens Open the sourceclawhub.ai analyzed 2 d ago

Activate when: user asks why a team keeps doing the wrong thing despite training; user is designing compensation, bonuses, or commissions; user says 'people…

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
95
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
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: 5. 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 63/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 28 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1763 tokens
    • low 10 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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 588: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 28 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.

    External checks

    ClawHub: clean
    This is a text-only thinking skill for analyzing incentive systems, with no hidden execution, credential use, or persistence behavior.
    LLM: benign (high) · VirusTotal: · 16 Jul 2026