AC trade-validation
10-dimension weighted scoring framework for prediction market trade evaluation. Enforces disciplined position sizing, circuit breakers, and mandatory counter-arguments. Use when: evaluating prediction market trades, scoring opportunities, deciding position sizes, comparing Polymarket/Kalshi opportunities, running pre-trade checklists. Don't use when: general crypto analysis, DeFi yield farming, non-prediction-market investments, stock/equity analysis, sports betting (different framework needed). Negative examples: - "Should I buy ETH?" → No. This is for prediction markets with binary/discrete outcomes. - "What's the best DeFi yield?" → No. Wrong domain entirely. - "Score this sports bet" → No. Sports betting has different dimensions (injuries, matchups). Edge cases: - Crypto prediction markets (e.g., "Will BTC hit $X?") → YES, use this if on Polymarket/Kalshi. - Multi-outcome markets → Score each outcome separately. - Markets with <$25 liquidity → Auto-fail on Liquidity dimension.
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- 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: 3. 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 59/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 50When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 12 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1139 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
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
- +3Description length 999: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 4 example trigger phrases
- +4Description says when NOT to use the skill
- +4Structure: 11 headings
- +3Step-by-step instructions: 12 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.