AD wq-challenge-gold-week
Use to compete in the WorldQuant BRAIN Challenge and climb to GOLD (10000) with AI assistance: sign-up, discovering accessible fields/operators via API, constructing alpha factors (fundamental x analyst-expectation x long-window, rank blends), passing Sharpe>=1.25 / Fitness>=1.0 / self-correlation<0.7 gates, and a daily submission harvest loop. Also for 中文 requests about WorldQuant BRAIN 因子挖掘、Challenge 冲金/GOLD、AI 辅助量化、alpha 提交拿分、Sharpe/Fitness/turnover 调优。
Use to compete in the WorldQuant BRAIN Challenge and climb to GOLD (10000) with AI assistance: sign-up, discovering accessible fields/operators via API…
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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: 0. 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 46/100
- 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
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 8 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 940 tokens
- 100Running it twice. No mutating operations
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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 460: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 8 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.