Results for “win-rate”

10 skills
More results
enuno
Wolf Howl
Runs a nightly automated retrospective on autonomous trading strategy performance, computing win rates, fee drag, holding period buckets, direction bias, and producing data-driven improvement suggestions.
1 · bundle
rulebase-co
Cx Incentive Design
Use to design support incentives that improve behaviour without destroying the metric — pairing pay with guardrails, naming gaming modes, and choosing measures that survive Goodhart pressure. Trigger for "incentive plan", "agent bonus scheme", "SPIFF design", "pay for QA score", "what metric should we bonus", CSAT incentives, or reviewing whether a comp change is driving gaming.
1
smith6jt-cop
Agent Validation V420
Agent validation overhaul: reward weight overrides, fitness decline gate, pinned data, staged experiments
3
bdm-15
Competitive Battlecard
Produce displace/team/ghost talk tracks for the incumbent on a recompete pursuit. Use when user wants competitive angles saved to the pursuit vault; optional multi-turn LLM for customer-facing phrasing.
0
curiositech
Windags Evaluator
Two-stage review engine with four-layer quality model for the WinDAGs meta-DAG. Receives completed node outputs and produces ReviewResult containing QualityVector. Stage 1 (Haiku) checks Floor + Wall on every node. Stage 2 (Sonnet) runs Ceiling evaluation conditionally using economic escalation formula. Enforces BC-EVAL-001 through BC-EVAL-006. Activate when operating as the Evaluator role in the meta-DAG, when reviewing node outputs, when computing quality vectors, or when deciding Stage 2 escalation.
10
builderio
Stay Within Limits
Keep long-running agent work within 5-hour and weekly usage limits by checking usage between waves, pausing near the cap, and resuming only when the window is clear.
3.4k · bundle
pymodel
Pythinker Datasource
Universal data-source assistant. Use this skill when the user wants external structured data such as stocks, financial reports, technical indicators, A-share/HK/US markets, global macroeconomics, Chinese enterprise registry information, arXiv papers, Google Scholar results, Chinese laws/regulations and judicial cases, Wind financial data (intraday/minute quotes, funds, bonds), IMF macro datasets (FX rates, CPI, GDP forecasts), Gildata smart screening, US SEC filings (10-K/10-Q, Form 4, 13F), or S&P Capital IQ fundamentals (top holders, consensus estimates, valuation ratios). This plugin exposes tools via MCP server `plugin-pythinker-datasource_data`; call them in the flow `mcp__plugin-pythinker-datasource_data__get_data_source_desc` → `mcp__plugin-pythinker-datasource_data__call_data_source_tool`.
14 · bundle