Results for “pairwise-ranking”

Find adjacent vendors and subs (not top market primes) who fill a capability gap against a displacement target using USASpending flows and SAM entity signals. Use when user defines a teaming gap and wants vault-ready partner shortlist with citations.

Identifies and analyzes statistical arbitrage opportunities through pair trading, using correlation analysis, cointegration testing, and z-score calculations to generate entry/exit signals for market-neutral strategies.

Evaluates automated metrics and vision-language models on identifying granular errors in detailed image descriptions and ranking paired descriptions against human judgments, using macro F1, pairwise accuracy, Spearman rank ρ, and Kendall's τ.

Turn a pile of accounts into a stack-ranked priority list with a reason on every row. A layered score (gates first, then an evidence-weighted base rank over the signals you actually have, then bounded boosts for product usage and buyer intent) that stays fair across channels and never scores a blank field as a zero. Built for B2B GTM teams, customizable to your signals and your ICP. Trigger on "score these accounts", "rank by fit", "composite ICP score", "stack-rank my list", "who should I work first", "prioritize these leads", or any multi-signal account qualification.

NLVR2: A Visual Reasoning Benchmark for Natural Language

Ranks mutual connections by bridge value for warm introductions, using a weighted graph model with decay and engagement bonuses.

Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.

Benchmarks language model agents on the C2C multi-agent negotiation task, reporting win rate across starting positions.

Quantifies the minimum accuracy gap needed between two models for a sampled micro-benchmark to reliably preserve their ranking, using the MDAD metric from Yauney et al. (2025).

Price-to-win lens using GSA CALC+, BLS OEWS, and incumbent USASpending award patterns for a pursuit. Use when user asks for realism checks or competitive pricing posture before proposal — draft skill, not production-verified.

Scores generated image captions against reference captions and source images using the Polos metric, which is trained to align with human judgments and probes hallucination robustness and open-vocabulary evaluation.

Evaluates prospective partnerships by classifying partner tier, designing a 90-day joint GTM plan, modeling revshare economics, and surfacing kill criteria for under-performing partnerships.

Prioritize product work with explicit criteria, scoring, tradeoffs, and decision rationale.

Reranking retrieved documents with cross-encoders and LLM rerankers. Cohere Rerank v3, Voyage rerank-2, BGE reranker, ColBERT late interaction, Jina reranker. Cost and latency tradeoffs, top-K in / top-N out strategy. USE WHEN: user mentions "rerank", "reranker", "cross-encoder", "Cohere Rerank", "Voyage rerank", "BGE reranker", "ColBERT", "Jina reranker", "bi-encoder" DO NOT USE FOR: initial retrieval - use `advanced-retrieval` or `hybrid-search`; query rewriting - use `query-transformations`; agent decisions - use `agentic-rag`

Pricing models, tier packaging, and willingness-to-pay research. Use when the user requests 'pricing tiers', 'freemium', 'value metric', 'pricing page', 'willing to pay', or 'van Westendorp'.

Provides a dashboard template for community/dating metrics with a navigation rail, ticker bar, headline KPIs, 30-day mutual-matches bar chart, and match-rate trend block.

Use to design VIP or tiered support programmes and to test whether higher tiers actually deliver better outcomes. Trigger for "VIP support programme", "service tiering", queue priority for premium customers, entitlement vs priority, tier inflation, or "does our enterprise tier get faster support".

Scores competitors across nine weighted dimensions with explicit 1–5 rubrics and a tension plot, producing comparable profile cards for competitive analysis.

Use to compare BPO sites, vendors or partner teams fairly, adjusting for the work mix each is given before concluding anything about performance. Trigger for "compare our BPO sites", "which vendor is performing best", "site A scores lower than site B", outsourcer QBR packs, partner MI reporting, or setting contractual quality targets with a vendor.

DataFrame operations with pandas and polars — groupby, joins, reshaping, performance. Use when manipulating tabular data, choosing between pandas and polars, optimizing DataFrame code, or translating between the two libraries.

AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.

Build a USASpending relationship snapshot for the incumbent and buying agency on a pursuit row. Use when user needs award flows and rel counts before battlecard or teaming work — deterministic from DuckDB bulk.

通过 AIsa API 发现 Polymarket 和 Kalshi 预测市场的套利机会。扫描体育市场跨平台价差、比较实时赔率、验证订单簿流动性。适用场景：预测市场套利、跨平台价差、体育博彩套利、赔率对比、无风险利润、市场低效。 Use when: the user needs market data, stock analysis, watchlists, or portfolio workflows.

Converte diagnóstico de desempenho em plano de ação priorizado por tiers, com confiança declarada e foco em uma recomendação por vez.

Establishes a two-party working posture where the user directs and the agent executes, surfacing every genuine fork as a batched single-select question with a recommended default.

通过 AIsa API 查询跨平台预测市场数据。支持 Polymarket 和 Kalshi 的市场行情、价格、订单簿、K线、持仓和交易记录。适用场景：查询预测市场赔率、选举博彩、事件概率、市场情绪、Polymarket 价格、Kalshi 价格、体育博彩赔率、钱包盈亏、跨平台市场对比。 Use when: the user needs market data, stock analysis, watchlists, or portfolio workflows.

Score LLM and agent outputs using LLM-as-judge techniques — direct scoring against rubrics or pairwise comparison between two outputs. Includes built-in bias mitigation for position bias, length bias, and self-enhancement bias. Load when the user asks to score an output, judge a response, evaluate against a rubric, compare two outputs, do direct scoring, run pairwise comparison, or says "rate this", "which response is better", "score this against the rubric", "judge this output", "LLM as judge this". Sub-skill of eval-output orchestrator.

Systematically evaluates alternatives against weighted criteria, builds a comparison matrix, and recommends the best option with documented rationale and trade-offs.

Rank a backlog of feature ideas by impact, effort, risk, and strategic alignment to identify the top 5 to pursue.

Use to find where support routing is failing — misrouted contacts, reassignment chains, unassigned work, and queues nobody owns. Trigger for "why do tickets bounce between teams", "are we routing correctly", "tickets sitting unassigned", "how many tickets get reassigned", ticket ping-pong, or reviewing routing rules and queue design.

Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".

Analyze URLs or text to discover ranked Polymarket, Hyperliquid, and Deribit markets with Quotient edge scores, paid via on-chain x402 settlement.

Correlate findings across OSINT sources—username enumeration, email lookups, social media profiles, domain records, breach databases, and dark-web mentions—into unified intelligence profiles with confidence scoring and link analysis.

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.

Live Upwork market research producing a ranked niche-opportunity dashboard. Use when the user wants to scan the Upwork market, find or validate a freelance niche, asks 'is there demand for X', wants competitor profile analysis, or needs rate observations for a niche. Triggers: 'research my niche', 'scan the Upwork market', 'is there demand', 'what should I specialize in', 'check the market', 'what do freelancers charge for X'.