Results for “promoter-prediction”

Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates with evals.

Promote project-specific memories into strict, small global memory only when they are cross-project, stable, useful, safe, and worth the global context cost. Load when the user says make this global, remember across projects, save this globally, or promote this learning.

Graduate a proven pattern from auto-memory (MEMORY.md) to CLAUDE.md or .claude/rules/ for permanent enforcement.

Use when making a forward-looking claim with a checkable outcome (reply within 24h, error rate will drop, this skill will see more use) — record to state/predictions.jsonl with a review horizon so reflection can grade you later. Closes the in-the-moment double-loop.

Agent profile for PPC and paid-growth risk review before changing ads, budgets, bidding, tracking, or landing pages.

Compares prediction market probabilities from Polymarket with options-implied probabilities to identify mispricing signals and potential arbitrage opportunities.

Walk into the negotiation with a plan, not a hope. Sets your walk-away and your target, builds the concession ladder (what you trade and what you get for it), maps their likely asks, and drafts the responses that protect price and terms. Built for B2B sales teams, customizable to your CRM and your deal process. Trigger on "prep me for this negotiation", "the prospect wants a discount", "what should I concede", "build my concession ladder", "hold price on a new deal", or any pre-negotiation planning.

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.

Build predictive models for sports and event outcomes using statistical methods, ELO ratings, regression, Monte Carlo simulation, and machine learning. Use when creating power rankings, projecting game outcomes, estimating win probabilities, or building a quantitative edge. Also trigger for 'prediction model', 'ELO rating', 'power rankings', 'win probability', 'Monte Carlo', 'regression model', 'expected goals', or 'predictive analytics'.

Extract warm prospects from competitor and industry post engagers across LinkedIn, Twitter, and blogs

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

This skill should be used when building prediction market integrations, implementing betting functionality, or querying Polymarket odds. Covers market search, odds checking, and position management.

Use to define what actually predicts support performance in your organisation and build a hiring profile around validated predictors — structured work samples, not vibes. Trigger for "hiring profile", "what should we look for in support hires", "interview scorecard for agents", "reduce bad hires", "work sample test", screening criteria review, or checking whether your interview process predicts ramp and QA.

Trains language models with Direct Preference Optimization using preference pairs, covering DPOTrainer setup, dataset preparation, and beta tuning for stable preference learning without explicit reward models.

Takes a conference or event speakers URL, extracts the people, filters their companies against the user's ICP, and deep-researches only the speakers at ICP-fit companies. Outputs a person-first HTML report with a 'why reach out' rationale per person.

Quantifies market top probability using O'Neil Distribution Days, Minervini leading stock deterioration, and Monty defensive sector rotation, producing a 0-100 composite score with risk zone classification for tactical 2-8 week timing signals.

Revenue forecast from pipeline data with stage-weighted probability and sensitivity analysis

Predicts enhancer activity in DNA sequences using the hosted Genomic Intelligence G0 DeepSTARR model, returning per-window activity scores.

Build a quarterly bookings forecast with commit, best-case, and pipe-only tiers, project cohort-level NRR/GRR to surface leaky cohorts, and score per-stage funnel confidence using coefficient-of-variation analysis.

Generates at most one token recommendation and one prediction-market pick per run, each with a falsifiable thesis, entry, sizing, and kill criterion. Returns NO_PICK when no candidate meets the bar.

Evaluate industry dynamics and market attractiveness by analyzing competitive rivalry, supplier power, buyer power, threat of substitutes, and threat of new entrants.

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.

Build, review, or restructure a fundraising deck for pre-seed through Series A, with a clear narrative arc, investor-grade slide logic, and explicit asks.

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'.

Sports betting fundamentals including spreads, moneylines, totals, parlays, teasers, props, and futures. Use when analyzing sports betting markets, converting odds formats, evaluating bet types, or building a sports betting strategy. Also trigger for 'point spread', 'moneyline', 'over under', 'parlay', 'prop bet', 'teaser', 'sports odds', or 'sportsbook'.

Creates a presenter-mode deck with speaker notes, theme switching, and a popup teleprompter.

Build a scalable outbound B2B sales machine with specialized SDR, AE, and CSM roles, using Cold Calling 2.0, referral emails, ANUM qualification, and pipeline math to generate predictable revenue.

Prediction markets data - Polymarket, Kalshi markets, prices, positions, and trades. Use when: the user needs market data, stock analysis, watchlists, or portfolio workflows.

Use when planning a POPL campaign calendar — backward-scheduling theory and mechanization from the July deadline, riding the October notification into the conditional-acceptance revision and artifact evaluation, landing the January conference, and retargeting across the SIGPLAN/PACMPL deadline wheel if July slips.

Queries prediction market data from Polymarket's public API and provides market analysis with risk management guidance.

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

The instruction to profile before optimizing survives because intuition about

Off-page playbook (Lesson 9, link building). Mines our competitors' backlink profiles via Ahrefs to produce a VETTED link-prospect list — their "superfans" (sites linking to several of a competitor's best-by-links pages), their "power-linkers" (sites linking to multiple competitors but NOT to us), and their fresh new links — filtered for relevance and de-duped against sites that already link to us. The Link Intersect method, encoded. Output is a prospect queue for /outreach; it does NOT send anything.

Manages a B2B sales pipeline within 1ai-social: tracks leads through stages, qualifies with BANT, generates proposals, schedules follow-ups, and produces daily analytics.

v4.1.0 reward function redesign to fix overtrading and DSR dominance