Results for “mckinsey-growth-pyramid”

Generates McKinsey-style HTML presentations from marketing content. Use when user requests to 'create a presentation', 'generate a report', or 'build a deck' from marketing strategy or plan content.

Builds narrative-driven HTML slide decks for pitches, briefings, demos, and talks, applying persuasion frameworks and design tokens.

Screen US stocks using William O'Neil's CANSLIM growth stock methodology with composite scoring and weighted components.

Entrena modelos de machine learning con Scikit-learn, LightGBM y XGBoost, desde un baseline hasta un modelo productivo con validación robusta y explicabilidad.

Generate OKR cascades, alignment scores, and strategic plans for product leadership, with templates for growth, retention, revenue, innovation, and operational strategies.

JACKAL — First Jump pyramider. FOX v1.6's exact five-layer entry gauntlet combined with RHINO's pyramiding mechanic. Enter at 30% on a qualifying First Jump, add 40% at +10% ROE and final 30% at +20% ROE — but only after re-validating 4h trend, SM alignment, and volume. Failed scouts cost $15 instead of FOX's $50. Full pyramids capture the same upside. DSL High Water Mode (mandatory).

SQLAlchemy Python SQL toolkit and ORM with powerful query builder, relationship mapping, and database migrations via Alembic

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

RHINO — Momentum pyramider. Top 10 assets by OI + volume. Enters small (30% of max) on high-conviction convergence, then adds to winners at +10% ROE (40% more) and +20% ROE (final 30%). Thesis re-validated before every add — 4h trend intact, SM aligned, volume present. DSL High Water Mode trails the full position. The only skill in the zoo that builds into winners instead of entering full size and hoping.

Generates synthetic body MRI volumes using NVIDIA's NV-Generate-CTMR rflow-mr model. Wraps the upstream diffusion inference pipeline with config staging, output validation, and NIfTI volume summarization.

Build and modify Minecraft server plugins for Paper, Spigot, or Bukkit, covering plugin.yml setup, commands, listeners, schedulers, player state, team/arena systems, persistent progression, economy, configuration, and Adventure text.

当需要在众多机会中筛选出投入产出比最高的选项，以最小化风险并最大化潜在回报时

Delivers measurement-driven performance optimization for latency, throughput, memory, and tail behavior, with correctness preservation and regression guards.

Solve single- and multi-objective optimization problems with NSGA-II/III, MOEA/D, and other evolutionary algorithms, including Pareto front analysis, constraint handling, and benchmarking on standard test problems.

Evaluates US market bubble risk through quantitative data analysis using the Minsky/Kindleberger framework, scoring indicators like Put/Call ratio, VIX, margin debt, breadth, and IPO data.

Solves single- and multi-objective optimization problems with NSGA-II/III, MOEA/D, and other evolutionary algorithms, including constraint handling, Pareto front analysis, and benchmark problems.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Automate multi-turn adversarial conversations against LLM agents using Microsoft PyRIT, including Crescendo and Tree-of-Attacks-with-Pruning (TAP) attack chains with scorer feedback loops.

Expert in designing and optimizing growth loops - self-reinforcing systems where the output of one cycle becomes the input for the next. Covers viral loops, content loops, paid loops, sales loops, and product loops. Knows how to identify, measure, and accelerate the specific loop mechanics that drive sustainable growth. Use when "growth loop, viral loop, content loop, flywheel, compounding growth, acquisition loop, referral loop, " mentioned.

Use for writing, reviewing, debugging, testing, or optimizing Python SymPy symbolic mathematics. Trigger on Symbol, assumptions, Expr, Eq, solve/solveset, simplify, factor, expand, calculus, matrices, exact arithmetic, lambdify, code generation, or symbolic-to-numeric conversion. Do not use for NumPy-only arrays, mpmath-only arbitrary-precision numerics, CVXPY optimization models, or parsing untrusted mathematical text.

Implements Manifold-Constrained Hyper-Connections (mHC) using Doubly Stochastic Matrices to improve deep learning stability.

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

TIGER v2 — Multi-scanner trading system for Hyperliquid perps via Senpi MCP. 5 signal patterns (BB compression breakout, BTC correlation lag, momentum breakout, mean reversion, funding rate arb), DSL v4 trailing stops, goal-based aggression engine, and risk guardrails. Configurable profit target over deadline. 12-cron architecture (10 TIGER + prescreener + ROAR meta-optimizer). Pure Python analysis. Requires Senpi MCP, python3, mcporter CLI, and OpenClaw cron system.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Validate product opportunities, map assumptions, plan discovery sprints, and test problem-solution fit before committing delivery resources.

Synthesize market breadth, regime, and flow signals into a unified capital commitment recommendation with exposure ceiling, bias, and action guidance.

Identifies and designs growth loops (flywheels) for sustainable traction — evaluating 5 loop types: Viral, Usage, Collaboration, User-Generated, and Referral. Use when designing growth mechanisms or reducing reliance on paid acquisition. Triggers on: growth loop, flywheel, viral loop, referral program, product-led growth, PLG, user acquisition, growth strategy, retention loop, compounding growth.

Development with Myriad: tools and best practices

CEO/founder-mode plan review. (gstack)

Build, fit, validate, and compare Bayesian models using PyMC's modern API, including hierarchical models, MCMC sampling, variational inference, posterior predictive checks, and model comparison.

Structured threat modeling skill using the PASTA framework (Process for Attack Simulation and Threat Analysis) combined with Adam Shostack's 4-question framework. Use this skill whenever the user asks to do threat modeling, security analysis, map the attack surface, identify threats, or review an application for security risks — even if they don't mention PASTA or a specific framework by name. Core activities: Component Mapping (architecture + data flows), Critical Assessment (business impact prioritization), and Logic Flaw Identification (attacker mindset on business logic). Produces: component map diagram (Mermaid), data flow diagram (Mermaid), attack tree (Mermaid), STRIDE threat table, prioritized risk register, and an actionable mitigation plan. Invoke proactively for any security review, architecture review, or "what could go wrong?" session.

Design product-led growth loops, activation paths, and expansion mechanics for a software product. Use when the team needs onboarding-to-value planning, sharing or collaboration loops, retention hooks, or realistic post-launch growth motions tied to product usage.

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting mu

Econometrics skill for Synthetic Control Method (SCM). Activates when the user asks about: "synthetic control", "SCM", "synthetic counterfactual", "donor pool", "placebo test", "in-space placebo", "in-time placebo", "MSPE ratio", "Abadie Diamond Hainmueller", "augmented synthetic control", "penalized SCM", "synthetic DID", "合成控制", "合成控制法", "捐助池", "安慰剂检验", "合成反事实", "合成DID", "政策评估"

Onboard a new GTM project — run an agency-style interview, then generate CLAUDE.md, PROGRESS.md, about/me.md, strategy/brand.md, and scaffold content + assets folders. Use this skill whenever the user wants to set up a new project, onboard a brand, start from scratch, configure their marketing context, or initialize their workspace. Trigger on: bootstrap, onboard, set up my project, get started, configure my brand, initialize, new project setup, brand onboarding, set up my brand, create project structure, onboard my company, set up content folders, prepare my workspace, start a new GTM project. Also use when the user seems to be starting fresh and hasn't set up strategy/brand.md or about/me.md yet.