Results for “probabilistic-programming”

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Modelagem Bayesiana com PyMC. Construa modelos hierárquicos, MCMC (NUTS), inferência variacional, comparação LOO/WAIC, verificações posteriores, para programação probabilística e inferência.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

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

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

Bayesian modeling and probabilistic programming with PyMC — hierarchical models, MCMC (NUTS) sampling, variational inference, LOO/WAIC model comparison, and posterior predictive checks. Use when fitting Bayesian or hierarchical models, estimating posteriors and credible intervals, running probabilistic inference, or comparing models with LOO/WAIC. Part of the AlterLab Academic Skills suite.

A* pathfinding skill for heuristics and optimization.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Build and improve AI or probabilistic software through evaluation-driven development. Use for LLM applications, agents, prompts, RAG, tool use, classifiers, model migrations, quality regressions, golden datasets, LLM-as-judge rubrics, benchmarks, or requests to add evals and measurable release gates. Pair with TDD for deterministic code; do not use as the primary guide for ordinary unit testing without model behavior.

Write efficient C code with proper memory management, pointer arithmetic, and system calls. Handles embedded systems, kernel modules, and performance-critical code. Use PROACTIVELY for C optimization, memory issues, or system programming.

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.

The instruction to profile before optimizing survives because intuition about

A library of reusable, production-tested prompt engineering patterns for building AI-powered features. Use when designing system prompts for apps, building AI pipelines, selecting the right prompting technique for a use case, or reviewing prompts for common failure modes. Complements the prompt-engineering skill (which covers the optimization framework); this skill covers the pattern library itself.

Use for writing, reviewing, debugging, testing, or diagnosing Python Bayesian models built directly with PyMC, including Model, coords/dims, Data, random variables, potentials, posterior sampling, prior/posterior predictive checks, and InferenceData output. Trigger on model geometry, shape errors, divergences, sampler choice, mutable prediction data, and probabilistic validation. Do not use for Bambi formula models, NumPyro/JAX programs, ArviZ-only analysis of existing draws, deterministic optimization, or general statistics without PyMC code.

Mechanics for building and managing pollers — subprocess scripts that check external services on a schedule and emit events when something has changed. Use when authoring a new poller (a `pollers.json` manifest plus a script in any language), debugging why a poller isn't firing, or extending an existing one. Pollers run on cron, emit JSONL events when there's something to report, and stay silent otherwise (silence-as-filter). The framework discovers `<home>/skills/<name>/pollers.json` files at startup and via `reload_pollers`; each emitted event becomes a fresh turn on a `poller:<name>` synthetic channel. Companion to the `world-scanning` skill, which catalogs *what's worth polling*. Distinct from `async-tasks` (one-shot wake-up via bash_async, not recurring) and from in-process scheduler callables (saga-consolidate, oauth-usage-poll — those mutate mimir-internal state and aren't subprocess-isolated).

Authorized reverse engineering of custom binary protocols, Protobuf/gRPC, WebSocket frames, and PCAP-driven protocol recovery with structured workflow and tooling.

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques for 1.5-3.6× speedup without quality loss.

Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

Program LM pipelines with DSPy — define Signatures (input/output fields), build Modules (ChainOfThought, ReAct, Predict), compose them into Programs, then optimize with Teleprompters (BootstrapFewShot, MIPRO, COPRO) to auto-improve prompts from examples.

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.

Apply meta-principles of software craftsmanship: DRY, orthogonality, tracer bullets, and design by contract to build systems that are easy to change, understand, and trust.

Performance profiling principles. Measurement, analysis, and optimization techniques.

Run Karpathy-style autonomous ML search on a real training repo: choose the right mode (setup, program.md, bounded loop, results interpretation, or constrained-hardware adaptation), preserve the immutable prepare.py / 300-second / val_bpb contract, and route prompt/skill eval work away to LangSmith, Promptfoo, Braintrust, or skill-autoresearch.

Write idiomatic Elixir code with OTP patterns, supervision trees, and Phoenix LiveView. Masters concurrency, fault tolerance, and distributed systems. Use PROACTIVELY for Elixir refactoring, OTP design, or complex BEAM optimizations.

Provides idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications, covering model architecture, training loops, data pipelines, and checkpointing.

Validates inputs, generates samplesheets and launch scripts, and optionally executes nf-core/pacsomatic matched tumor-normal workflows from BAM files, supporting local runs and scheduler submission (LSF/Slurm/PBS/SGE).

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.

Analyzes algorithm time and space complexity, classifies problems by complexity classes, proves NP-completeness, and designs approximation algorithms.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.