Results for “sam”

Generate a sequence of keyframe images from a story premise using the MuAPI image generation service.

Turns a person's photo into a custom 3D action figure with collectible toy packaging.

Parses external reviews, enforces coverage and grounding, and drafts a text-only rebuttal under venue limits, managing follow-up rounds.

Generates a structured post-task reflection capturing surprises, patterns, prompt improvements, and gotchas, writing it to a markdown file without modifying project rules.

Creates and maintains persistent markdown planning files that survive session resets by hooking into Claude Code's lifecycle.

Delegates independent debugging tasks to parallel agents with isolated context, preserving the main session for coordination.

Build Cloudflare D1 schemas and queries with Drizzle ORM, including migrations, relations, and batch operations.

Routes ambiguous or multi-domain engineering requests to the smallest ordered skill set. Do not use for clear single-skill work or same-task follow-ups.

Use when you need to perform quality control analysis on high-throughput sequencing data (fastq, bam, sam, or fast5 files) to identify potential problems before downstream analysis.

Translates creative intent into technical cinematographic directives for Veo3, Kling, and Luma video models via muapi.ai.

Conducts multi-source web research on any topic, cross-referencing claims and delivering a structured report with sources and recommendations.

Drafts a LaTeX paper section by section from an outline, with venue-specific templates and citation management.

Launches long-running evaluation batches in isolated tmux sessions with pre-flight verification, monitoring, and post-flight analysis for unattended runs.

Guides setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

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.

Build, run, and visualize multi-step AI generation workflows by chaining image, video, and audio nodes into automated pipelines.

Generates high-fidelity images using reasoning-driven prompts and structured creative briefs via muapi.ai.

Consolidates auto-memory files in ~/.claude/projects by auditing, planning, and executing a 4-phase cleanup with user approval.

Generates diagrams and visuals from code or descriptions, routing to specialized tools for hand-drawn, formal, academic, or architectural output.

Runs a post-batch analysis pipeline after an eval completes: verifies results, executes analysis scripts, refreshes dashboards, and writes a summary report.

Iteratively improves a compiled LaTeX paper through two rounds of external LLM review, fix implementation, and recompilation.

Guides test-driven development with a red-green-refactor loop, emphasizing behavior-focused tests and vertical slicing over horizontal test writing.

Guides users through structured decision-making with weighted scoring, criteria analysis, and clear recommendations, including quick and interview modes.

Researches conversation and code context to generate 1-6 targeted clarifying questions when a prompt is vague, then executes the original request.

Runs a disciplined debugging loop for hard bugs and performance regressions, covering reproduction, hypothesis testing, instrumentation, fixing, and regression testing.

Analyzes a codebase with multiple agents and builds an interactive React knowledge graph dashboard for onboarding and visual architecture exploration.

Provides 20 standard operating procedures for growth teams, covering A/B testing, analytics tracking, and related growth workflows.

Creates a branch, atomic commits, pushes, and opens a pull request with terse, exact messages following conventional commit format.

Reproduces an existing design as a faithful, self-contained HTML file, then generates and evaluates design variations to recommend the strongest option.

Serves the current git worktree as a Laravel Herd site, rewrites its .env for the new host, and installs dependencies.

Compute the Kruskal-Wallis H-test using scipy.stats.kruskal for independent samples, returning the H statistic and p-value.

Kit de ferramentas para arquivos genômicos. Leia/escreva alinhamentos SAM/BAM/CRAM, variantes VCF/BCF, sequências FASTA/FASTQ, extraia regiões, calcule cobertura, para pipelines de processamento de dados NGS.

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

Post-execution skill crystallization and learning engine updates for WinDAGs. Runs after successful execution to update Thompson sampling parameters, track method quality, detect monster-barring, log near-miss events, and signal Kuhnian crises. Activate on "curator", "learning update", "skill crystallization", "Thompson sampling", "monster-barring", "near-miss", "Kuhnian crisis", "post-execution learning". NOT for pre-execution risk scanning (use windags-premortem), retrospective analysis (use windags-looking-back), or DAG construction (use windags-architect).

Re-render a hero image into aspect ratios for Instagram, TikTok, YouTube Shorts, and Twitter/X.