Results for “pi”

Expert deployment engineer specializing in modern CI/CD pipelines, GitOps workflows, and advanced deployment automation. Masters GitHub Actions, ArgoCD/Flux, progressive delivery, container security, and platform engineering. Handles zero-downtime deployments, security scanning, and developer experience optimization. Use PROACTIVELY for CI/CD design, GitOps implementation, or deployment automation.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Create stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web, or create slides for a talk/pitch. Helps non-designers discover their aesthetic through visual exploration rather than abstract choices.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Turn any GitHub repo, local folder, PR, or markdown/Obsidian vault into an interactive architecture map with CodeFlow — a zero-build single index.html browser app (React 18 + D3.js from pinned CDNs) that runs 100% client-side with no backend and no data collection. Pick an input (public repo, private repo with a local token, local files, PR URL, markdown vault), choose a visualization mode (folder/layer/churn/blast), and read the analysis — dependency graph, blast radius, code ownership, heuristic security scanner, pattern/anti-pattern detection, A–F health score, activity heatmap, PR impact — then export JSON/Markdown/text/SVG/PDF or wire the self-updating CodeFlow Card SVG onto a README.

Process and analyze mass spectrometry data: import spectra from MGF, mzML, MSP, and JSON formats; apply 40+ filters for metadata harmonization and peak cleaning; compute spectral similarities (cosine, modified cosine) for compound identification; build reproducible processing pipelines.

Perform runtime iOS app security assessments using Objection and Frida to inspect keychain, filesystem, and memory, bypass client-side protections, and evaluate data storage, network, and authentication controls during authorized penetration tests.

Tests APIs for excessive data exposure where endpoints return more data than the client application needs, relying on the frontend to filter sensitive fields. Maps to OWASP API3:2023 Broken Object Property Level Authorization.

Translate a vague request or GitHub issue into a concrete, testable spec using five structured elements (problem, scope, acceptance criteria, edge cases, success criterion) before writing any code. Prevents scope creep, PR churn, and mid-implementation pivots.

Routing-first Fabric operator skill for reusable named AI transforms over stdin, files, transcripts, notes, logs, and cleaned web text. Use when the user wants Fabric patterns, custom pattern packs, shell-pipe composition, or `fabric --serve` workflows — not generic one-off chat prompting, repo-aware coding, or fully deterministic automation.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Full test suite improvement composite — audit health, prune dead tests, validate with mutation testing, capture rationale in ADR. Use after a major feature ship, before tightening coverage gates, or when the suite shows bloat, excessive skips, or slow runtime. Chains test-health → test-cleanup → mutation-test → adr-write.

Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

Create stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web, or create slides for a talk/pitch. Helps non-designers discover their aesthetic through visual exploration rather than abstract choices.

Generate syntactically correct Claude Code configuration components (commands, subagents, skills, output styles) with optimal cross-component integration. Triggers on requests to create slash commands, agents, subagents, skills, output styles, Claude Code plugins, agentic workflows, or multi-agent pipelines. Ensures parameter validity per component type and semantic coherence across component references.

Generic webapp QA fallback — use only when no project-specific QA skill applies (project-local QA wins; native builds → mobile-emulator-test). Use when "QA the app", "test CRUD", or "smoke test". Monkey / guest vs logged-in → test-exploratory. Dead buttons → plan-stub-checker. Pixel diffs → test-visual-regression.

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

Create stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web, or create slides for a talk/pitch. Helps non-designers discover their aesthetic through visual exploration rather than abstract choices.

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.

Universal project workflow with built-in quality auditing. Quick mode (fast brainstorm, ~5 min) or Deep mode (full plan with audits, ~30 min). Auto-picks based on task size. Trigger: 'build', 'create', 'plan', 'new project', 'brainstorm', 'design', 'UI', 'website', 'dashboard', 'app', 'help me build', or any project-level task.

Automates static analysis of Android APK/AAB files using MobSF to identify hardcoded secrets, insecure permissions, vulnerable components, and weak cryptography for pre-deployment security assessments or CI/CD integration.

Edits real video footage through a structured pipeline: transcribe and plan with an AI agent, cut with FFmpeg, compose with Remotion, generate voiceover and music via ElevenLabs and fal.ai, then polish in Descript or CapCut.

Reference for MariaDB's built-in date and time functions, covering current time, arithmetic, differences, extraction, parsing, formatting, Unix time, time-zone conversion, construction, and Oracle-compatible helpers, with guidance on version-specific behavior and common pitfalls.

Provides a creative-direction layer for music videos and short-form edits, defining an angelcore/cloud-trance/hyperpop aesthetic with mood, color, light, and beat-synced editing grammar, and orchestrating video production skills into a pipeline.

Classifies personal vs non-personal data per GDPR Art. 4(1) definition test with decision tree for borderline cases. References Breyer v Germany CJEU C-582/14 dynamic IP ruling and WP29 Opinion 4/2007. Keywords: personal data, GDPR Art 4, data classification, Breyer ruling, identifiability test, PII.

Production pipeline for mathematical and technical animations using Manim Community Edition. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories. Use when users request: animated explanations, math animations, concept visualizations, algorithm walkthroughs, technical explainers, 3Blue1Brown style videos, or any programmatic animation with geometric/mathematical content.

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. Use when you need to build complex AI systems, program LMs declaratively, optimize prompts automatically, create modular AI pipelines, or build RAG systems and agents.

Run the full autonomous engineering pipeline end-to-end (plan, work, code review, test, commit, push, open PR, watch CI, fix CI failures until green). Use only when the user explicitly requests hands-off execution of a software task and provides a feature description; do not auto-route casual conversation here.