Results for “e-e-a-t”

API security checklist for reviewing endpoints before deployment. Use when creating or modifying API routes to ensure proper authentication, authorization, and input validation.

Review an open GitHub issue for a selected repository against the current codebase, then either close it as already implemented with an evidence comment, label it needs clarification with an explanatory comment, or create a fleshed-out Jira story in the requested board/project when the remaining work is clear. Use when triaging GitHub issues into Jira backlog work or closing stale implemented GitHub issues.

Assistant for newapi (new-api), an open-source unified AI gateway platform (https://github.com/QuantumNous/new-api). Use when the user asks about New API, managing models, groups, balance, or tokens, or securely copying keys, applying them to config files, or using them in commands without exposing secrets.

Simulates open quantum systems with QuTiP, the Quantum Toolbox in Python, solving Lindblad master equations (mesolve), Monte Carlo trajectories (mcsolve), and unitary dynamics (sesolve). Use when studying master-equation or Lindblad dynamics, decoherence, dissipation, quantum optics, cavity QED, or open-system time evolution. NOT for circuit-based quantum computing or hardware execution — for IBM Quantum circuits prefer alterlab-qiskit, for Google Quantum AI or NISQ circuits prefer alterlab-cirq, and for gradient-trained quantum ML prefer alterlab-pennylane. Part of the AlterLab Academic Skills suite.

Foundational Elixir patterns covering modules, pattern matching, processes, OTP behaviors (GenServer, Supervisor), Phoenix framework basics, and functional programming idioms. Use when writing Elixir code, building concurrent systems, working with Phoenix, or needing guidance on Elixir development patterns. This is the entry point for Elixir development.

Guides authorized hardware and embedded interface security research including UART/JTAG discovery, debug pad triage, secure boot assessment, and offline firmware extraction support.

Audit a repo (or diff) for weak error handling — swallowed exceptions, bare catches, unhandled promise rejections, missing network timeouts/retries, and errors logged but not surfaced. Pure Claude Code, no MCP, no signup.

Implements finite automata, regular expressions, parsers, and lexers for building text processors and pattern matchers.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

Use to export CSAT or satisfaction survey responses from a support platform into one canonical shape. Trigger for "export our CSAT data", "pull satisfaction ratings", "get survey responses out of Zendesk/Freshdesk/Gorgias/HubSpot", joining CSAT to tickets, or when a conversation export left the csat field null.

Detects and removes AI writing patterns from Chinese text, making it sound more natural and human-like. Based on Wikipedia's comprehensive guide to AI writing characteristics.

Build detailed ideal customer profiles with pain points, objections, buying triggers, and messaging angles. Includes community research to find where ICPs gather online and extract their exact language. Use when researching audiences, creating buyer personas, or developing targeted messaging.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Manage and monitor Heap product analytics by querying the Heap API for account info, defined events, user properties, segments, event volume, active users, and data health.

Update ElevenLabs agent skills from a merged weekly changelog, then open a pull request.

Generate, edit, and read PowerPoint presentations using PptxGenJS for creation, XML workflows for editing, and markitdown for text extraction.

Interact with Slack by reacting to messages, managing pins, sending, editing, and deleting messages, and fetching member and emoji information.

Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems.

Use when planning a CAV (Computer Aided Verification) project timeline from venue and category selection through submission, the two-stage review with early reject and rebuttal, artifact evaluation by the AEC, and the LNCS open-access camera-ready, with backward-planning offsets for a verification-tool paper and honest handling of the single-annual-deadline cycle.

Discover and install complementary skills from the inference.sh registry to expand AI agent capabilities.

Evaluates whether suing is worthwhile, explains the litigation process from filing to resolution, and guides case preparation and settlement negotiations.

Creates, edits, and analyzes .docx files using OOXML manipulation, pandoc conversion, and tracked changes for document review.

Guides technical evaluation of code review feedback, emphasizing verification before implementation and reasoned pushback over performative agreement.

Guides daily mindfulness practice to reduce stress, improve focus, and build emotional regulation through structured routines and reflection.

Create a detailed, phased implementation plan with documentation discovery. Use when asked to plan a feature, task, or multi-step implementation — especially before executing with do.

Utilize Shodan, the search engine for Internet-connected devices, to discover exposed assets, vulnerable ports, default credentials, and specific infrastructure configurations using advanced search queries (dorks).

Trains, evaluates, and runs inference for BEVFusion multi-sensor 3D object detection models that fuse LiDAR and camera data in bird's-eye-view space for autonomous driving.