Agents that get to workAgents that get to work

Searches and reads content from the web and 16 platforms (Reddit, YouTube, X, LinkedIn, GitHub, RSS…) to research a topic. Read-only: it never posts.

Designs and implements an LLM agent in your codebase: the loop, tools, memory, stop limits and a task-level eval suite to run before shipping.

Adds an AI API integration (Claude, OpenAI) to your app: client setup, retries, error handling, streaming, typed requests and tests with mocked responses.

Scaffolds a chatbot on your existing stack: streaming responses, persistent conversation history, optional RAG grounding and a feedback loop.

Audits what goes into your AI app's context window and decides what to retrieve, summarize or compact, then checks the result with an eval.

Implements vector embeddings in your app for semantic search, RAG or similarity matching: ingestion, chunking, querying and relevance tuning.

Decides honestly whether an LLM needs fine-tuning or better prompts, and if so prepares data, trains and compares before and after on held-out data.

Adds input and output filters, prompt-injection defense and personal-data handling around an AI feature, then proves them with adversarial tests.

Measures where your app spends tokens, then applies model routing, caching and prompt compression, keeping each change only if an eval shows quality holds.

Sets up an evaluation suite for an AI feature: test cases, rubrics, code-based checks, LLM-as-judge with bias controls and a CI gate.

Instruments your AI app with traces, token and cost tracking, quality signals, dashboards and alerts for every model call.

Builds a Model Context Protocol server that exposes your project's functions, APIs or data to clients like Claude Code, with transport, auth and tests.

Designs a system of several AI agents with an orchestration pattern, contracts between agents and failure handling, or advises a single agent instead.

Reviews and rewrites your system prompts, user prompts and tool definitions, then gives an A/B comparison of the original and improved versions.

Builds a test harness that measures a RAG pipeline: a golden question set, retrieval metrics, groundedness checks and a regression gate.

Adds a cache in front of LLM calls so repeated or near-duplicate requests get a stored answer, and measures whether it helps without serving wrong ones.

Makes a model return dependable JSON or typed objects: schema design, native structured modes, validation and bounded repair loops.

Audits or implements the tools a model can call: clear schemas, parallel calls, error handling that lets the model recover, and an accuracy eval.

Designs a low-latency voice agent that handles interruptions: speech-to-text and text-to-speech choice, latency budget and conversation design.

Applies a multi-phase research method, with several angles and sources, before answering or creating content that needs up-to-date information.

Checks every installed skill for structure, declared permissions and safety, then produces a health report of what passes and what fails.

Runs a chain of skills defined in a pipeline file, with quality gates between steps and a final summary showing the result of each step.

Generates a new skill from a plain-language description, together with its permission manifest and a test file.

Scans your project, detects its tech stack and activates only the relevant skills, saving the choice as a project configuration file.

Turns a raw dataset into a clean, well-split, leakage-free base for modeling: profiling, cleaning, train/validation/test splits and balance checks.

Designs features for a tabular ML problem inside a leakage-safe pipeline: encodings, scaling, interactions and date-based features.

Gives an ML project reproducible training, experiment tracking, a model registry and CI, sized to the team instead of a full platform.

Takes a trained model to production: batch or online serving pattern, prediction API, packaging, monitoring, drift detection and rollback plan.

Evaluates a model beyond one headline number: metrics matched to error costs, calibration, per-slice performance and error-by-error analysis.

Trains a model methodically: a trivial baseline first, then the simplest model that could work, with cross-validation and tuning.

Builds a recommender that beats a popularity baseline: picks the approach, handles cold start and evaluates it offline.

Builds a forecast with naive baselines, seasonality, backtesting and prediction intervals, without leaking future data.