Your technology department
Plan software projects, review architecture and APIs, debug failures, and improve engineering workflows. Headcount Technology includes nineteen reusable skills covering implementation planning, code review, testing, development practices, and AI automation design. More
Skills 19 in the ChatGPT listing
Ai workflow architect Designs AI systems, automations, and agent workflows for a business — identifying which manual work is worth automating, how to structure the system, which tools fit, and what could go wrong. Api design Designs interfaces that survive their consumers — resource modeling, errors, versioning, pagination, and compatibility. Branch and worktree workflow Isolates feature work in its own branch or worktree and integrates it cleanly when done. Chief technology officer Owns architecture, engineering delivery, infrastructure, data platform, and internal systems. Cloud infrastructure Designs and runs cloud infrastructure — environments, infrastructure as code, networking and isolation, scaling, and cost. Code review Conducts and responds to code review — reviewing a change for correctness, design, and risk, and evaluating review feedback received on your own work. Completion verification Verifies that work is actually complete before it is claimed to be — running the checks, reading the output, and confirming the original request was satisfied rather than approximated. Data migration Moves data from one system to another without losing it or corrupting it — profiling the source before mapping, deciding between big-bang and parallel-run cutover, reconciling counts and values rather than assuming, handling the records that will not map cleanly, and planning a rollback that is actually executable. Implementation planning Turns a spec or requirement into a written plan a separate session or agent can execute, then drives that plan through review checkpoints. Observability and reliability Makes systems debuggable and reliably operable — instrumentation, alerting that is worth waking for, service objectives, and learning from failure. Parallel agent delivery Splits work across multiple agents or sessions running at once, keeping their surfaces disjoint so results merge cleanly. Prompt optimizer Turns rough intent or a weak prompt into a reliable one — diagnosing why output is inconsistent, restructuring the instruction, and adapting it across models. Release and deployment Ships changes safely and often — pipelines, deployment strategies, feature flags, rollback, and database changes. Skill authoring Writes and revises agent skills so they trigger at the right moments and give usable instruction when they do. Solution architecture Designs system structure and makes architectural decisions defensible — boundaries, coupling, trade-offs, and recording why. Solution exploration Explores the problem and the range of possible approaches before any code is written — clarifying what is actually being asked, surfacing options with their tradeoffs, and converging on one. Systematic debugging Finds the root cause of a bug, test failure, or unexpected behavior before proposing any fix. Technical debt management Makes technical debt visible and decidable — distinguishing real debt from mess, quantifying its cost, and arguing for remediation in business terms. Test driven development Drives implementation by writing a failing test first, then the smallest code that passes it. 