Fit and interpret regression models for insight, reading coefficients, fit, and caveats honestly rather than as causal truth.
Run an internal dogfooding program with staged cohorts, one feedback intake, and numeric exit criteria to general availability.
Ship reusable prompt templates from an MCP server so users invoke tested workflows rather than improvising.
Support the recurring people function with agents that maintain onboarding paths, keep policy documents current, prepare review inputs, and flag gaps, while every human decision…
Provide starting templates that encode the organisation's defaults so new services are consistent without anyone reading a standards document.
Operate as a performance engineer who sets budgets, gates regressions in CI, and runs profiling as a service other teams rely on.
Build input systems with action mapping, buffering, dead zones, and rebinding that feel responsive and fair.
Structure issues, labels, and boards so the tracker reflects reality and answers what is being worked on. Use when the backlog has grown beyond what anyone reads.
Observe external dependencies so their degradation is visible before users report it and attributable when it happens. Use when your service depends on APIs you do not control.
Find dependency cycles between modules and break them with layering, interface extraction, or dependency inversion.
Rebalance a top-heavy test suite toward fast unit tests without dropping the coverage the slow tests provide.
Understand caches, branches, and pipelines well enough to explain why equivalent code differs in speed by an order of magnitude.
Run an agent across several MCP servers without name collisions, tool overload, or ambiguity about which server owns an action.
Configure what a coding agent may do without asking, balancing autonomy against the cost of an unwanted action. Use when setting up an agent's permissions for a project.
Reach customers before they contact you, when telemetry or a known issue means they are about to have a problem.
Tell correlation from causation and avoid the confounding, selection, and reverse-causation traps. Use when data shows a relationship and someone is about to claim one thing…
Follow up on unanswered emails effectively: the right timing, adding value, and knowing when to stop.
Write ISRs that stay minimal, share data through volatile-correct handoffs, and defer real work safely.
Operate as a CTO-level advisor who turns business goals into build-or-buy calls, org design, technology bets, and a named risk posture.
Put confirmation gates, blast-radius limits, audit trails, and kill switches around automation that can destroy things. Use when building scripts or bots with destructive power.
Design SDKs that feel idiomatic per language, handle auth and resilience, and version cleanly. Use when building an official client library for your API.
Run a war room for a live incident with defined roles, a steady cadence, a decision log, and explicit stand-down criteria.
Monitor a GPU fleet so you can tell a genuinely busy GPU from one reporting 100 percent utilization while computing almost nothing, and find the wasted spend.
Initialise parameters so signal and gradients propagate at usable scale from the first step. Use when a deep network fails to train from the start or diverges immediately.
Improve at chess through tactical pattern training, endgame fundamentals, and engine-assisted analysis of your own losses.
Verify changes against real traffic without risking users, using canaries, shadow traffic, and feature gates with automatic rollback.
Write emails that get read and acted on: one clear purpose, the ask up front, and only what the reader needs.
Prevent cross-site scripting by encoding output for its exact sink, keeping template autoescaping on, and backing it with a strict Content Security Policy.
Report progress in a form that surfaces problems early and lets a reader act, rather than reassuring. Use when reporting to sponsors or across teams.
Choose isolation levels by the anomalies they prevent, understand locking versus MVCC, and retry on serialization failures.
Accept user file uploads without letting them become code execution, storage exhaustion, or a path into other users' data.
Judge whether a sample represents the population and spot the selection, survivorship, and response biases that invalidate conclusions.
Write status updates with progress, risk, and asks calibrated to the audience, honoring the no-surprises rule.
Read a diff for security by tracing attacker-controlled input to dangerous operations and checking every trust boundary it crosses.
Sort and compare text using locale collation rather than byte order, so lists read correctly in every language.
Improve RAG answer quality by reranking retrieved candidates so the most relevant chunks reach the model.
Generate several independent attempts, score them against explicit criteria, and select or combine the winner, instead of iterating one attempt.
Reshape rows into columns and back, and know when the reshape belongs in SQL rather than in the reporting layer.
Guard a multi-agent team against the structural failures single agents never hit: context lost across handoffs, false agreement, and runaway cost.
Manage a code freeze with a scoped declaration, risk-classed changes, a fast exception process, and a planned thaw.
Produce a recurring newsletter with a sustainable format, a clear reason to open, and rendering that survives email clients. Use when publishing regularly to a subscribed audience.
Track technical debt deliberately by framing each item as interest owed and budgeting steady paydown.
Database schema validation, data integrity testing, migration testing, transaction isolation, and query performance.
Run a deprecation from usage telemetry through a hard sunset date, with migration tooling and staged comms, so a system retires without stranding its callers.
Ship FP16, BF16, or FP8 training and inference that holds accuracy while capturing the speedup, using loss scaling and numeric validation.
Keep the suite fast enough to run on every change by measuring the slow tests, shaping the pyramid, cutting IO from the fast tier, and sharding.
Operate as an ML engineer who takes a research model to reliable production behind eval gates and keeps it healthy across its lifecycle.
Establish heuristic and simple-model baselines that bound what complexity is worth. Use when starting any ML project or auditing whether a complex model earns its cost.
Assemble an investor or board update from source metrics with agents that draft, fact-check every number, and surface bad news rather than bury it.
Transfer a ticket between agents, shifts, or teams without the customer repeating themselves. Use when work crosses people and context is being lost.
Coordinate across push, email, in-app, and SMS so a user gets one message rather than the same thing four times. Use when several channels exist and users receive duplicates.
Use leases with fencing tokens for mutual exclusion across machines, or restructure to need no lock at all.
Ongoing Gmail inbox management via scheduled runs. Archives known noise, flags urgent items, drafts replies in-thread (never auto-sends), and catches stale follow-ups.
Document chunking strategies for RAG systems. Use when implementing document processing pipelines to determine optimal chunking approaches based on document type and retrieval…
Keep and re-engage existing customers, which is usually cheaper and more valuable than acquiring new ones.
Test localisation readiness with generated pseudo-translations that expand, accent, and bracket text, before any real translation exists.
Choose the structure whose operations match how the data will actually be used, rather than defaulting to a list or a map.
Audit third-party packages by pinning resolved versions, scanning against advisory databases, and catching malicious lookalikes before install.
Decide whether asyncio pays off, keep the event loop unblocked, and structure concurrency with TaskGroup and disciplined cancellation.
Cut LLM cost and latency with caching, model tiering, prompt diet, batching, and streaming UX. Use when the inference bill or response time needs engineering down.