Shared Skills

Discovers and triages recent arXiv papers for AI/ML, agents, and software/QA. Use when scouting categories, arXiv IDs, or source lists.

Applies systematic code review patterns and checklists. Use when reviewing PRs or diffs for correctness, security, readability, maintainability, and AI-generated changes.

Guides secure blockchain development across EVM, Bitcoin, Solana, Cosmos, and TON. Use when building contracts, wallets, custody flows, bridges, or on-chain backends.

Guides user research methods and research ops. Use when running interviews, usability tests, surveys, or A/B tests to de-risk product decisions.

Prompt engineering for production LLMs — structured outputs, evals, RAG, tool workflows, multimodal prompting, and safety. Use when designing, debugging, or shipping prompts.

Diagnoses and tunes SQL for OLTP workloads on PostgreSQL, MySQL, and SQL Server. Use when tuning queries, reading plans, indexing, or fixing lock contention.

Measures AI coding impact and extension robustness. Use when tracking delivery, quality trajectories, cost, experience, pilots, scorecards, or leadership reporting.

Plans complex development workflows for Claude Code, Codex, and assistants. Use when breaking features, refactors, migrations, or parallel work into verified steps.

Builds and debugs Playwright E2E suites. Use when authoring browser tests, fixing flakes, or hardening Playwright CI and locator strategy.

Implements production-grade i18n/l10n for React, Vue, Angular, and Next.js with ICU format and RTL support. Use when setting up or debugging localisation.

Designs task runtimes for Loop Engineering, Graph Engineering, and background work. Use when work needs task lists, cyclic/workflow graphs, cancellation, or teammate coordination.

Generates or validates Conventional Commits messages from staged diffs. Use when drafting commit messages, checking repo rules, or inferring scope from changed files.

Design local AI engines for iOS. Use when wiring Apple Foundation Models, local classifiers, extraction, summarization, grounded answers, and cloud fallbacks.

Guides multi-GPU pre-training: DDP, FSDP2, ZeRO, tensor/pipeline/expert parallelism, fp8/Muon. Use when scaling a run, training MoE, or reproducing GPT-2 on rented GPUs.

Context-driven AI development with AGENTS.md, repo knowledge bases, Claude Code, Codex, and Copilot. Use when adopting repo-native AI workflows or multi-repo setups.

Game-theory primitives for strategic decision systems, auctions, mechanism design, incentives, attribution, negotiation, debate, and trust. Use when modeling strategic play.

Team-theory primitives for cooperative multi-agent decisions, subagent allocation, communication value, Dec-POMDPs, and decentralized control. Use when organizing agents.

Applies C# and .NET backend standards. Use when shaping API boundaries, data access, resilience, observability, or security defaults.

Designs plugin systems for coding-agent runtimes and CLIs. Use when adding plugin manifests, extension points, built-in plugins, or reloadable agent integrations.

API contract testing across REST, GraphQL, gRPC, AsyncAPI, webhooks, and workflow contracts. Use when you need schema validation, breaking-change detection, and CI quality gates.

Designs database schemas, migrations, and data models for PostgreSQL, MySQL, MongoDB, and Redis. Use when planning tables, relationships, indexes, or ORM-backed schema changes.

Provides application security guidance for design and implementation. Use when reviewing auth, data handling, supply-chain controls, or AppSec architecture.

Designs session lifecycle for coding-agent runtimes. Use when implementing resume, transcript restoration, checkpoint rewind, cross-worktree recovery, or session-state persistence.

Guides dependency management across languages and ecosystems. Use when choosing package managers, lockfiles, update policy, security scanning, SBOMs, or monorepo patterns.

Coordinates multi-agent execution across subagents, teams, and workflows. Use when planning dependency-aware fan-out, verifier passes, runtime selection, or Loop Engineering.

Designs operating models for AI in product teams. Use when planning platform ownership, provider strategy, data boundaries, evals, or sensitive-data controls.

Builds analytics engineering layers for metrics, contracts, and BI-ready models. Use when shaping dbt or SQLMesh marts, metric governance, lineage, or data quality.

Control-theory primitives for PID, MPC, Kalman, stability, anti-windup, dead-time, breakers, and limits. Use when tuning autoscaling, retries, or agent loops.

Designs transactional email systems and send infrastructure. Use when implementing resets, receipts, deliverability controls, templates, or inbound email handling.

Adds per-skill learnings loops for dated patterns, mistakes, and domain facts. Use when wiring skill memory, consolidation, or drift audits.

Applies Beer's VSM and Ashby's Law to diagnose org or agent-system viability. Use when a team or agent hierarchy has coordination, escalation, or requisite-variety problems.

Decision-theory primitives for uncertain choices, utility, Bayesian decisions, regret, value of information, MCDA, options, and bandits. Use when choosing under uncertainty.

Network-science primitives for graph systems, centrality, PageRank, communities, contagion, link prediction, and temporal networks. Use when analyzing graph structure.

Applies queueing theory (Little's Law, M/M/c, Erlang, Kingman, USL) to capacity and latency decisions. Use when load causes non-linear latency growth or queue overrun risk.

Designs approval and permission systems for coding-agent runtimes. Use when modeling tool approvals, plan-mode transitions, sandbox prompts, or worker permission handoffs.

Designs terminal-first coding-agent UX: REPL, prompt input, status lines, keybindings, display modes. Use when shaping TUI rendering, history, or background-task navigation.

Builds and audits LLM pretraining corpora: extraction, filtering, dedup, decontamination, data mixing, synthetic data. Use when curating or ablating a pretraining data pipeline.

Designs runtime and platform architecture inside a chosen solution. Use when deciding modular monolith vs services, consistency, resilience, or estate topology.

Defines clean-code standards and CC-* rules. Use when reviewing code, setting team standards, citing lint findings, or measuring erosion, complexity mass, and verbosity.

Guides Cloudflare Wrangler CLI usage for Workers, bindings, deploys, local dev, and config. Use when running or reviewing wrangler commands.

Designs workflow automation with n8n, Langflow, Temporal, and Trigger.dev. Use when choosing automation platforms, durable runtimes, or custom code.

Applies planning and search theory (A*, CSP, MCTS, STRIPS/PDDL, HTN) to agent design. Use when an LLM agent hallucinates action sequences or needs precondition/effect validity.

Information-theory primitives for AI systems, entropy, mutual information, KL, compression, channel limits, MDL, bottlenecks, and signal quality. Use when quantifying information.

Reliability-theory primitives for MTBF/MTTR, availability, hazards, FMEA, redundancy, error budgets, Weibull analysis, and SLOs. Use when modeling failure.

Proves iOS build/install/launch truth and triages hangs, crashes, jank, memory kills, and stale builds. Use when simulator, bundle, or runtime performance state is in doubt.

Designs cross-system target states and transition plans from business workflows and system boundaries. Use when comparing end-to-end solution options or phased migrations.

Designs remote execution and bridge runtimes for coding agents. Use when implementing remote sessions, local-UI remote-execution, reconnect logic, or permission bridging.

Distributed-systems primitives for CAP/PACELC, FLP, Paxos, Raft, clocks, CRDTs, leases, quorums, and broadcast protocols. Use when designing coordination.

Designs slash-command runtimes for coding-agent CLIs. Use when modeling command registries, lazy loading, aliases, forked commands, or remote-safe dispatch.

Designs settings and policy layers for coding-agent runtimes. Use when modeling source precedence, managed policy, env controls, or runtime settings validation.

16 behavioral-economics primitives for ethical pricing, choice design, and retention. Use when framing, defaults, habits, dark patterns, AI-agent nudging, or nudge ethics apply.

Designs provider runtimes for coding agents. Use when modeling model abstraction, streaming semantics, tool-call normalization, retries, or fallback routing.

Analyzes commit and PR history to score contribution quality objectively. Use when building engineering scorecards, calibrating promotions, or measuring AI-assist impact.

Consumer-neuroscience primitives for attention, arousal, bonding, narrative, memory, and reward. Use when shaping ethical UX, neuro study design, or DMCC/AI Act gates.

Theory of Constraints primitives for focusing steps, drum-buffer-rope, throughput accounting, critical chain, and policy constraints. Use when sequencing by bottleneck.

Designs execution sandboxes for coding agents. Use when modeling process isolation, filesystem policy, network controls, workspace mounts, or destructive-command boundaries.

Build/install/launch proof, ANR/jank/memory triage, and stale-build debugging for native Android apps. Use when runtime truth, performance, or crash root cause is in doubt.

Grounding-theory primitives for human-AI and agent handoffs, common ground, acceptance evidence, repair, and ambiguity. Use when coordinating meaning.

Designs coding-agent observability and evals. Use when measuring traces, replay, checkpoint lineage, quality trajectories, tool grading, regression, or cost.

Designs release and distribution systems for coding-agent CLIs. Use when modeling packaging, auto-update channels, plugin compatibility, cache migrations, or install footprints.