ai-coding-agents-release-distribution
Designs release and distribution systems for coding-agent CLIs. Use when modeling packaging, auto-update channels, plugin compatibility, cache migrations, or install footprints.
ai-coding-agents-remote-runtime
Designs remote execution and bridge runtimes for coding agents. Use when implementing remote sessions, local-UI remote-execution, reconnect logic, or permission bridging.
ai-coding-agents-sessions
Designs session lifecycle for coding-agent runtimes. Use when implementing resume, transcript restoration, checkpoint rewind, cross-worktree recovery, or session-state persistence.
ai-coding-agents-settings-policy
Designs settings and policy layers for coding-agent runtimes. Use when modeling source precedence, managed policy, env controls, or runtime settings validation.
ai-coding-agents-tasks
Designs task runtimes for Loop Engineering, Graph Engineering, and background work. Use when work needs task lists, cyclic/workflow graphs, cancellation, or teammate coordination.
ai-coding-agents-terminal-ui
Designs terminal-first coding-agent UX: REPL, prompt input, status lines, keybindings, display modes. Use when shaping TUI rendering, history, or background-task navigation.
ai-coding-agents-tools
Designs tool runtimes for coding agents. Use when modeling tool registries, deferred loading, permission-aware execution, tool search, or remote tool rendering.
ai-coding-agents
Creates coding agents on Claude Code, Codex, and Agent SDK. Use when defining review, test, refactor, or team agents — not building a runtime.
ai-data-curation-pretraining
Builds and audits LLM pretraining corpora: extraction, filtering, dedup, decontamination, data mixing, synthetic data. Use when curating or ablating a pretraining data pipeline.
ai-deep-research
Builds repeatable deep-research workflows for verified synthesis. Use when producing evidence-backed briefs, comparisons, dossiers, or research pipelines.
ai-distributed-training
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.
ai-evals
Designs trustworthy LLM, agent, responsible-AI, and multimodal evaluations. Use when measuring quality, fairness, privacy, grounding, safety, or judge reliability.
ai-llm-inference
LLM inference patterns for latency, batching, caching, quantization, routing, and serving stacks. Use when optimizing throughput, tail latency, or serving cost.
ai-llm
Guides the LLM lifecycle from strategy to deployment. Use when planning, comparing, fine-tuning, distilling, compressing, migrating, or operating LLM systems.
ai-local-model-ops
Runs local and self-hosted LLM workflows with Ollama, LM Studio, MLX, Open WebUI, llamafile, and adapters. Use when operating private model stacks.
ai-post-training
Post-training and alignment: reward modeling, RLHF/PPO, DPO/DAAs, GRPO, RLVR, RLAIF, over-optimization. Use when adapting an SFT model with preference or verifiable-reward signals.
ai-pretraining
Builds a transformer/GPT and BPE tokenizer from scratch. Use when implementing autograd, self-attention, a nanoGPT-style pretraining loop, or a byte-level tokenizer.
ai-product-operating-model
Designs operating models for AI in product teams. Use when planning platform ownership, provider strategy, data boundaries, evals, or sensitive-data controls.
ai-rag
Designs retrieval-augmented generation and search systems. Use when choosing retrieval, chunking, hybrid search, grounding, or RAG evaluation patterns.
ai-scaling-laws
Sizes models and token budgets using Kaplan/Chinchilla scaling laws. Use when reasoning about compute-optimal N and D, tokens-per-parameter ratios, or over-training tradeoffs.
ai-vector-brain
Builds vector-brain implementations for repos, docs hubs, and compliance corpora. Use when creating pgvector retrieval brains with scripts, SQL, manifests, and evals.
ai-voice-bots
Builds production voice bots and IVR with Python STT/TTS pipelines. Use when designing telephony, streaming audio, latency budgets, or voice quality monitoring.
data-analytics-engineering
Builds analytics engineering layers for metrics, contracts, and BI-ready models. Use when shaping dbt or SQLMesh marts, metric governance, lineage, or data quality.
data-lake-platform
Designs lakehouse platforms across Iceberg, Delta, Hudi, and Paimon. Use when choosing catalogs, CDC paths, query engines, governance, or cost controls.
data-sql-optimization
Diagnoses and tunes SQL for OLTP workloads on PostgreSQL, MySQL, and SQL Server. Use when tuning queries, reading plans, indexing, or fixing lock contention.
data-streaming
Designs streaming platforms for Kafka, Flink, CDC, and lakehouse ingestion. Use when planning event backbones, CDC pipelines, schema governance, or real-time lakehouse delivery.
dev-ai-coding-metrics
Measures AI coding impact and extension robustness. Use when tracking delivery, quality trajectories, cost, experience, pilots, scorecards, or leadership reporting.
dev-api-design
Designs durable API contracts across REST, GraphQL, gRPC, tRPC, and AsyncAPI. Use when specifying interfaces, auth, versioning, errors, rate limits, or agent APIs.
dev-context-code-graph
Builds per-repo code graphs in JSON and markdown-ready derived artifacts. Use when you need blast radius, symbol-level maps, import graphs, inheritance, or test links.
dev-context-engineering
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.
dev-context-multi-repo
Builds multi-repo context hubs and compiled markdown knowledge maps. Use when profiling repo portfolios or assembling LLM-ready cross-repo knowledge bases.
dev-contribution-quality-analysis
Analyzes commit and PR history to score contribution quality objectively. Use when building engineering scorecards, calibrating promotions, or measuring AI-assist impact.
dev-dependency-management
Guides dependency management across languages and ecosystems. Use when choosing package managers, lockfiles, update policy, security scanning, SBOMs, or monorepo patterns.
dev-git-commit-message
Generates or validates Conventional Commits messages from staged diffs. Use when drafting commit messages, checking repo rules, or inferring scope from changed files.
dev-git-workflow
Designs team Git workflows for branching, PRs, and releases. Use when choosing branching models, stacked PRs, merge queues, worktree isolation for agents, or collaboration rules.
docs-notes-retrieval
Builds local-first note vault retrieval for Obsidian, markdown notebooks, and NotebookLM-adjacent exports. Use when packaging notes into LLM-ready context.
document-docx
Create/edit .docx files with styles, templates, comments, and extraction workflows. Use when asked to generate Word reports, contracts, proposals, or convert Word content.
document-pdf
Extracts, creates, and transforms PDF documents. Use when parsing text or tables, generating files, merging pages, or handling PDF forms.
document-pptx
Create/edit .pptx presentations with charts, templates, and speaker notes. Use when asked for pitch decks, QBR decks, or slide automation.
document-xlsx
Create/edit .xlsx spreadsheets with tables, formulas, charts, validation, and workbook automation. Use when asked to generate Excel reports, models, exports, or audit spreadsheets.
foundations-ai-planning-search
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.
foundations-behavioral-economics
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.
foundations-causal-inference
Causal-inference primitives: DAGs, IV, RDD, DiD, synthetic control, propensity, CATE, interference. Use when attributing confounded impact or rollout and LLM-eval confounding.
foundations-consumer-neuroscience
Consumer-neuroscience primitives for attention, arousal, bonding, narrative, memory, and reward. Use when shaping ethical UX, neuro study design, or DMCC/AI Act gates.
foundations-control-theory
Control-theory primitives for PID, MPC, Kalman, stability, anti-windup, dead-time, breakers, and limits. Use when tuning autoscaling, retries, or agent loops.
foundations-cybernetics-vsm
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.
foundations-decision-theory
Decision-theory primitives for uncertain choices, utility, Bayesian decisions, regret, value of information, MCDA, options, and bandits. Use when choosing under uncertainty.
foundations-distributed-systems
Distributed-systems primitives for CAP/PACELC, FLP, Paxos, Raft, clocks, CRDTs, leases, quorums, and broadcast protocols. Use when designing coordination.
foundations-game-theory
Game-theory primitives for strategic decision systems, auctions, mechanism design, incentives, attribution, negotiation, debate, and trust. Use when modeling strategic play.
foundations-grounding-communication
Grounding-theory primitives for human-AI and agent handoffs, common ground, acceptance evidence, repair, and ambiguity. Use when coordinating meaning.
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