
Use when designing or extending a static analyzer — a linter, a taint tracker, a contract checker, an entity extractor, a governance rule engine, or any tool that reads source code and produces verdicts about it without running the program. Use when adding typed dataflow (taint, ownership, capability) to a system that currently does pattern matching, when an inherited analyzer cannot be extended cleanly, when suppressions are growing faster than rules, or when choosing between static and runtime enforcement of a property. Engineering pack — how to build the analyzer. For consuming an existing analyzer's output to map a codebase, use `/system-archaeologist` instead.
Use when working on a Python task and unsure which specialist to load - routes to type-system, mypy-resolution, project-tooling, delinting, testing, async, scientific-computing, ML-workflows, debugging-and-profiling, or Textual-TUI specialists based on symptoms
Use when making architectural decisions without documentation, skipping risk analysis, accepting risks without mitigation, or treating governance as optional bureaucracy - enforces mandatory DAR/RSKM based on project risk level
Use when working in Rust — routes to specialist sheets for borrow-checker errors (E0502/E0597/E0382), trait bounds (E0277), async Send/Sync, clippy warnings, unsafe/FFI soundness, performance profiling, or PyO3/candle interop
Use when starting new projects with CMMI, adding CMMI to active development, preparing for audits, migrating tools, or facing team resistance to process adoption
Use when implementing CMMI processes in GitHub or Azure DevOps, migrating between platforms, or establishing traceability/compliance on GitHub/Azure - platform-specific process guidance
Use when building web APIs, backend services, or encountering FastAPI/Django/Express/GraphQL questions, microservices architecture, authentication, or message queues - routes to 11 specialist skills rather than giving surface-level generic advice
Use when you have architecture documentation from system-archaeologist and need critical assessment, refactoring recommendations, or improvement prioritization - routes to appropriate architect specialist skills
Use when starting any UX/UI design task — visual design, IA, interaction, accessibility (WCAG 2.2), user research across mobile, web, desktop, game, and AI surfaces, plus first-principles audience-derived needs analysis. Routes to 11 specialist sheets.
Use when managing multi-document sets as wikis - manifest-driven architecture, reading paths, derivation discipline, terminology/claim consistency, change evolution, and governance for standards suites, policy frameworks, and technical documentation collections
Routes to appropriate deep-RL skills based on problem type and algorithm family
Router skill directing to deployment, optimization, MLOps, and monitoring guides.
Use when defining requirements, tracking traceability, managing requirement changes, or establishing RTM - covers elicitation, analysis, specification across CMMI Levels 2-4
Route AI/ML tasks to correct Yzmir pack - frameworks, training, RL, LLMs, architectures, production
Use when working on LLM applications — chat / instruct prompting, reasoning models (o-series / Claude extended thinking / DeepSeek-R1 / Gemini thinking / Qwen QwQ), agentic patterns + MCP, RAG, fine-tuning (SFT / DPO / IPO / KTO / SimPO / ORPO / GRPO + LoRA family), context engineering and prompt caching, inference optimization (vLLM / SGLang / TensorRT-LLM), evaluation (incl. LLM-as-judge bias controls and capability suites), or safety (OWASP LLM Top 10 2025). Calibrated to 2026-05 with capability-tier vocabulary (frontier-reasoning / frontier-general / fast-cheap / on-device) instead of hardcoded model IDs. Routes to the right specialist sheet.
Use when writing or improving documentation - ADRs, APIs, runbooks, READMEs, architecture docs, security/compliance docs, post-mortems, register review or translation (technical/policy/government/public-facing/executive/academic), fact-checking research papers, or surgical edits on large (>=2000 line) files
Use when selecting or comparing neural architectures - routes by data modality (vision / sequence / graph / generative / multimodal) and constraints (dataset size, compute, latency); covers CNNs (ConvNeXt v2 / EfficientNetV2), Transformers + MoE (Mixtral / DeepSeek), SSM/Mamba, modern diffusion (SDXL/FLUX/DiT), multimodal (CLIP/SigLIP/LLaVA), SAM/SAM-2, equivariant GNNs, normalization, attention variants
Use when encountering training problems (loss not decreasing, instability, NaN, overfitting, slow training) or selecting training dynamics (optimizer / LR / schedule / batch / precision / clipping / regularization) — routes to specialist sheets for the 2026-era landscape (Lion, Sophia, Muon, AdEMAMix, Schedule-Free, Prodigy, AdamW8bit, paged optimizers, WSD schedules, FP8 / BF16 precision, muP / mu-Transfer, ZeRO/FSDP strategy).
Use when designing or reviewing security architecture - threat modeling (STRIDE), defense-in-depth controls, secure-by-design patterns, LLM/AI security (OWASP LLM Top 10, MITRE ATLAS), supply-chain security (SLSA/SBOM/Sigstore), compliance mapping (NIST CSF/ISO 27001/PCI-DSS/GDPR), classified-systems MLS, and government authorization (ATO/RMF/FedRAMP).
Use when fact-checking a research paper or document - dual-verified web search with claim extraction, independent researcher and adversarial verifier, structured JSON output and human-readable exception report. Invoke explicitly via /fact-check; expensive operation.
Use when analyzing existing codebases to generate architecture documentation - coordinates subagent-driven exploration with mandatory workspace structure, validation gates, and pressure-resistant workflows
Use when designing or critiquing the structure of a staged procedure — a wizard, configuration flow, troubleshooting tree, training curriculum, multi-stage approval pipeline, decision pipeline, or any decomposition of expert work into composable stages. Use for both producer work (build the decomposition) and critic work (audit a proposed decomposition). Use when reasoning about capacity, bottlenecks, or soundness of a procedural flow. Do not use for implementation-plan critique of code changes (use `/axiom-planning` instead), for execution-time dynamics (use `/simulation-foundations`), or for rendering an already-designed procedure as docs or UI (use `/technical-writer` or `/ux-designer`).
Use when designing a system whose past behaviour must be recoverable as a fact — RL training substrates, multi-agent simulations, deterministic game engines, replay-debuggable services, multiplayer lockstep, or any pipeline where "I cannot reproduce that bug" is unacceptable. Use when teams disagree about what "snapshot", "seed", or "replay" mean across modules. Use when cross-machine or cross-process determinism is required, or a regulator will ask "what was the input at tick T?". Architecture-level — how to design a deterministic system. For verifying an existing simulation against known patterns, use `/check-determinism` from yzmir-simulation-foundations instead.
Use when designing, implementing, or auditing an MCP (Model Context Protocol) server — tool API design, idempotency under agent retry, structured error envelopes agents can recover from, schema versioning across model drift, transport reliability (stdio / HTTP), output-shape and pagination discipline, and choosing between tools / resources / prompts / sampling. Also use when an MCP server's tools confuse agents, return unstructured errors, deadlock under concurrent calls, double-execute under retry, or lose state across reconnects. Do not use for general REST/GraphQL API design (use `/web-backend`), for client-side prompt engineering or tool-loop design (use `/llm-specialist`), for general in-process plugin architecture (use `/system-architect`), or for cryptographic-provenance audit trails (use `/audit-pipelines`).
Use when running **SQLite or DuckDB inside an application process** as the durable store — not as a development convenience but as the production database. Use when scaling an SQLite layer that worked at low concurrency and is now hitting SQLITE_BUSY, WAL bloat, lock contention, schema-migration ceremony, or correctness gaps under multi-process writers. Use when introducing DuckDB as an OLAP complement to an OLTP SQLite store, or when picking between the two for a new component. Pairs with `/web-backend` (the API surface above the DB) and `/audit-pipelines` (when the DB is also the audit trail). Do not load for server databases (Postgres, MySQL), key-value stores, or ORM choice in isolation.
Use when the user wants to draft fiction or creative nonfiction prose, get craft critique on prose they have written, or plan story structure, outline, or premise. Workshop-voiced. Three explicit modes (draft, critique, plan) and the router will refuse to begin work without a declared mode.
Use when deciding test strategy, struggling with code reviews, shipping without tests, or conflating verification with validation
Use when making architecture decisions, setting up CI/CD, managing technical debt, or choosing branching strategies - enforces ADR requirements and prevents resume-driven design
Router for systems-as-experience - emergence, sandbox, optimization, discovery, narrative, modding
Routes to appropriate PyTorch specialist skill based on symptoms and problem type
Router for systems thinking methodology - patterns, leverage points, archetypes, stocks-flows, causal loops, BOT graphs
Router for simulation math - ODEs, state-space, stability, control, numerics, chaos, stochastic
Use when an RL controller decides whether/when/how to mutate a network's topology during training - growing seeds, grafting modules, retiring underperformers - and you need controller action/observation/reward design, governor and safety-gate discipline, rollback-as-RL-signal shaping, deterministic replay across topology change, or growth-aware ablation/evaluation.
Mandatory protocol for all SME (Subject Matter Expert) agents. Defines fact-finding requirements, output contracts, confidence/risk assessment, and qualification of advice.
Use when creating professional documents, PDF reports, Typst templates, Pandoc conversions, or any task where document visual design quality matters
Use when user asks about E2E testing, performance testing, chaos engineering, test automation, flaky tests, test data management, or quality practices - routes to specialist reference sheets with deep expertise instead of providing general guidance
Use when maintaining, enhancing, or modifying existing Claude Code plugins - handles skills, commands, agents, hooks, and reference sheets through systematic domain analysis, structure review, behavioral testing, and quality improvements
Use when a system makes procedural decisions that must be defensible after the fact — rule firings, governor decisions, state transitions, gate verdicts, eligibility determinations, automated approvals — and you need a verifiable provenance chain rather than ordinary observability. Use when an auditor, regulator, customer, or court might later ask "prove it" about a decision the system made. Use when retention, redaction, or right-to-be-forgotten obligations conflict naively with append-only storage. Do not use for ordinary observability or for system-level threat modelling.
Use when working in a Rust **cargo workspace** — multiple crates under one `[workspace]` root, shared `Cargo.lock`, shared target dir. Use when promoting a single-crate project to a workspace, when crates have drifted into per-crate dependency versions / lint configs / licence policies, when feature-unification surprises produce different binaries from `cargo build` vs `cargo build -p`, or when deciding which crates to publish vs keep private. Pairs with `/rust-engineering` — that pack is single-crate-shaped; this pack composes those concerns at workspace scale. Do not load for a single-crate project.
Use when designing or implementing static websites for developer tools, open-source projects, or technical documentation — information architecture, HTML/CSS, design tokens, developer UX patterns
Use when building networks that grow, prune, or adapt topology during training. Routes to continual learning, gradient isolation, modular composition, and lifecycle orchestration skills.
Use when building or maintaining a **PyO3 extension module** — a Rust crate exposed to Python as an importable extension. Use when scaling a PyO3 prototype that worked at low call rates and is now in a hot loop (RL self-play, batched inference, real-time pipeline). Use when the boundary symptom is GIL contention, segfault on import or exit, NumPy view pointing at freed memory, traceback truncated to one frame, async hang, or a wheel that imports on Mac and crashes on Linux. Pairs with `/rust-engineering` (single-crate Rust) and `/rust-workspaces` (multi-crate Rust). Do not load for pure-Python work or for Rust crates with no Python surface.
Use when facing complex engineering challenges that require systematic methodology - horrible bugs, safe refactoring, code review, production incidents, technical debt decisions, or building confidence in unfamiliar codebases
Use after implementation-planning to validate plans against codebase reality, risk, complexity, and project conventions before execution
Routes a design brief, HLD, epic, or brownfield change through the full solution-architecture workflow — triage, NFR quantification, tech selection, ADRs, traceability, integration/migration, optional TOGAF/ArchiMate, and consolidated SAD with consistency gate
Use when setting up CI/CD pipelines, experiencing deployment failures, slow feedback loops, or production incidents after deployment - provides deployment strategies, test gates, rollback mechanisms, and environment promotion patterns to prevent downtime and enable safe continuous delivery
Use when you have specifications or requirements for multi-step implementation tasks requiring comprehensive documentation for handoff
Use when establishing measurement programs, analyzing metrics with statistical process control, setting baselines, or implementing CMMI Level 4 quantitative management - prevents vanity metrics and measurement theater
Orchestrate a simulated reader panel review — spawn persona-reader agents as teammates, manage the chapter pump, enforce the Step A gate, and coordinate synthesis
Use when users request SDLC guidance, CMMI processes, requirements management, design documentation, quality assurance, governance, metrics, or adopting processes on existing projects
Router skill - analyze requirements and direct to appropriate tactics