CI/CD pipeline design and GitHub Actions workflow authoring: pipeline architecture, testing stages, deployment strategies, caching, secrets management, security hardening, performance optimization. Triggers: creating CI/CD pipelines, writing GitHub Actions workflows, configuring automated testing, setting up deployment automation, debugging workflow failures, optimizing pipeline performance, reviewing CI/CD config, designing deployment pipelines, implementing build automation.
Application deployment: Docker Compose (watch mode, profiles, GPU passthrough, health checks), PaaS, cloud containers, Kubernetes, AI-native GPU platforms, reverse proxy, secrets management, AI/ML model serving. Triggers: deploying an app, writing compose.yaml or Dockerfile, choosing a hosting platform, configuring Caddy/nginx, deploying Ollama/vLLM, GPU passthrough, choosing Render/Railway/Fly.io/ Vercel, managing Railway environments or PR environments, wiring Railway's agent plugin/MCP/CLI, deploying to Cloud Run/ECS/Modal/CoreWeave, writing systemd units for Compose. Siblings: cicd-engineer (pipeline automation), observability (monitoring).
Planning complex software tasks via a three-document model (IMPLEMENTATION_PLAN.md, WIP.md, LEARNINGS.md) in small, known-good increments. Triggers: starting significant development, breaking down complex features, architecture planning, multi-session projects, feature breakdown, work planning, task tracking, WIP management, incremental development. Language modules available for Python, TypeScript.
Behavioral specification methodology with requirement traceability for medium and large features: spec format (When/and/the system/so that), complexity triage (trivial through spike), REQ ID conventions, REQ-* traceability threading through the pipeline, decision documentation, spec archival. Triggers: behavioral specifications, requirement traceability, SDD methodology, spec format, REQ IDs, spec-driven development, complexity triage, spec archival.
Interpret and act on the Agent Readiness score produced by /project-metrics: 8 Factory pillars × 5 maturity levels × 80%-per-level gate + Praxion-native Pillar 9 Manageability sub-score. Read the readiness block from METRICS_REPORT_*.json, understand the LLM-judged criteria (naming_conventions, test_quality, readme_quality, docs_agent_friendliness), prioritize remediation by failing criterion → pipeline handoff, and interpret level-by-level progress. Triggers: reading or acting on an agent readiness report, understanding why a project scored a given level, deciding how to improve readiness, interpreting mechanical vs LLM criteria, running readiness without an API key, grounding a new readiness run on a prior report.
Task-intake protocol for turning a fuzzy user request into an unambiguous, verifiable goal before committing to a direction. Covers the ask-vs-proceed decision rule (the 2x2 of intent-clarity x reversibility), lexical ambiguity detection, the XY-problem test, assumption surfacing over interrogation, Mom-Test question phrasing with a hard 3-question cap, and capturing measurable outcome signals (Intent / Key Signals / Health Guards / Uncertainty Flag) into a TASK_BRIEF.md that seeds downstream architecting, planning, testing, and verification. Triggers: at the start of any non-trivial task when the request is vague, implementation-first, or its success is not measurable; deciding whether to ask the user a clarifying question or proceed with stated assumptions; capturing acceptance criteria / definition-of-done at intake; reducing rework from misread intent.
AI agent evaluation: Inspect AI/DeepEval/Promptfoo framework selection, golden datasets, LLM-as-judge, grader design, scoring, non-determinism handling, CI/CD integration. Triggers: evaluating agent behavior, choosing an eval framework, designing eval suites, building golden datasets, trajectory evaluation, eval-driven development, integrating evals into CI/CD. Python-focused; TypeScript coverage (Vitest + Promptfoo) in references/typescript.md.
CLI/TUI design craft: clig.dev contract, output discipline (data→stdout, messages→stderr, NO_COLOR, --json), help-text levels, three-part error messages (what/why/fix), TTY detection, exit codes, TUI frameworks (Python textual+rich, Node Ink, Go Charm/Bubble Tea+Lip Gloss), render discipline (diff-before-render, DECSET 2026, stream-as-it-arrives), terminal accessibility. Triggers: designing/ reviewing CLI tools, agent terminal output, TUIs, help text, error messages, exit codes, choosing a TUI framework. Not for web UI (web-ui-design) or agent-tool design (api-design-craft, agentic-interface-design).