chief-ai-officer-advisor

Snowflake SQL, data pipelines (Dynamic Tables, Streams+Tasks), Cortex AI functions, Snowpark Python, and dbt integration. Includes query helper script, reference guides, and troubleshooting.

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End-to-end SLO/SLI/error-budget discipline per Google SRE Workbook. Ships SLO designer (refuses to render without required fields), error-budget calculator with multi-window burn-rate alert thresholds (PromQL-shaped), and SLO reviewer that catches the 7 common bugs. 4 references on principles + SLI design + error budget math + composition with feature-flags-architect/chaos-engineering/kubernetes-operator. Asset templates for SLO YAML and error budget policy. /slo-design slash command. NOT a generic observability skill.

Hypothesis testing, A/B experiment analysis, sample size calculation, and confidence intervals. 3 stdlib-only Python tools: Z-test/t-test/chi-square with effect sizes, sample size calculator with power tradeoffs, and Wilson score confidence intervals.

Google Workspace administration via the gws CLI. Install, authenticate, and automate Gmail, Drive, Sheets, Calendar, Docs, Chat, and Tasks. 5 Python tools, 3 reference guides, 43 built-in recipes, 10 persona bundles.

End-to-end feature-flag discipline: classify, ship, ramp, retire. Detects stale flags as debt, generates phased rollout plans (ring/linear/log/cohort), and audits every flag for a documented kill switch. 3 stdlib Python tools, 4 references on flag taxonomy + provider trade-offs (LaunchDarkly/GrowthBook/Statsig/Unleash/Flipt/DIY) + rollout strategies + lifecycle. /flag-cleanup slash command. Cross-tool compatible.

Production-grade Playwright testing toolkit. 9 skills, 3 agents, 55 templates, TestRail + BrowserStack MCP integrations. Generate tests, fix flaky failures, migrate from Cypress/Selenium.

Autonomous experiment loop — optimize any file by a measurable metric. 5 slash commands (/ar:setup, /ar:run, /ar:loop, /ar:status, /ar:resume), 8 built-in evaluators, configurable loop intervals (10min to monthly).

Enterprise / cross-functional Research Operations domain — the managed counterpart to the academic research/ domain. v2.9.0 ships 5 skills: orchestrator (context: fork) + clinical-research (study design: protocol synopsis + endpoint selection + sample-size/power for means/proportions/survival + phase-gate feasibility) + research-finance (R&D program budgeting with F&A split + burn/runway + capitalize-vs-expense routing + portfolio ROI) + market-research (TAM/SAM/SOM computed both top-down and bottoms-up + survey sampling with FPC and per-segment minima + Kotler segmentation scoring) + product-research (goal-matched study design + method-based saturation with confidence + insight synthesis that flags single-source anecdotes). Hard rules: clinical outputs are estimates with a named clinical owner (never fact), finance outputs surface assumptions and route capex-vs-opex to a named finance owner (never auto-decide), market sizes show method + assumptions (never a single number), product insights require recurrence across independent participants. Each sub-skill ships per-skill onboarding questions (onboard.py), a customization config consumed by every tool, and an isolated opt-in autoresearch evaluator (ar_evaluator.py) bridging to engineering/autoresearch-agent. 24 stdlib Python tools (12 analysis + 12 onboarding/customization/autoresearch), 12 reference docs. Distinct from ra-qm-team (regulatory/QM submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), marketing-skill (campaign analytics).

Patent prior-art + IP landscape skill. FTO/novelty/family-resolver via 3-pass Jaccard heuristic. Research-pack convention. Path-B from megaprompt 12.