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Analyze BigQuery usage, identify cost hotspots, repeated failures, and practical optimization opportunities.

Use when creating new skills, editing existing skills, or verifying skills work before deployment

Process-first advisor routing with artifact capture

Use when a Codex or generic local agent needs a focused second opinion from Claude CLI without MCP routing.

Use when connecting to a managed codebase-context MCP/session service, checking stale maps, or safely using MCP-provided repository context.

Use when optimizing agent runtime loops, card packs, MCP session lifecycle, tool-call count, or multi-agent orchestration patterns.

Relay questions and tasks to a persistent local Gemini ACP session with cross-turn context.

Use to audit and remove unused or redundant third-party dependencies from package manifests.

Use continuously during long tasks. Teaches how to read less, output less, and keep the LLM context window lean and fast.

Check existing repo capability, external libraries, MCP options, and maintenance risk before writing custom code. Decide adopt/wrap/build with explicit criteria.

Run an anti-slop cleanup workflow on AI-generated code — regression-tests-first, smell-by-smell, behavior preserved.

Use when designing or using MCP-backed structured code search with search, AST query, symbol inventory, and bounded extraction workflows.

Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost controls.

Deterministic 3-cycle loop for gathering codebase context before acting. Broad search → exact source and tests → target-specific docs and setup. Stop after enough context or report what is still missing.

Deterministic 3-cycle loop for gathering codebase context before acting. Broad search → exact source and tests → target-specific docs and setup. Stop after enough context or report what is still missing.

Use when creating or reviewing red-team eval plugins, attack templates, grader rubrics, safety fixtures, or model-risk test metadata.

Use when querying, ingesting, or maintaining a local RAG MCP corpus for semantic document retrieval with privacy controls.

Use when diagnosing agent session history, interrupted tool loops, missing tool results, timing bottlenecks, or subagent trace correlation.

Use when designing, running, debugging, or hardening deterministic eval suites for agent skills, prompts, tool workflows, or MCP-backed cases.

Design agent tool sets with stable names, narrow schemas, deterministic output shapes, and explicit error paths. No catch-all tools unless unavoidable.

Vendor-neutral routing guide for choosing the right model tier by task type. Mechanical work uses a smaller/faster model; implementation uses a standard model; architecture, security, and release audit use the most capable model.

Multi-model orchestration — route to two external advisors, then synthesize

Use to collapse over-engineered abstractions, remove unnecessary layers, or consolidate redundant logic.

Analyze BigQuery usage, identify cost hotspots, repeated failures, and practical optimization opportunities.

Use when adding or reviewing data quality tests for dbt models in warehouse-backed analytics projects. Covers dbt generic tests, singular tests (assert_*.sql), accepted-values macros, dbt_utils patterns, cross-system consistency tests, and warehouse-oriented validation. Use when writing data tests, creating assert_*.sql files, testing business logic, or validating referential integrity.

Use when creating or modifying dimensional dbt models in warehouse-backed analytics projects. Covers a four-layer warehouse architecture (sources/staging/core/marts), naming conventions, no-alias SQL rule, surrogate-key and missing-record patterns, incremental strategies, deduplication, and common project macros. Use when building fact tables, dimension tables, staging models, writing SQL, or desi

Evaluate metric and semantic model changes for BI/reporting breakage and business meaning drift.

Use when modifying dbt metrics or semantic models to ensure mathematical correctness and backwards compatibility.

Review SQL for business logic correctness, semantic drift, aggregation risk, and silent definition changes.

Deterministic 3-cycle loop for gathering codebase context before refactoring or maintenance work. Broad search → exact source and tests → config and build setup.

Use when migrating APIs, libraries, or patterns across a large codebase. Ensures safe, step-by-step progress rather than risky mega-commits.

Use to restructure code while guaranteeing that all existing tests continue to pass.

Use to identify and safely delete unused functions, classes, exports, and files.

Use to enforce consistent naming conventions and file structures across a project without changing business logic.

Use when code needs restructuring without changing observable behavior.

Coordinate multi-system deployment steps as a review-first runbook with explicit approval before any external write or promotion action.

Audit whether a dbt incremental model uses the right incremental strategy for the repo, the data shape, and the operational constraints.

Use after fixing a bug to generate a blameless post-mortem summary for human review.

Inspect changed dbt assets, estimate blast radius, identify missing tests, and recommend the narrowest safe validation plan.

Use when reproducing, isolating, and fixing a bug with the smallest safe change.

Use when confronted with an unknown failure in CI or production to rapidly categorize the issue before deep debugging.

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

Use when conversation context is bloated, constraints are being forgotten, or a compact handoff is needed before continuing work.

Use to diagnose tests that pass and fail intermittently without code changes.

Use when a bug was recently introduced but you don't know which commit caused it.

Deterministic 3-cycle loop for gathering codebase context before acting. Broad search → exact source and tests → target-specific docs and setup. Stop after enough context or report what is still missing.

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

Verify that API endpoints match their OpenAPI/Swagger specifications.