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Found 18 Skills
Use when the user wants to orchestrate defect image generation, run associated setup, or handle outputs on OSMO. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint.
Pre-indexed code knowledge graph (MCP, SQLite + tree-sitter) for faster, lower-token exploration of brownfield codebases. Use when starting work on a repo larger than ~500 files or when the task involves cross-file traversal — "where is X used", "what calls Y", "what breaks if I change Z", "trace flow from A to B", "explain this subsystem". Skip for single-file edits or sessions shorter than the cold-start cost. Triggers include "codegraph", "code graph", "index this repo", "where is X defined", "find callers of", "callees of", "blast radius of changing X", "explore this codebase". Replaces grep + Read loops with O(1) SQLite lookups and FTS5 search via 8 MCP tools.
Fast native networking primitives for React Native built on Nitro Modules — react-native-nitro-fetch, react-native-nitro-websockets, and react-native-nitro-text-decoder. Covers the fetch API, global replacement, prefetching and cold-start cache warming, the NitroWebSocket class and pre-warming, migrating from React Native's built-in WebSocket, the in-process NetworkInspector, native Perfetto / Instruments tracing, the native TextDecoder, and plugging nitro-fetch into axios via a custom adapter.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Generate a resumable handoff document from an in-progress conversation, review, debugging session, or investigation. Dispatches co-located subagents to extract original instructions and Q&A context, capture evidence-backed insights, optionally validate claims from tracking files, and assemble a cold-start-ready handoff file plus structured working artifacts. Use when the user says "create a handoff doc", "save this for later", "document what we found", "update the resumption file", or wants a fresh agent to resume later without relying on chat history.
Generate a persistent .nexus-map/ knowledge base that lets any AI session instantly understand a codebase's architecture, systems, dependencies, and change hotspots. Use when starting work on an unfamiliar repository, onboarding with AI-assisted context, preparing for a major refactoring initiative, or enabling reliable cold-start AI sessions across a team. Produces INDEX.md, systems.md, concept_model.json, git_forensics.md and more. Requires shell execution and Python 3.10+. For ad-hoc file queries or instant impact analysis during active development, use nexus-query instead.
Specialized skill for building production-ready serverless applications on GCP. Covers Cloud Run services (containerized), Cloud Run Functions (event-driven), cold start optimization, and event-driven architecture with Pub/Sub.
AWS CloudFormation patterns for Lambda functions, layers, event sources, and integrations. Use when creating Lambda functions with CloudFormation, configuring API Gateway, Step Functions, EventBridge, SQS, SNS triggers, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for cold start optimization.
Provides AWS Lambda integration patterns for Python with cold start optimization. Use when deploying Python functions to AWS Lambda, choosing between AWS Chalice and raw Python approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless Python applications. Triggers include "create lambda python", "deploy python lambda", "chalice lambda aws", "python lambda cold start", "aws lambda python performance", "python serverless framework".
Provides AWS Lambda integration patterns for PHP with Symfony using the Bref framework. Use when deploying PHP/Symfony applications to AWS Lambda, optimizing cold starts, configuring API Gateway integration, or implementing serverless PHP applications with Bref. Triggers include "create lambda php", "deploy symfony lambda", "bref lambda aws", "php lambda cold start", "aws lambda php performance", "symfony serverless", "php serverless framework".
Provides AWS Lambda integration patterns for TypeScript with cold start optimization. Use when deploying TypeScript functions to AWS Lambda, choosing between NestJS framework and raw TypeScript approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless TypeScript applications. Triggers include "create lambda typescript", "deploy typescript lambda", "nestjs lambda aws", "raw typescript lambda", "aws lambda typescript performance".
AWS Lambda, Vercel Edge Functions, Cloudflare Workers, cold starts, deployment patterns, and infrastructure as code (SST, Serverless Framework). Use when building serverless applications or optimizing function-based architectures.