Data & AI skills

VictoriaLogs provider for obz. Supports log search using LogsQL, a purpose-built query language with _msg, _time, and _stream filters plus pipes for transformation. This skill should be used when the user mentions "VictoriaLogs", "LogsQL", "obz log search -p vl", or needs to search logs from a VL backend.

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Query Strava fitness data including activities, athlete stats, segments, routes, clubs, and gear. Use when the user asks about cycling, running, swimming, workouts, training, or Strava data.

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DNS C2 operator skill for the AgentCore sandbox breakout demo. This skill should be used when the user asks to run C2 verification, attack a victim chatbot, send commands to a compromised session, retrieve exfiltrated data, or interact with the DNS C2 system.

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Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box review.

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Use this when setting up a GitHub repo to push its Nix builds to Juspay's shared OSS Attic cache (cache.nixos.asia/oss) — adds the substituter to flake.nix, a nix-cache.yml GitHub Actions workflow, and prompts for the ATTIC_TOKEN secret.

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Fine-tune large language models — data curation, SFT, parameter-efficient methods (LoRA), evaluation, and avoiding common training failures. Use when a base model needs task-specific behavior or domain knowledge.

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Build evaluation frameworks for LLM systems — harness design, graders, datasets, regression tracking, and human-in-the-loop eval. Use when you need systematic measurement of model or agent quality.

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Statistical analysis of extreme weather — EVT, return periods, thresholds, and detection/attribution of rare events.

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Programmatic form completion — web forms, PDF AcroForms, and data mapping — use when automating repetitive form filling.

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fMRI data analysis — preprocessing, GLM modeling, and group-level inference for task and resting-state studies.

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Writing figure captions that make figures self-contained — what to include, ordering, and statistical reporting.

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Work with text embeddings — choosing models, pooling, similarity measures, evaluation, and multilingual considerations. Use when building semantic search, clustering, or any similarity-based feature.

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Generate and edit images with diffusion models: prompting, seeds, CFG, img2img, inpainting, conditioning, and API integration. Use when adding AI image generation to apps or workflows.

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Compare files, text, and data effectively with diff strategies, semantic comparison, and review workflows.

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Elasticsearch search and analytics — mappings, queries, and cluster health — use when building search or log analytics.

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Distill large models into smaller ones — teacher-student training, data generation, loss design, and quality validation. Use when you need big-model behavior at small-model cost.

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Building robust document ingestion — chunking, metadata, and multi-format pipelines for search and RAG.

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Build rich visualizations with Apache ECharts: option config, dataset transforms, large-data performance, and custom rendering. Use for complex dashboards and big datasets.

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Electronic health record analysis — phenotyping, missingness, bias, and observational study design with EHR data.

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Drizzle ORM guidance — schema-as-code, relational queries, migrations, and SQL-first TypeScript data access.

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Analyzing contact tracing data — network reconstruction, transmission chains, and evaluating tracing performance.

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Manage ML datasets with the datasets ecosystem — loading, streaming, preprocessing, versioning, and sharing. Use when data handling (not modeling) is the bottleneck.

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Run the data science workflow end to end — framing, exploration, feature engineering, modeling, validation, and communicating results. Use when turning raw data into decisions or predictions.

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Manage LLM context windows deliberately — budgeting, prioritization, summarization, and retrieval instead of dumping everything in. Use when prompts grow long, costs rise, or the model loses track of key details.

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