Use when designing or auditing long-term memory for persistent AI agents. Stores immutable source evidence before canonical facts, links beliefs to provenance, applies novelty-aware ADD/UPDATE/NOOP write gating, keeps uncertain merges explicit, separates retrieval from answer generation, supports deletion, and evaluates memory failures layer by layer.
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Writes and reviews dbt projects targeting Snowflake: models, schema.yml metadata, sources, tests, macros, and project structure. Use when building or reviewing dbt models, generating dbt YAML, or applying dbt best practices.
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Use when working with Entity Framework Core — modeling entities and relationships, migrations, LINQ query performance, change tracking, N+1 problems, and connection and transaction handling.
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Grounds every implementation decision in official documentation. Use when you want to verify an approach against the official docs before implementing it, or when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters.
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Refactors and reviews SwiftUI views: consistent layout ordering, subview extraction, dependency injection, Observation, and MV versus view-model patterns. Use when SwiftUI views have grown large or inconsistent.
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This skill should be used when the user asks to "optimize context", "reduce token costs", "improve context efficiency", "implement KV-cache optimization", "partition context", or mentions context limits, observation masking, context budgeting, or extending effective context capacity.
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Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations. Use before deploying or after a cost spike in a Python pipeline that queries BigQuery.
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Step-by-step guide for capturing key application requirements for NoSQL use-case and produce Azure Cosmos DB Data NoSQL Model design using best practices and common patterns, artifacts_produced: "cosmosdb_requirements.md" file and "cosmosdb_data_model.md" file. Use when designing a new Azure Cosmos DB for NoSQL data model or reviewing partition keys and document shapes.
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Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.
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Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit code on the current branch.
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Deep, evidence-first review of GitHub bugs and PRs: reads the code before forming opinions, finds root causes, decides whether an issue is stale, duplicate, or real, and proposes the best fix. Use when an issue or PR needs real investigation rather than a summary.
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Use when completing tasks, implementing major features, or before merging to verify work meets requirements
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Use when starting a coding session, setting up a project for AI-assisted development, switching between parts of a codebase, or when agent output quality degrades — hallucinated APIs, wrong patterns, ignored project conventions. Structures what the agent loads and when.
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Use when making an architectural decision, choosing between competing approaches, adding or changing a public API, shipping a user-facing behavior change, or repeatedly explaining the same thing. Captures the why — context, constraints, alternatives — as ADRs and project docs. Not for restating obvious code.
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Use on every code change — commit structure, branching, history hygiene, and versioning. Apply when committing work, opening or splitting a branch, writing commit messages, cutting a release, or recovering from a bad history state.
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Reverse-engineers and debugs macOS and iOS binaries with Hopper Disassembler and LLDB: ObjC/Swift symbols, dyld loading, framework internals, and crash investigation from disassembly. Use when private API behavior or a runtime crash needs to be understood at the binary level.
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Extracts what the user actually wants instead of what they think they should want. Achieves this through one-question-at-a-time interview until ~95% confidence about the underlying intent. Use when an ask is underspecified ("build me X" without "for whom" or "why now"), when the user explicitly invokes ("interview me", "grill me", "are we sure?", "stress-test my thinking"), or when you catch yourself silently filling in ambiguous requirements before any plan, spec, or code exists.
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Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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Cloud design patterns for distributed systems architecture covering 42 industry-standard patterns across reliability, performance, messaging, security, and deployment categories. Use when choosing architecture patterns for reliability, performance, messaging, or security in a distributed cloud system.
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Build and operate Python Azure IoT Edge modules with robust messaging, deployment manifests, observability, and production readiness checks. Use when writing, deploying, or hardening a Python module for Azure IoT Edge, including telemetry filtering and offline retries.
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Design and plan end-to-end Azure IoT and Smart City solutions: requirements, architecture, security, operations, cost, and a phased delivery plan with concrete implementation artifacts. Use when planning an IoT or Smart City platform on Azure (traffic, lighting, waste, sensors) from requirements to a delivery roadmap.
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When user is asking for guidance for which role to assign to an identity given desired permissions, this agent helps them understand the role that will meet the requirements with least privilege access and how to apply that role
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Use when writing, editing, or reviewing code, READMEs, docs, commits, changelogs, or any repo content. Pre-2020 human style — terse, opinionated, restrained, with project voice. Blocks the LLM tics (emoji headers, "leverage", "robust", "seamless", "delve", defensive bloat, type-hint overkill, marketing prose, "Whether you're X or Y", verbose error messages) catalogued from cpython, requests, flask, git, ripgrep, express, kubernetes, serde, and the Go stdlib. Triggers on "anti-ai", "human-style", "no slop", "write it like a human", or any time code/docs should look hand-written.
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Enforce output quality, evidence verification, and quality gates across security audits. Use this skill when you perform SAST, SCA, threat modeling, or security code reviews. Use this skill to verify code locations and taint traces for all findings. Use this skill to prevent the suppression of valid security findings. Use this skill to evaluate reports against quality thresholds (score ≥ 8/10) before delivery.
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