Use this agent when you need to review Python code changes with an extremely high quality bar. This agent should be invoked after implementing features, modifying existing code, or creating new Python modules. The agent applies Kieran's strict Python conventions and taste preferences to ensure code meets exceptional…
Use this agent when you need to review TypeScript code changes with an extremely high quality bar. This agent should be invoked after implementing features, modifying existing code, or creating new TypeScript components. The agent applies Kieran's strict TypeScript conventions and taste preferences to ensure code…
Use this agent when you need to analyze code for design patterns, anti-patterns, naming conventions, and code duplication. This agent excels at identifying architectural patterns, detecting code smells, and ensuring consistency across the codebase.
Use this agent when you need to analyze code for performance issues, optimize algorithms, identify bottlenecks, or ensure scalability. This includes reviewing database queries, memory usage, caching strategies, and overall system performance. The agent should be invoked after implementing features or when performance…
Perform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go). Do not…
Use this agent when you need to perform security audits, vulnerability assessments, or security reviews of code. This includes checking for common security vulnerabilities, validating input handling, reviewing authentication/authorization implementations, scanning for hardcoded secrets, and ensuring OWASP compliance.
Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling.…
This skill should only be used when the user uses the word workflow and asks to review changes (select mode = light, light-plus, heavy) with verification and context handoff/pickup to avoid context rot.
This skill should be used when a user needs a demo package that starts with a live demo (not slides) and produces both a demo script and a navigable single-file HTML runbook, including caveats, respectful competitive comparison, and LLM architecture details (RAG, evals, observability).
Use this agent when a PR touches production data, migrations, or any behavior that could silently discard or duplicate records. Produces a concrete pre/post-deploy checklist with SQL verification queries, rollback procedures, and monitoring plans. Essential for risky data changes where you need a Go/No-Go decision.
This skill should only be used when the user uses the word workflow and asks to release or ship changes with a release checklist, verification, and clean handoff/pickup boundaries.
Capture solved problems as categorized documentation with YAML frontmatter for fast lookup. Use when a non-trivial problem is solved and worth reusing.
Build a custom browser-based annotation interface tailored to your data for reviewing LLM traces and collecting structured feedback. Use when you need to build an annotation tool, review traces, or collect human labels.
Help the user systematically identify and categorize failure modes in an LLM pipeline by reading traces. Use when starting a new eval project, after significant pipeline changes (new features, model switches, prompt rewrites), when production metrics drop, or after incidents.
Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch…
Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling…
Calibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with standard unit tests).
Design LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests). Do NOT use when you need to…
Expert guidance for creating, writing, and refining Claude Code Skills. Use when working with SKILL.md files, authoring new skills, improving existing skills, or understanding skill structure and best practices.
This skill should be used when fixing incorrect SKILL.md files with outdated instructions or APIs.
★not rated 5 6mo agoA22 tokens
originalMIT
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: