claude-api
claudeClaude API and SDK documentation skill for building LLM-powered applications
19 skills
Scaffolding, running, documenting, and publishing AI evaluations. Ships skills and commands for setting up eval workspaces, creating custom evals (or adapting existing frameworks/benchmarks), running them, and publishing evals or datasets. Bundles a curated ground-truth list of open-source eval tools and benchmarks as a reference data source.
Not in a marketplace yet, so it loads for one session with --plugin-dir.
Plugin eval-runner in danielrosehill/Claude-Eval-Runner-Plugin. Commands read from the repo's manifest — 1 month ago.
create-evalDesign a custom eval from scratch, or remix an existing benchmark. Use when the user wants to define the eval itself — task framing, dataset composition, scoring rubric, and reporting format — rather than simply wiring up a framework. Produces a fully specified eval definition ready to be run.
new-workspaceProvision a new eval-runner workspace on disk. Use when the user wants to start a new evaluation project — scaffolds evals/, datasets/, results/, and docs/ directories, personalises CLAUDE.md, and (by default) creates a GitHub repo.
publish-datasetPublish an eval dataset to Hugging Face Hub (or GitHub as a fallback). Use when the user wants to share the inputs/labels used by an eval — with a dataset card, licensing, splits, and a content hash so downstream runs can verify integrity.
publish-evalPublish an eval (definition + results) so others can reproduce it. Use when the user wants to share an eval publicly — as a GitHub repo, Hugging Face space, or a standalone writeup. Produces a clean, self-contained bundle with README, task spec, rubric, dataset pointer, and a run report.
run-evalExecute an eval defined in the current workspace and capture results with full metadata. Use when the user wants to actually run an eval (one or many SUTs), collect scored outputs under results/, and produce a run manifest so findings are reproducible and comparable over time.
setup-evalSet up an evaluation in the current workspace. Use when the user wants to scaffold a single eval — choosing an existing framework (DeepEval, Inspect AI, OpenAI Evals, lm-evaluation-harness, LightEval, OLMES, Promptfoo, etc.), adapting an existing benchmark, or preparing a clean slate for a custom ev
/eval-runner:create-evalDesign a custom eval (or remix an existing benchmark) — task spec, dataset plan, scoring rubric, reporting format.
/eval-runner:document-evalWrite up the rationale and findings of an eval into a durable document under docs/.
/eval-runner:new-workspaceProvision a new eval-runner workspace (scaffold + optional GitHub repo).
/eval-runner:publish-datasetPublish an eval dataset to Hugging Face Hub (or GitHub), with dataset card, content hash, and license.
/eval-runner:publish-evalPublish an eval (definition + results) to GitHub, Hugging Face Space, or a local bundle.
/eval-runner:run-evalRun an eval from the current workspace, score outputs, and write a run manifest under results/.
/eval-runner:setup-evalSet up an eval in the current workspace — choose a framework, write the brief, and scaffold the eval directory.
eval-engineerAutonomous evaluation engineering subagent. Use for multi-step eval work — choosing a framework, designing a custom eval, running it across multiple SUTs, writing findings, and publishing. Coordinates the plugin's skills end-to-end.
Claude API and SDK documentation skill for building LLM-powered applications
19 skills
Access thousands of AI prompts and skills directly in your AI coding assistant. Search prompts, discover skills, save your own, and improve prompts with AI.
2 skills · 2 commands · 2 agents · 1 MCP server
Security review for Claude-generated code. Pattern-based warnings on edits, LLM-powered diff review on Stop, and an agentic commit reviewer that catches injection, XSS, SSRF, hardcoded secrets, and 25+ other vulnerability classes.
hooks
Open-source AI job search that runs locally in your AI coding CLI: scan job portals, evaluate listings into a structured A-H report with a 1-5 score, tailor your CV, track applications
5 skills · hooks
RuVLLM local inference with chat formatting, MicroLoRA fine-tuning, and SONA adaptation
3 skills · 1 command · 1 agent
Skill: add headroom-ai compress() to a Python or TypeScript app to cut LLM input tokens (tool outputs, logs, JSON, chat history). No API key.
1 skill
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