hive.quality-monitor
Periodically self-assess output quality to catch degradation before the judge does.
10.70k repo starsObserved in 2 repos
Agent Building
Agent Building skills
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
hive.terminal-tools-foundations
Required reading whenever any shell_* tool is available. Teaches the foreground/background dichotomy (terminal_exec auto-promotes past 30s, returns a job_id you poll with terminal_job_logs), the standard envelope shape (exit_code, stdout, stdout_truncated_bytes, output_handle, semantic_status, warning, auto_backgrounded, job_id), output handle pagination via terminal_output_get, when to read semantic_status instead of raw exit_code (grep/rg/find/diff/test exit 1 is NOT an error), the destructive-warning surface (rm -rf, git push --force, DROP TABLE), tool preference (use files-tools / gcu-tools / hive_tools before raw shell), and the bash-only-on-macOS policy. Skipping this leads to "tool returned no output" surprises, orphaned jobs, and panic over benign grep exit codes.
10.70k repo starsObserved in 2 repos
Agent Building
fine-tuning-expert
Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.
10.60k repo starsObserved in 1 repos
Agent Building
phoenix-skills-audit
Audit recent changes to Phoenix's user-facing surfaces (Python clients, TypeScript clients, CLI, REST/GraphQL APIs) and patch the three external-facing agent skills — `phoenix-tracing`, `phoenix-cli`, and `phoenix-evals` — so they stay in sync with what actually shipped. Use this skill whenever a user asks to update those skills, sync the skills with recent changes, audit skill drift, check what client/CLI/API changes need to land in the skills, or mentions "skill freshness", "skill drift", "stale skills", or "are the skills up to date". Also trigger when shipping a notable client/CLI/API change and the user asks "do the skills need updating?". Default window is the last 7 days on `origin/main`; user may override.
10.58k repo starsObserved in 2 repos
Agent Building
annotate-spans
Write effective, consistent annotations on LLM/agent spans and traces, and coach the user on annotation practice. Load this whenever you are about to record structured feedback with the `batch_span_annotate` tool, or when the user asks how to annotate, label, score, or review spans/traces, build a failure taxonomy, or set up human/LLM review. Do NOT load for: pure analysis with no intent to save feedback (use debug-trace), latency or cost statistics, or prompt authoring (use playground).
10.58k repo starsObserved in 1 repos
Agent Building
datasets
Understand what a Phoenix dataset is and reason well about its examples, outputs, splits, and how it feeds evaluators and experiments. Load this whenever a dataset is in view or the user asks what a dataset is, how splits work, what an output "means", or how datasets relate to experiments and evals. This skill governs the judgment; any tool descriptions govern the mechanics.
10.58k repo starsObserved in 1 repos
Agent Building
evaluators
Author or refine a Phoenix evaluator — code or LLM-as-a-judge — that scores a run's output. Trigger when the user wants to create a new evaluator, improve an existing one's logic or rubric, choose labels, or decide what to measure on a dataset or experiment. Do NOT trigger on: (1) manual prompt drafting (use `playground`), (2) running or comparing experiments themselves (use `experiments`), (3) cross-trace failure diagnosis with no evaluator in scope (use `debug-trace`).
10.58k repo starsObserved in 1 repos
Agent Building
experiments
Run, read, and compare dataset-backed experiments to find evidence that a prompt or pipeline is improving. Trigger when the user wants to iterate over a dataset with experiments, compare experiment runs, read experiment quality/latency/cost, or decide whether a change actually helped. Running a prompt over a dataset is implicitly an experiment — load this skill when dataset-backed work begins, before authoring evaluators for the experiment and before starting the recorded run, not only when reading results. Do NOT trigger on: (1) manual prompt drafting with no dataset-backed evaluation in scope (use `playground`), (2) authoring or refining an evaluator's logic or rubric (use `evaluators`), (3) cross-trace failure diagnosis with no experiment in scope (use `debug-trace`).
10.58k repo starsObserved in 1 repos
Agent Building
phoenix-evals
Build and run evaluators for AI/LLM applications using Phoenix.
10.58k repo starsObserved in 1 repos
Agent Building
phoenix-evals-new-metric
Create a new built-in classification evaluator for Phoenix evals. Use this skill whenever the user asks to create a new eval, build a new metric, add a new builtin evaluator, create an LLM-as-a-judge metric, or add a new classification evaluator to Phoenix.
10.58k repo starsObserved in 1 repos
Agent Building