ソース情報
- リポジトリ
- exiao/skills
- ソースの最終更新活動
- 2026年5月12日 16:00
- 検出された SKILL.md の言語
- 英語
- スター
- 37
- フォーク
- 12
インストール方法
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
ソースファイルを確認
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
メニュー
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/exiao/skills --skill phoenix-cliコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか?SkillsMP が現在取得できるファイルをダウンロードできます。
Use when generating hooks, headlines, titles, and scroll-stopping openers for content. Also use when analyzing viral posts, Reels, TikToks, YouTube Shorts, or successful social examples to extract reusable hook patterns and improve hook guidance.
Verify a code change actually works by building/running the app and observing it at its real surface (CLI, API, UI, library, agent), capturing runtime evidence rather than trusting tests. Make sure to use this skill whenever the user has changed code and wants to know it works, is about to merge/push and wants confidence, says "did this actually work", "verify this works", "prove it works", "confirm the change", "make sure it works", wants runtime evidence, or is re-running tests / importing-and-calling just to check behavior, even if they never say the word "verify". When in doubt after any code change, reach for this. For post-deploy production health checks use verify-deploy; for static correctness/quality review use simplify or code-review.
Test an interactive lesson/course (or any "instructions to an AI" skill) by self-play. An agent plays BOTH the instructor following the lesson script AND a calibrated student persona, producing full turn-by-turn transcripts of every lesson, then publishes the raw transcripts to a single static page. Use when asked to "run lesson transcripts", "test the course end to end", "self-play the lessons", "publish raw test transcripts", "walk a synthetic student through every lesson", or to QA an interactive-instruction skill by actually running it rather than just reviewing findings. Distinct from dogfood and adversarial-ux-test (web-app browser QA) and synthetic-userstudies (findings plus a few cherry-picked transcripts). This one captures the COMPLETE run of every lesson and ships them all raw.
SOC 職業分類に基づく
SKILL.md を表示中
| name | phoenix-cli |
| description | Use when debugging LLM apps with Phoenix CLI: traces, errors, experiments. |
| license | Apache-2.0 |
| metadata | {"author":"arize-ai","version":"1.0"} |
Unified skill for Phoenix AI observability: CLI debugging, evaluators, and tracing instrumentation.
| Task | Read This |
|---|---|
| CLI usage, fetch traces, debug failures, analyze experiments | This file (below) |
| Build evaluators (code, LLM, RAG) | references/evals.md + references/evals-rules/ |
| Instrument tracing (OpenInference, spans, production) | references/tracing.md + references/tracing-rules/ |
Phoenix CLI reads PHOENIX_HOST, PHOENIX_API_KEY, and PHOENIX_PROJECT
from the environment (project can also be passed via --project).
Set them in whatever env-management surface your agent runtime uses, e.g.:
# Hermes
echo 'PHOENIX_HOST=https://app.phoenix.arize.com/s/<workspace>' >> ~/.hermes/.env
echo 'PHOENIX_API_KEY=...' >> ~/.hermes/.env
echo 'PHOENIX_PROJECT=bloom_chat' >> ~/.hermes/.env
# Plain shell
export PHOENIX_HOST=https://app.phoenix.arize.com/s/<workspace>
export PHOENIX_API_KEY=...
export PHOENIX_PROJECT=bloom_chat
# OpenClaw gateway
openclaw config set env.vars.PHOENIX_HOST 'https://app.phoenix.arize.com/s/<workspace>'
openclaw config set env.vars.PHOENIX_API_KEY '...'
openclaw config set env.vars.PHOENIX_PROJECT 'bloom_chat'
Verify: px traces --limit 1 (or px traces --limit 1 --project bloom_chat).
Projects: bloom_chat (prod), bloom_chat_dev (dev).
npm install -g @arizeai/phoenix-cli
# Or: npx @arizeai/phoenix-cli
CLI flags override environment variables when specified.
# Recent traces
px traces --limit 10
# Find failed traces
px traces --limit 50 --format raw --no-progress | jq '.[] | select(.status == "ERROR")'
# Specific trace details
px trace <trace-id>
# Errors in spans
px trace <trace-id> --format raw | jq '.spans[] | select(.status_code != "OK")'
# Slowest traces
px traces --limit 20 --format raw --no-progress | jq 'sort_by(-.duration) | .[0:5]'
# Span durations within a trace
px trace <trace-id> --format raw | jq '.spans | sort_by(-.duration_ms) | .[0:5] | .[] | {name, duration_ms, span_kind}'
px traces --limit 50 --format raw --no-progress | \
jq -r '.[].spans[] | select(.span_kind == "LLM") | {model: .attributes["llm.model_name"], prompt_tokens: .attributes["llm.token_count.prompt"], completion_tokens: .attributes["llm.token_count.completion"]}'
px datasets # List datasets
px experiments --dataset my-dataset # List experiments
px experiment <id> --format raw --no-progress | jq '.[] | select(.error != null) | {input: .input, error}' # Failures
px experiment <id> --format raw --no-progress | jq '[.[].latency_ms] | add / length' # Avg latency
| Command | Purpose | Key Options |
|---|---|---|
px traces | Fetch recent traces | -n <limit>, --last-n-minutes, --since, --format, --include-annotations |
px trace <id> | Fetch specific trace | --file, --format, --include-annotations |
px datasets | List datasets | |
px dataset <name> | Fetch dataset examples | --split, --version, --file |
px experiments | List experiments | --dataset <name> (required) |
px experiment <id> | Fetch experiment runs | --format, --file |
px prompts | List prompts | |
px prompt <name> | Fetch prompt |
pretty (default): Human-readable tree viewjson: Formatted JSON with indentationraw: Compact JSON for piping to jqUse --format raw --no-progress when piping.
Key span kinds: LLM, CHAIN, TOOL, RETRIEVER, EMBEDDING, AGENT.
Key LLM span attributes:
llm.model_name, llm.providerllm.token_count.prompt, llm.token_count.completionllm.input_messages.*, llm.output_messages.*input.value, output.valueexception.message| File | Contents |
|---|---|
references/evals.md | Evaluator guide: code evals, LLM evals, RAG evals, experiments, validation, production guardrails |
references/evals-rules/ | 34 rule files for evaluator implementation (fundamentals, error analysis, axial coding, experiments, validation, production) |
references/tracing.md | Tracing guide: setup, instrumentation, span types, projects, sessions, production deployment |
references/tracing-rules/ | 30 rule files for tracing implementation (setup, auto/manual instrumentation, span types, annotations, production) |