Aidd Riteway AI
paralleldrive/aidd
Teaches agents how to write correct riteway ai prompt evals (.sudo files) for multi-step flows that involve tool calls.
Debug LLM applications using the Phoenix CLI. An agent skill from github/awesome-copilot.
$ npx skills add github/awesome-copilot --skill phoenix-cli -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install github/awesome-copilot phoenix-cli --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/phoenix-cli .claude/skills/phoenix-cli && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "phoenix-cli" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/phoenix-cli into .claude/skills/phoenix-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phoenix-cli", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/github/awesome-copilot/tree/main/skills/phoenix-cliType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add github/awesome-copilot --skill phoenix-cli -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install github/awesome-copilot phoenix-cli --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/phoenix-cli .agents/skills/phoenix-cli && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "phoenix-cli" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/phoenix-cli into .agents/skills/phoenix-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phoenix-cli", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add github/awesome-copilot --skill phoenix-cli -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install github/awesome-copilot phoenix-cli --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/phoenix-cli .cursor/skills/phoenix-cli && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "phoenix-cli" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/phoenix-cli into .cursor/skills/phoenix-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phoenix-cli", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/github/awesome-copilot.git --path skills/phoenix-cli--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add github/awesome-copilot --skill phoenix-cli -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install github/awesome-copilot phoenix-cli --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/phoenix-cli .gemini/skills/phoenix-cli && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "phoenix-cli" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/phoenix-cli into .gemini/skills/phoenix-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phoenix-cli", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install github/awesome-copilot phoenix-cliInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add github/awesome-copilot --skill phoenix-cli -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/phoenix-cli .github/skills/phoenix-cli && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "phoenix-cli" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/phoenix-cli into .github/skills/phoenix-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phoenix-cli", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add github/awesome-copilot --skill phoenix-cli -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install github/awesome-copilot phoenix-cli --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/phoenix-cli .opencode/skills/phoenix-cli && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "phoenix-cli" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/phoenix-cli into .opencode/skills/phoenix-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phoenix-cli", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
phoenix-cliDebug LLM applications using the Phoenix CLI. An agent skill from github/awesome-copilot.
Phoenix CLI is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming…
Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/axial-coding.md` and `references/open-coding.md`). Compatibility notes: Requires Node.js (for npx) or global install of @arizeai/phoenix-cli. Optionally requires jq for JSON processing.
It sits in AI & LLM Engineering, covering LLM observability, LLM evaluation and GraphQL. It works with GraphQL. The repository describes itself as: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. The licence is Apache-2.0.
Read from SKILL.md and the folder at commit 7cce7cf. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
jqnpxFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
PHOENIX_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires Node.js (for npx) or global install of @arizeai/phoenix-cli. Optionally requires jq for JSON processing.
From compatibility in the SKILL.md frontmatter.
Phoenix CLI loads about 4k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 136 tokens; SKILL.md has 509 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from github/awesome-copilot at commit 7cce7cf, republished under its Apache-2.0 licence (© github). 509 words, ~4,050 tokens.
.claude/skills/phoenix-cli/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.px <resource> <action> # if installed globally
npx @arizeai/phoenix-cli <resource> <action> # no install requiredThe CLI uses singular resource commands with subcommands like list and get:
px trace list
px trace get <trace-id>
px trace annotate <trace-id>
px trace add-note <trace-id>
px trace-annotations delete
px span list
px span annotate <span-id>
px span add-note <span-id>
px span-annotations delete
px session list
px session get <session-id>
px session annotate <session-id>
px session add-note <session-id>
px session-annotations delete
px dataset list
px dataset get <name>
px project list
px project get <name>
px annotation-config list
px auth status
px profile list
px profile show [name]
px profile create <name>
px profile use <name>
px profile edit <name>
px profile delete <name>export PHOENIX_HOST=http://localhost:6006
export PHOENIX_PROJECT=my-project
export PHOENIX_API_KEY=your-api-key # if auth is enabledAlways use --format raw --no-progress when piping to jq.
| Task | Files |
|---|---|
| Look at sampled traces, spans, or sessions and write specific notes about what went wrong (no taxonomy yet) | references/open-coding |
| Group those notes into a structured failure taxonomy and quantify what matters | references/axial-coding |
Both stages tag every artifact with one shared coding annotation identifier (descriptive shape, e.g. coding-run:chatbot-context-loss-2026-05-06) so the run is queryable, reversible, and viewable as a unit. Pass --identifier <value> explicitly on every px call — shell inheritance is unreliable across agent harnesses. Open coding writes notes via px ... add-note and records a small local JSONL sidecar at .px/coding/<sanitized-identifier>.jsonl; axial coding reads that sidecar as the deterministic handoff and records labels in .px/coding/<sanitized-identifier>-axial.jsonl. Pick the identifier once per run (see references/open-coding.md), then share the Phoenix UI link from the wrap-up section. Revert is opt-in and runs three identifier-bound DELETEs only after explicit user confirmation.
Workflow term vs. server annotation name. The skill prose calls this value the coding annotation identifier (shell-variable hint:
CODING_ANNOTATION_IDENTIFIER). The server-side annotation NAME used for the UI filter is unchanged —coding_session_id— for data compatibility with rows already written by previous runs. Don't try to rename the server-side annotation; treat the asymmetry as load-bearing.
"What do I do after instrumenting?" / "Where do I focus?" / "What's going wrong?" open-coding → axial-coding → build evals for the top categories.
| Prefix | Description |
|---|---|
references/open-coding | Free-form notes against sampled traces, spans, or sessions — reach for it whenever the user wants to make sense of LLM traffic but has no failure categories yet. Includes a unit-of-analysis diagnostic so the workflow runs at the level the failure modes actually live at (trace for stateless single-shot calls, session for multi-turn agents, span for mechanical/in-isolation failures). |
references/axial-coding | Inductive grouping of notes into a MECE taxonomy with counts — reach for it whenever the user has observations and needs categories or eval targets |
px auth status # check connection and authentication
px auth status --endpoint http://other:6006 # check a specific endpoint
px auth status --profile staging # check a named profile's connectionNamed profiles let you switch between multiple Phoenix instances (local, staging, cloud) without juggling environment variables. Profiles are stored in ~/.px/settings.json (or $XDG_CONFIG_HOME/px/settings.json).
Configuration priority (highest to lowest): CLI flags > env vars > active profile > built-in defaults.
px profile list # list all profiles (shows active profile)
px profile show # show the active profile's settings
px profile show staging # show a named profile's settings
px profile create prod --endpoint https://app.phoenix.arize.com --api-key <key> --activate
px profile create local --endpoint http://localhost:6006 --project my-app
px profile use prod # switch the active profile
px profile edit prod # open profile JSON in $EDITOR (validates on save)
px profile delete prod --yes # delete a profile (--yes skips confirmation)Use --profile <name> on any command to target a specific profile without changing the active one:
px trace list --profile staging --limit 10 --format raw --no-progress | jq .
px auth status --profile prodpx profile create options: --endpoint <url>, --project <name>, --api-key <key>, --header <key=value> (repeatable), --activate.
px project list # list all projects (table view)
px project list --format raw --no-progress | jq '.[].name' # project names as JSON
px project get my-project --format raw --no-progress # single record by exact name
px project get my-project --format raw --no-progress | jq -r '.id' # extract project idproject get exits with ExitCode.FAILURE (1) on a name miss and writes a StructuredError {error, code: "FAILURE", hint} to stderr in --format json|raw.
px trace list --limit 20 --format raw --no-progress | jq .
px trace list --last-n-minutes 60 --limit 20 --format raw --no-progress | jq '.[] | select(.status == "ERROR")'
px trace list --since 2025-01-15T00:00:00Z --limit 50 --format raw --no-progress | jq .
px trace list --format raw --no-progress | jq 'sort_by(-.duration) | .[0:5]'
px trace list --include-notes --format raw --no-progress | jq '.[].notes'
px trace get <trace-id> --format raw | jq .
px trace get <trace-id> --format raw | jq '.spans[] | select(.status_code != "OK")'
px trace get <trace-id> --include-notes --format raw | jq '.notes'
px trace annotate <trace-id> --name reviewer --label pass
px trace annotate <trace-id> --name reviewer --score 0.9 --format raw --no-progress
px trace annotate <trace-id> --name reviewer --label pass --identifier "<coding-annotation-id>" # tag with a coding annotation identifier
px trace add-note <trace-id> --text "needs follow-up"
px trace add-note <trace-id> --text "needs follow-up" --identifier "<coding-annotation-id>" # tag + upsert on identifier
px trace-annotations delete --identifier "<coding-annotation-id>" --all -y # nuke every annotation tied to this coding annotation identifierpx <entity>-annotations delete requires --all or both --start-time and --end-time and emits {deleted: true, target, filter} on success.
Trace
traceId, status ("OK"|"ERROR"), duration (ms), startTime, endTime
annotations[] (with --include-annotations, excludes note)
name, result { score, label, explanation }
notes[] (with --include-notes)
name="note", result { explanation }
rootSpan — top-level span (parent_id: null)
spans[]
name, span_kind ("LLM"|"CHAIN"|"TOOL"|"RETRIEVER"|"EMBEDDING"|"AGENT"|"RERANKER"|"GUARDRAIL"|"EVALUATOR"|"UNKNOWN")
status_code ("OK"|"ERROR"|"UNSET"), parent_id, context.span_id
notes[] (with --include-notes)
name="note", result { explanation }
attributes
input.value, output.value — raw input/output
llm.model_name, llm.provider
llm.token_count.prompt/completion/total
llm.token_count.prompt_details.cache_read
llm.token_count.completion_details.reasoning
llm.input_messages.{N}.message.role/content
llm.output_messages.{N}.message.role/content
llm.invocation_parameters — JSON string (temperature, etc.)
exception.message — set if span erroredpx span list --limit 20 # recent spans (table view)
px span list --last-n-minutes 60 --limit 50 # spans from last hour
px span list --since 2025-01-15T00:00:00Z --limit 50 # spans since a timestamp
px span list --span-kind LLM --limit 10 # only LLM spans
px span list --status-code ERROR --limit 20 # only errored spans
px span list --name chat_completion --limit 10 # filter by span name
px span list --trace-id <id> --format raw --no-progress | jq . # all spans for a trace
px span list --parent-id null --limit 10 # only root spans
px span list --parent-id <span-id> --limit 10 # only children of a span
px span list --include-annotations --limit 10 # include annotation scores
px span list --include-notes --limit 10 # include span notes
px span list --attribute llm.model_name:gpt-4 --limit 10 # filter by string attribute
px span list --attribute llm.token_count.total:500 --limit 10 # filter by numeric attribute
px span list --attribute 'user.id:"12345"' --limit 10 # force string match for numeric-looking value
px span list --attribute session.id:sess:abc:123 --limit 20 # colon in value OK (split on first colon only)
px span list --attribute llm.model_name:gpt-4 --attribute session.id:abc --limit 10 # AND multiple filters
px span list output.json --limit 100 # save to JSON file
px span list --format raw --no-progress | jq '.[] | select(.status_code == "ERROR")'
px span annotate <span-id> --name reviewer --label pass
px span annotate <span-id> --name checker --score 1 --annotator-kind CODE
px span annotate <span-id> --name reviewer --label pass --identifier "<coding-annotation-id>" # tag with a coding annotation identifier
px span add-note <span-id> --text "verified by agent"
px span add-note <span-id> --text "verified by agent" --identifier "<coding-annotation-id>" # tag + upsert on identifier
px span-annotations delete --identifier "<coding-annotation-id>" --all -y # nuke every annotation tied to this coding annotation identifierSpan
name, span_kind ("LLM"|"CHAIN"|"TOOL"|"RETRIEVER"|"EMBEDDING"|"AGENT"|"RERANKER"|"GUARDRAIL"|"EVALUATOR"|"UNKNOWN")
status_code ("OK"|"ERROR"|"UNSET"), status_message
context.span_id, context.trace_id, parent_id
start_time, end_time
attributes
input.value, output.value — raw input/output
llm.model_name, llm.provider
llm.token_count.prompt/completion/total
llm.input_messages.{N}.message.role/content
llm.output_messages.{N}.message.role/content
llm.invocation_parameters — JSON string (temperature, etc.)
exception.message — set if span errored
annotations[] (with --include-annotations, excludes note)
name, result { score, label, explanation }
notes[] (with --include-notes)
name="note", result { explanation }px session list --limit 10 --format raw --no-progress | jq .
px session list --order asc --format raw --no-progress | jq '.[].session_id'
px session list --include-annotations --include-notes --format raw --no-progress | jq '.[].notes'
px session get <session-id> --format raw | jq .
px session get <session-id> --include-annotations --format raw | jq '.session.annotations'
px session get <session-id> --include-notes --format raw | jq '.session.notes'
px session annotate <session-id> --name reviewer --label pass
px session annotate <session-id> --name reviewer --score 0.9 --format raw --no-progress
px session annotate <session-id> --name reviewer --label pass --identifier "<coding-annotation-id>" # tag with a coding annotation identifier
px session add-note <session-id> --text "verified by agent"
px session add-note <session-id> --text "verified by agent" --identifier "<coding-annotation-id>" # tag + upsert on identifier
px session-annotations delete --identifier "<coding-annotation-id>" --all -y # nuke every annotation tied to this coding annotation identifierSessionData
id, session_id, project_id
start_time, end_time
token_count_prompt, token_count_completion, token_count_total — cumulative across all LLM spans in the session (int, default 0)
annotations[] (with --include-annotations, excludes note)
name, result { score, label, explanation }
notes[] (with --include-notes)
name="note", result { explanation }
traces[]
id, trace_id, start_time, end_timepx dataset list --format raw --no-progress | jq '.[].name'
px dataset get <name> --format raw | jq '.examples[] | {input, output: .expected_output}'
px dataset get <name> --split train --format raw | jq . # filter by split
px dataset get <name> --version <version-id> --format raw | jq .
px experiment list --dataset <name> --format raw --no-progress | jq '.[] | {id, name, failed_run_count}'
px experiment get <id> --format raw --no-progress | jq '.[] | select(.error != null) | {input, error}'
px prompt list --format raw --no-progress | jq '.[].name'
px prompt get <name> --format text --no-progress # plain text, ideal for piping to AIpx annotation-config list # list all configs (table view)
px annotation-config list --format raw --no-progress | jq '.[].name' # config names as JSONFor ad-hoc queries not covered by the commands above. Output is {"data": {...}}.
px api graphql '{ projectCount datasetCount promptCount evaluatorCount }'
px api graphql '{ projects { edges { node { name traceCount tokenCountTotal } } } }' | jq '.data.projects.edges[].node'
px api graphql '{ datasets { edges { node { name exampleCount experimentCount } } } }' | jq '.data.datasets.edges[].node'
px api graphql '{ evaluators { edges { node { name kind } } } }' | jq '.data.evaluators.edges[].node'
# Introspect any type
px api graphql '{ __type(name: "Project") { fields { name type { name } } } }' | jq '.data.__type.fields[]'Key root fields: projects, datasets, prompts, evaluators, projectCount, datasetCount, promptCount, evaluatorCount, viewer.
Download Phoenix documentation markdown for local use by coding agents.
px docs fetch # fetch default workflow docs to .px/docs
px docs fetch --workflow tracing # fetch only tracing docs
px docs fetch --workflow tracing --workflow evaluation
px docs fetch --dry-run # preview what would be downloaded
px docs fetch --refresh # clear .px/docs and re-download
px docs fetch --output-dir ./my-docs # custom output directoryKey options: --workflow (repeatable, values: tracing, evaluation, datasets, prompts, integrations, sdk, self-hosting, all), --dry-run, --refresh, --output-dir (default .px/docs), --workers (default 10).
© github, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files (references) in skills/phoenix-cli of github/awesome-copilot.
Open the folder on GitHubat commit 7cce7cf
We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.
Phoenix CLI next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Phoenix CLI this skillgithub/awesome-copilot | 40k | 2 repos | ~4k | Automated safety check: Pass | Apache-2.0 | |
| Aidd Riteway AIparalleldrive/aidd | 384 | — | ~1.6k | Automated safety check: Pass | MIT | |
| Phoenix CLIArize-ai/phoenix | 12k | — | ~6.8k | Automated safety check: Notes | Apache-2.0 | |
| Phoenix GraphqlArize-ai/phoenix | 12k | — | ~2.2k | Automated safety check: Pass | Custom licence | |
| LLM Trace Review Interfaceai-evals-course/evals-skills | 1.5k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Phoenix ServerArize-ai/phoenix | 12k | — | ~1.6k | Automated safety check: Pass | Custom licence |
paralleldrive/aidd
Teaches agents how to write correct riteway ai prompt evals (.sudo files) for multi-step flows that involve tool calls.
Arize-ai/phoenix
Debug LLM applications using the Phoenix CLI. An agent skill from Arize-ai/phoenix.
Arize-ai/phoenix
Write efficient GraphQL queries against the Phoenix API. An agent skill from Arize-ai/phoenix.
ai-evals-course/evals-skills
Builds a browser-based annotation page for reviewing LLM traces one at a time with pass/fail labels, notes and saved results, tailored to your data.
Arize-ai/phoenix
Backend development guide for the Phoenix AI observability platform (Strawberry GraphQL, SQLAlchemy async, FastAPI).
Orchestra-Research/AI-Research-SKILLs
Sets up Arize Phoenix to trace, evaluate and monitor LLM applications, with instrumentation for OpenAI, LangChain and LlamaIndex and a self-hosted server.
github/awesome-copilot
Maps an unfamiliar codebase into seven evidence-backed documents in docs/codebase/, using a scan script and templates, for onboarding or architecture write-ups.
github/awesome-copilot
Designs Azure infrastructure from a natural-language description, or diagrams an existing resource group, then refines the design through conversation and deploys it with Bicep.
github/awesome-copilot
Generates, edits and validates draw.io files with correct mxGraph XML, covering flowcharts, architecture, sequence, ER and UML class diagrams.
github/awesome-copilot
Cleans raw credit data and screens variables before loan modeling, dropping unstable, noisy or redundant features and writing an Excel report of every step.
github/awesome-copilot
Builds a warm, browser-based daily focus board the user updates by talking to their agent, with Eisenhower priorities, a brain-dump box and kind not-today carryover.
github/awesome-copilot
End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI.
Works with
Categories
Debug LLM applications using the Phoenix CLI. An agent skill from github/awesome-copilot. Phoenix CLI is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Debug LLM applications using the Phoenix CLI.
Phoenix CLI fits situations like: the user is analyzing traces; investigating LLM/agent failures; deciding what to do after instrumenting an app; building failure taxonomies.
Run `npx skills add github/awesome-copilot --skill phoenix-cli -a claude-code`. Or copy the skill folder (skills/phoenix-cli in github/awesome-copilot) into .claude/skills/phoenix-cli in your project. Claude Code loads it when a task matches its description.
Run `npx skills add github/awesome-copilot --skill phoenix-cli -a codex`. Or copy the skill folder (skills/phoenix-cli in github/awesome-copilot) into .agents/skills/phoenix-cli in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill phoenix-cli -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/phoenix-cli, .gemini/skills/phoenix-cli, .github/skills/phoenix-cli and .opencode/skills/phoenix-cli in your project.
Going by SKILL.md and its folder, Phoenix CLI needs the command-line tools its instructions call (jq and npx) and credentials named PHOENIX_API_KEY. Our summary lists: Node.js; A credential in PHOENIX_API_KEY. Compatibility (from SKILL.md): Requires Node.js (for npx) or global install of @arizeai/phoenix-cli. Optionally requires jq for JSON processing..
SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Phoenix CLI is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.9k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Phoenix CLI: Aidd Riteway AI (paralleldrive/aidd, 384 stars), Phoenix CLI (Arize-ai/phoenix, 12k stars), Phoenix Graphql (Arize-ai/phoenix, 12k stars) and LLM Trace Review Interface (ai-evals-course/evals-skills, 1.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,792 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 8, 2026.
Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.