phoenix-skills-audit

name	phoenix-skills-audit
description	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.
metadata	{"internal":true}

Phoenix Skills Audit

Keep the three external-facing skills — phoenix-tracing, phoenix-cli, phoenix-evals — truthful about what the Phoenix Python clients, TypeScript clients, CLI, and APIs actually do today. The output is patches applied to the skill files, not a report. The skill reads recent commits, identifies what changed in user-facing surfaces, and updates the relevant SKILL.md and references/*.md files in place.

This is a sibling skill to phoenix-docs-gap-audit. The docs-gap-audit produces a report about gaps in docs/phoenix/; this skill produces edits to .agents/skills/. Skills are loaded into agent context every time the user asks a question that triggers them, so a stale skill teaches every future agent the wrong API. That makes drift here strictly worse than drift in human-facing docs — humans can sanity-check; agents can't.

Targets — the three skills this audit owns

.agents/skills/phoenix-tracing/SKILL.md
.agents/skills/phoenix-tracing/references/*.md
.agents/skills/phoenix-cli/SKILL.md
.agents/skills/phoenix-evals/SKILL.md
.agents/skills/phoenix-evals/references/*.md

Do not touch any other skill directory. If a change clearly belongs in a skill outside these three (e.g. phoenix-server, phoenix-frontend), note it in the run summary and skip — those skills are internal and have their own owners.

Source mapping — which code feeds which skill

Source area	Skill
packages/phoenix-otel/ (Python)	phoenix-tracing
js/packages/phoenix-otel/ (TS)	phoenix-tracing
OpenInference semantic conventions, span attributes, instrumentation patterns	phoenix-tracing
js/packages/phoenix-cli/ (commands, flags, output JSON shape)	phoenix-cli
packages/phoenix-evals/ (Python)	phoenix-evals
js/packages/phoenix-evals/ (TS)	phoenix-evals
New evaluators, eval templates, experiment APIs	phoenix-evals
packages/phoenix-client/ (Python)	depends on what's exposed — see below
js/packages/phoenix-client/ (TS)	depends on what's exposed — see below
Server REST/GraphQL (src/phoenix/server/api/)	depends on what's exposed — see below

The generic clients and the server APIs are cross-cutting. Map them by the feature they expose, not the file they live in:

• A client method that creates spans / sets attributes → phoenix-tracing
• A client method that runs evaluations or experiments → phoenix-evals
• A new REST/GraphQL endpoint that the CLI wraps (or should wrap) → phoenix-cli
• A new attribute on a span returned by the API → phoenix-cli (JSON shape doc) and potentially phoenix-tracing (if it's an OpenInference attribute)

A single feature can — and often does — span multiple skills. That's fine. Make all the edits; cross-reference between skills only when the user genuinely needs to read both.

Workflow

Phase 1: Gather commits

Default window is the last 7 days on origin/main. The user may override. Translate their phrasing into a concrete range before running anything.

Always audit origin/main, not the local main branch. Local main is routinely stale by dozens of commits — auditing the stale tip silently misses everything that shipped after the last git pull.

git fetch origin main --quiet

# Default 7-day window
git log --since="7 days ago" origin/main --no-merges --pretty=format:"%h %s" --name-status

# Tag range if the user specified one
git log <prev-tag>..<current-tag> --no-merges --pretty=format:"%h %s" --name-status

# Sanity check
git rev-list --count main..origin/main

Save the raw list. Note the audited SHA in the run summary so a reader can reproduce.

Phase 2: Triage to user-facing surfaces

Commit messages lie or under-report. Use them as an index, not a source of truth. Split the list into three buckets:

• Audit candidates — anything that changes what the Python clients, TypeScript clients, CLI, REST/GraphQL APIs, or instrumentation packages do from the user's perspective. New methods, new flags, new commands, new attributes, new env vars, renamed parameters, behavior changes, deprecations, breaking changes.
• Skip — internal refactors with no public surface, dep bumps, test-only changes, CI/build changes, formatting, frontend-only changes (those belong to other skills), release-please bookkeeping (chore(main): release …), and feature flags (env vars named *_DANGEROUSLY_*, *_EXPERIMENTAL_*, intentionally undocumented escape hatches). Feature flags are deliberately kept out of public skills.
• Unclear — when you cannot tell from the message and changed paths. Default to reading the diff. Cheap, and catches features hidden behind refactor: prefixes.

Group related commits that implement one logical feature across server + SDK + CLI. Audit them as one unit.

Breadth before depth. Enumerate every audit candidate in a flat list before going deep on any one. The goal is a complete sweep, not the single juiciest finding.

For each candidate, in the same one-liner, tag the cross-cutting concept(s) it most likely affects, even if the commit's file path doesn't say so. The four concepts:

• tracing — spans, attributes, instrumentation, OpenInference, otel
• evals — evaluators, experiments, datasets, prompts
• cli — CLI commands, flags, JSON output shape
• none — internal/refactor/dep/frontend-only/feature-flag

Once a candidate is tagged with tracing / evals / cli, you have committed to a hypothesis: there is potentially a skill edit here. The only way to legitimately drop the candidate later is to read the diff and find that the change is internal, or already documented, or genuinely user-irrelevant. "I don't see the relevant package in the path" is not a sufficient reason.

Only after this tagged list exists do you expand each entry into edits.

Phase 3: Locate the real code

For every candidate, open the actual changed files. Commit messages routinely omit:

• New optional parameters added to existing functions
• New exported symbols added alongside a refactor
• New CLI flags, JSON output fields, error codes
• Behavior changes in existing code paths
• Schema changes that change the shape returned by the CLI or REST API

Read enough of each file to answer:

1. What is the public surface? Exact symbol names, paths, command names, attribute keys. Write them down.
2. What is the behavior? What the resulting code does, not what the commit said.
3. Why would a user reach for it? If the code doesn't make this clear, flag it — the skill needs a usage rationale, not just a signature.
4. What does the example look like? Draft a real, runnable example using the current signature. Verify imports.

Do not trust a commit's file path to dictate the skill it belongs to. An evaluator change often lives in packages/phoenix-client/, not packages/phoenix-evals/. A span attribute lands in the server or a client, not in phoenix-otel. For every audit candidate, scan all client/SDK directories — Python and TypeScript, generic client and topical packages — and map each new symbol to the skill that documents the feature it exposes, per the source-mapping table. The easiest way to miss a finding is to stop looking after the first package you find.

Pair each Python change with its TypeScript mirror (and vice versa). Nearly every user-facing feature ships on both runtimes, usually in the same PR. When you locate a Python change, grep the TS package equivalent for the same symbol family (same commit SHA, or within a few commits). If the mirror is there, edit the -typescript.md reference too. If it's genuinely Python-only or TS-only, say so in the run summary — that itself is a finding worth noting.

Phase 4: Map each finding to skill edits

For each finding, decide:

1. Which target skill(s) does it belong to? (Use the source mapping table above.)
2. Which file(s) inside that skill?
   • New cross-cutting concept → new references/<slug>.md file, plus a row in SKILL.md's reference table and a link in the navigation section.
   • Update to an existing concept → edit the existing reference file.
   • Top-level navigation, workflow, or invocation change → edit SKILL.md directly.
3. What is the edit? A new section, a code-snippet replacement, a new row in a table, a renamed parameter, a deleted reference to a removed feature.

Reference file naming follows the existing patterns inside each skill — match what's already there:

• phoenix-tracing/references/: prefix by category (setup-*, instrumentation-*, span-*, production-*, attributes-*) and suffix by language (-python.md, -typescript.md).
• phoenix-evals/references/: prefix by category (evaluators-*, experiments-*, validation-*, production-*, fundamentals-*) and suffix by language.
• phoenix-cli: single-file skill — most edits land directly in SKILL.md. Only add a references/ directory if a topic has grown too large for inline (>40 lines).

When deleting a removed feature, remove every reference (cross-links in SKILL.md, table rows, workflow steps), not just the most obvious one.

Phase 5: Apply the patches

Edit the skill files in place. Use Edit for surgical changes; use Write for new reference files. After each edit:

• Verify the example runs against the current code. Every code snippet must use imports, classnames, and parameter names that exist in the tree as of the audited SHA. If you can't verify, don't include the snippet.
• Verify cross-links resolve. If you add a new references/foo.md, also add it to the navigation tables in SKILL.md. Broken links in a skill make the agent waste tool calls trying to read missing files.
• Quote signatures verbatim. A docstring or method header is the source of truth. Don't paraphrase parameters.

For breaking changes (renames, removals), do the destructive edit and the additive edit in the same pass. Removing the old reference without adding the new one leaves agents flailing; adding the new one without removing the old one leaves them confused about which is current.

Phase 6: Write the run summary

After patches are applied, produce a single markdown summary on stdout. The GitHub Actions wrapper reads this to populate the PR body. Do not write it to a file — let it surface as the agent's final message.

# Phoenix Skills Audit — <date range>

**Audited against:** `origin/main` at `<sha>`
**Commits analyzed:** N (M user-facing, K skipped)

## Edits applied

### phoenix-tracing
- `references/instrumentation-auto-python.md` — added section for new auto-instrumentor
  shipped in <commit>; example uses `<verified import path>`.
- `SKILL.md` — added row for the new reference file in the Quick Reference table.

### phoenix-cli
- `SKILL.md` — added `px experiment compare` subcommand documentation
  (commit `<sha>`); JSON shape grounded in `js/packages/phoenix-cli/src/commands/...`.
- `SKILL.md` — updated `--format` description; default changed in commit `<sha>`.

### phoenix-evals
- `references/evaluators-pre-built.md` — added entry for `<NewEvaluator>` from
  commit `<sha>`; example uses signature `<verified>`.

## Skipped commits

Brief list of commits intentionally excluded (deps, refactors, feature flags, frontend
changes that don't affect the three skills) so a reviewer can confirm nothing was
missed.

## Out-of-scope findings

Optional. If you saw a user-facing change that belongs in a different skill (e.g.
`phoenix-server`, `phoenix-frontend`), note it here so the maintainer knows — but do
not edit those skills.

Grounding rules — do not skip

Every edit must be verifiable against the tree at the audited SHA. Skills are read by agents that will not double-check; if you teach them an API that doesn't exist, every downstream task that follows the skill is wrong.

• Cite file paths and line numbers in the run summary for the source code that motivated each edit.
• Quote signatures verbatim. If the code says def foo(trace_id: str, *, kind: str = "LLM"), the skill must say that — not foo(trace_id, kind).
• Examples are real. Every snippet must use import paths, class names, and parameter names that exist in the current tree. Run a mental check: would python -c "from X import Y" succeed against this repo right now?
• No invented APIs. If unsure whether a symbol is exported, check the package's index.ts / __init__.py first.
• Stale checks are textual. Read the existing skill's snippet, then read the current source. If they diverge, replace the snippet exactly. Don't half-update it.
• Rationale comes from the code or the PR. If the why isn't evident, say so in the run summary as something the author needs to supply, and skip the rationale rather than inventing one.

When in doubt, read more code. The cost of an extra file read is tiny compared to the cost of poisoning every future agent with a hallucinated API.

Decision quick reference

Question	Answer
Commit message says refactor: — skip?	Read the diff. Refactors often add exports.
Frontend-only change (app/src/) — does it affect any of the three skills?	Almost never. Skip and note in "out-of-scope findings".
New env var — feature flag?	Skip. Feature flags (*_DANGEROUSLY_*, *_EXPERIMENTAL_*) stay out of public skills.
New env var that controls a public surface?	Update the relevant skill's setup or environment section.
Server-only change (src/phoenix/server/) — relevant to the three skills?	Only if it changes what a client/CLI sees. A new GraphQL field consumed by the CLI is in scope; an internal server refactor is not.
Removed/renamed public symbol?	Edit the skill: remove the old reference, add the new one in the same pass.
New evaluator class?	Add to phoenix-evals/references/evaluators-pre-built.md (or create a topic-specific reference if the patterns are new).
New CLI subcommand?	Add to the relevant section of phoenix-cli/SKILL.md. Document the JSON shape if the command emits structured output.
New OpenInference attribute?	Add to phoenix-tracing/references/attributes-*.md and the relevant span-*.md.
Breaking change to an existing skill example?	High priority. Update the example and search the rest of the skill for adjacent uses of the old signature.
Feature has no apparent use case from reading the code?	Add the API entry but flag in the run summary that rationale is missing — don't invent motivation.
Python-only change — is there a TS mirror?	Check. Most features ship on both runtimes in the same PR; a Python-only change is the exception, not the rule. If confirmed Python-only, call it out in the run summary so someone can chase the TS side.
SKILL.md links to a references/foo.md that doesn't exist?	Remove the link. Broken cross-links teach nothing and waste tool calls. Don't add a "TODO" flag — just delete.
Python change lives in packages/phoenix-client/ but conceptually belongs to tracing/evals?	Map it to the conceptual skill. File path is an implementation detail; the skill that documents the feature owns it.

Pre-submit checklist

Before exiting, walk through:

• Every edited code snippet uses symbols that exist in the tree at the audited SHA.
• Every new reference file is linked from its skill's SKILL.md (no orphan files).
• Every removed reference is also unlinked from SKILL.md (no broken cross-links).
• The run summary lists every edit, grouped by target skill, with the source commit SHA.
• Skipped commits are listed so a reviewer can confirm triage.
• Every Phase-2 audit candidate tagged tracing / evals / cli is either reflected in the edits, or the run summary states a concrete reason it was dropped (e.g., "PR #X already self-patched the skill", with the SHA). A tagged candidate that just disappears is a triage bug — go back and reconcile.
• No edits made outside .agents/skills/phoenix-tracing/, .agents/skills/phoenix-cli/, or .agents/skills/phoenix-evals/.
• No invented APIs, no invented imports, no placeholder values disguised as real code.

Operating modes

This skill runs in two contexts:

• Manual — a developer asks "audit the skills" or "are the skills up to date". Apply the workflow above end-to-end, edit files, print the run summary, and let the user review the diff before committing.

• CI (GitHub Actions) — .github/workflows/claude-skills-audit.yml runs this skill weekly. The action commits the patches to a branch and opens a PR using the run summary as the PR body. The skill itself does not commit or push — that's the workflow's job. Just edit the files and emit the summary.