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Jim Bennett d79183139a Add Arize and Phoenix LLM observability skills (#1204 )

* Add 9 Arize LLM observability skills

Add skills for Arize AI platform covering trace export, instrumentation,
datasets, experiments, evaluators, AI provider integrations, annotations,
prompt optimization, and deep linking to the Arize UI.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Add 3 Phoenix AI observability skills

Add skills for Phoenix (Arize open-source) covering CLI debugging,
LLM evaluation workflows, and OpenInference tracing/instrumentation.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Ignoring intentional bad spelling

* Fix CI: remove .DS_Store from generated skills README and add codespell ignore

Remove .DS_Store artifact from winmd-api-search asset listing in generated
README.skills.md so it matches the CI Linux build output. Add queston to
codespell ignore list (intentional misspelling example in arize-dataset skill).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Add arize-ax and phoenix plugins

Bundle the 9 Arize skills into an arize-ax plugin and the 3 Phoenix
skills into a phoenix plugin for easier installation as single packages.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Fix skill folder structures to match source repos

Move arize supporting files from references/ to root level and rename
phoenix references/ to rules/ to exactly match the original source
repository folder structures.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Fixing file locations

* Fixing readme

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-04-02 09:58:55 +11:00

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Axial Coding

Group open-ended notes into structured failure taxonomies.

Process

Gather - Collect open coding notes
Pattern - Group notes with common themes
Name - Create actionable category names
Quantify - Count failures per category

Example Taxonomy

failure_taxonomy:
  content_quality:
    hallucination: [invented_facts, fictional_citations]
    incompleteness: [partial_answer, missing_key_info]
    inaccuracy: [wrong_numbers, wrong_dates]
  
  communication:
    tone_mismatch: [too_casual, too_formal]
    clarity: [ambiguous, jargon_heavy]
  
  context:
    user_context: [ignored_preferences, misunderstood_intent]
    retrieved_context: [ignored_documents, wrong_context]
  
  safety:
    missing_disclaimers: [legal, medical, financial]

Add Annotation (Python)

from phoenix.client import Client

client = Client()
client.spans.add_span_annotation(
    span_id="abc123",
    annotation_name="failure_category",
    label="hallucination",
    explanation="invented a feature that doesn't exist",
    annotator_kind="HUMAN",
    sync=True,
)

Add Annotation (TypeScript)

import { addSpanAnnotation } from "@arizeai/phoenix-client/spans";

await addSpanAnnotation({
  spanAnnotation: {
    spanId: "abc123",
    name: "failure_category",
    label: "hallucination",
    explanation: "invented a feature that doesn't exist",
    annotatorKind: "HUMAN",
  }
});

Agent Failure Taxonomy

agent_failures:
  planning: [wrong_plan, incomplete_plan]
  tool_selection: [wrong_tool, missed_tool, unnecessary_call]
  tool_execution: [wrong_parameters, type_error]
  state_management: [lost_context, stuck_in_loop]
  error_recovery: [no_fallback, wrong_fallback]

Transition Matrix (Agents)

Shows where failures occur between states:

def build_transition_matrix(conversations, states):
    matrix = defaultdict(lambda: defaultdict(int))
    for conv in conversations:
        if conv["failed"]:
            last_success = find_last_success(conv)
            first_failure = find_first_failure(conv)
            matrix[last_success][first_failure] += 1
    return pd.DataFrame(matrix).fillna(0)

Principles

MECE - Each failure fits ONE category
Actionable - Categories suggest fixes
Bottom-up - Let categories emerge from data

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