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* 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>
96 lines
2.3 KiB
Markdown
96 lines
2.3 KiB
Markdown
# Axial Coding
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Group open-ended notes into structured failure taxonomies.
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## Process
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1. **Gather** - Collect open coding notes
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2. **Pattern** - Group notes with common themes
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3. **Name** - Create actionable category names
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4. **Quantify** - Count failures per category
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## Example Taxonomy
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```yaml
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failure_taxonomy:
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content_quality:
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hallucination: [invented_facts, fictional_citations]
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incompleteness: [partial_answer, missing_key_info]
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inaccuracy: [wrong_numbers, wrong_dates]
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communication:
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tone_mismatch: [too_casual, too_formal]
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clarity: [ambiguous, jargon_heavy]
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context:
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user_context: [ignored_preferences, misunderstood_intent]
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retrieved_context: [ignored_documents, wrong_context]
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safety:
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missing_disclaimers: [legal, medical, financial]
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```
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## Add Annotation (Python)
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```python
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from phoenix.client import Client
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client = Client()
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client.spans.add_span_annotation(
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span_id="abc123",
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annotation_name="failure_category",
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label="hallucination",
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explanation="invented a feature that doesn't exist",
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annotator_kind="HUMAN",
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sync=True,
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)
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```
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## Add Annotation (TypeScript)
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```typescript
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import { addSpanAnnotation } from "@arizeai/phoenix-client/spans";
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await addSpanAnnotation({
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spanAnnotation: {
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spanId: "abc123",
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name: "failure_category",
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label: "hallucination",
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explanation: "invented a feature that doesn't exist",
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annotatorKind: "HUMAN",
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}
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});
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```
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## Agent Failure Taxonomy
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```yaml
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agent_failures:
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planning: [wrong_plan, incomplete_plan]
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tool_selection: [wrong_tool, missed_tool, unnecessary_call]
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tool_execution: [wrong_parameters, type_error]
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state_management: [lost_context, stuck_in_loop]
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error_recovery: [no_fallback, wrong_fallback]
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```
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## Transition Matrix (Agents)
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Shows where failures occur between states:
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```python
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def build_transition_matrix(conversations, states):
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matrix = defaultdict(lambda: defaultdict(int))
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for conv in conversations:
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if conv["failed"]:
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last_success = find_last_success(conv)
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first_failure = find_first_failure(conv)
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matrix[last_success][first_failure] += 1
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return pd.DataFrame(matrix).fillna(0)
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```
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## Principles
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- **MECE** - Each failure fits ONE category
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- **Actionable** - Categories suggest fixes
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- **Bottom-up** - Let categories emerge from data
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