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Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and cont
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add github/awesome-copilot --skill arize-evaluator -a cursor -y
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
SPACE— All--spaceflags and theARIZE_SPACEenv var accept a space name (e.g.,my-workspace) or a base64 space ID (e.g.,U3BhY2U6...). Find yours withax spaces list.
This skill covers designing, creating, and running LLM-as-judge evaluators on Arize. An evaluator defines the judge; a task is how you run it against real data.
Proceed directly with the task — run the ax command you need. Do NOT check versions, env vars, or profiles upfront.
If an ax command fails, troubleshoot based on the error:
command not found or version error → see references/ax-setup.md401 Unauthorized / missing API key → run ax profiles show to inspect the current profile. If the profile is missing or the API key is wrong, follow references/ax-profiles.md to create/update it. If the user doesn't have their key, direct them to https://app.arize.com/admin > API Keysax spaces list to pick by name, or ask the userax ai-integrations list --space SPACE to check for platform-managed credentials. If none exist, ask the user to provide the key or create an integration via the arize-ai-provider-integration skill.env files or search the filesystem for credentials. Use ax profiles for Arize credentials and ax ai-integrations for LLM provider keys. If credentials are not available through these channels, ask the user.ax ai-integrations list, (4) contact support at https://arize.com/supportAn evaluator is an LLM-as-judge definition. It contains:
| Field | Description |
|---|---|
| Template | The judge prompt. Uses {variable} placeholders (e.g. {input}, {output}, {context}) that get filled in at run time via a task's column mappings. |
| Classification choices | The set of allowed output labels (e.g. factual / hallucinated). Binary is the default and most common. Each choice can optionally carry a numeric score. |
| AI Integration | Stored LLM provider credentials (OpenAI, Anthropic, Bedrock, etc.) the evaluator uses to call the judge model. |
| Model | The specific judge model (e.g. gpt-4o, claude-sonnet-4-5). |
| Invocation params | Optional JSON of model settings like {"temperature": 0}. Low temperature is recommended for reproducibility. |
| Optimization direction | Whether higher scores are better (maximize) or worse (minimize). Sets how the UI renders trends. |
| Data granularity | Whether the evaluator runs at the span, trace, or session level. Most evaluators run at the span level. |
Evaluators are versioned — every prompt or model change creates a new immutable version. The most recent version is active.
A task is how you run one or more evaluators against real data. Tasks are attached to a project (live traces/spans) or a dataset (experiment runs). A task contains:
| Field | Description |
|---|---|
| Evaluators | List of evaluators to run. You can run multiple in one task. |
| Column mappings | Maps each evaluator's template variables to actual field paths on spans or experiment runs (e.g. "input" → "attributes.input.value"). This is what makes evaluators portable across projects and experiments. |
| Query filter | SQL-style expression to select which spans/runs to evaluate (e.g. "span_kind = 'LLM'"). Optional but important for precision. |
| Continuous | For project tasks: whether to automatically score new spans as they arrive. |
| Sampling rate | For continuous project tasks: fraction of new spans to evaluate (0–1). |
The --data-granularity flag controls what unit of data the evaluator scores. It defaults to span and only applies to project tasks (not dataset/experiment tasks — those evaluate experiment runs directly).
| Level | What it evaluates | Use for | Result column prefix |
|---|---|---|---|
span (default) | Individual spans | Q&A correctness, hallucination, relevance | eval.{name}.label / .score / .explanation |
trace | All spans in a trace, grouped by context.trace_id | Agent trajectory, task correctness — anything that needs the full call chain | trace_eval.{name}.label / .score / .explanation |
session | All traces in a session, grouped by attributes.session.id and ordered by start time | Multi-turn coherence, overall tone, conversation quality | session_eval.{name}.label / .score / .explanation |
For trace granularity, spans sharing the same context.trace_id are grouped together. Column values used by the evaluator template are comma-joined into a single string (each value truncated to 100K characters) before being passed to the judge model.
For session granularity, the same trace-level grouping happens first, then traces are ordered by start_time and grouped by attributes.session.id. Session-level values are capped at 100K characters total.
{conversation} template variableAt session granularity, {conversation} is a special template variable that renders as a JSON array of {input, output} turns across all traces in the session, built from attributes.input.value / attributes.llm.input_messages (input side) and attributes.output.value / attributes.llm.output_messages (output side).
At span or trace granularity, {conversation} is treated as a regular template variable and resolved via column mappings like any other.
A task can contain evaluators at different granularities. At runtime the system uses the highest granularity (session > trace > span) for data fetching and automatically splits into one child run per evaluator. Per-evaluator query_filter in the task's evaluators JSON further narrows which spans are included (e.g., only tool-call spans within a session).
AI integrations store the LLM provider credentials the evaluator uses. For full CRUD — listing, creating for all providers (OpenAI, Anthropic, Azure, Bedrock, Vertex, Gemini, NVIDIA NIM, custom), updating, and deleting — use the arize-ai-provider-integration skill.
Quick reference for the common case (OpenAI):
# Check for an existing integration first
ax ai-integrations list --space SPACE
# Create if none exists
ax ai-integrations create \
--name "My OpenAI Integration" \
--provider openAI \
--api-key $OPENAI_API_KEY
Copy the returned integration ID — it is required for ax evaluators create --ai-integration-id.
# List / Get
ax evaluators list --space SPACE
ax evaluators get ID # accepts name or ID
ax evaluators get NAME --space SPACE # required when using name instead of ID
ax evaluators list-versions NAME_OR_ID
ax evaluators get-version VERSION_ID
# Create (creates the evaluator and its first version)
ax evaluators create \
--name "Answer Correctness" \
--space SPACE \
--description "Judges if the model answer is correct" \
--template-name "correctness" \
--commit-message "Initial version" \
--ai-integration-id INT_ID \
--model-name "gpt-4o" \
--include-explanations \
--use-function-calling \
--classification-choices '{"correct": 1, "incorrect": 0}' \
--template 'You are an evaluator. Given the user question and the model response, decide if the response correctly answers the question.
User question: {input}
Model response: {output}
Respond with exactly one of these labels: correct, incorrect'
# Create a new version (for prompt or model changes — versions are immutable)
ax evaluators create-version NAME_OR_ID \
--commit-message "Added context grounding" \
--template-name "correctness" \
--ai-integration-id INT_ID \
--model-name "gpt-4o" \
--include-explanations \
--classification-choices '{"correct": 1, "incorrect": 0}' \
--template 'Updated prompt...
{input} / {output} / {context}'
# Update metadata only (name, description — not prompt)
ax evaluators update NAME_OR_ID \
--name "New Name" \
--description "Updated description"
# Delete (permanent — removes all versions)
ax evaluators delete NAME_OR_ID
Key flags for create:
| Flag | Required | Description |
|---|---|---|
--name | yes | Evaluator name (unique within space) |
--space | yes | Space name or ID to create in |
--template-name | yes | Eval column name — alphanumeric, spaces, hyphens, underscores |
--commit-message | yes | Description of this version |
--ai-integration-id | yes | AI integration ID (from above) |
--model-name | yes | Judge model (e.g. gpt-4o) |
--template | yes | Prompt with {variable} placeholders (single-quoted in bash) |
--classification-choices | yes | JSON object mapping choice labels to numeric scores e.g. '{"correct": 1, "incorrect": 0}' |
--description | no | Human-readable description |
--include-explanations | no | Include reasoning alongside the label |
--use-function-calling | no | Prefer structured function-call output |
--invocation-params | no | JSON of model params e.g. '{"temperature": 0}' |
--data-granularity | no | span (default), trace, or session. Only relevant for project tasks, not dataset/experiment tasks. See Data Granularity section. |
--direction | no | Optimization direction: maximize or minimize. Sets how the UI renders trends. |
--provider-params | no | JSON object of provider-specific parameters |
PROJECT_NAME,DATASET_NAME, andevaluator_idall accept a name or base64 ID.
# List / Get
ax tasks list --space SPACE
ax tasks list --project PROJECT_NAME
ax tasks list --dataset DATASET_NAME --space SPACE
ax tasks get TASK_ID
# Create (project — continuous)
ax tasks create \
--name "Correctness Monitor" \
--task-type template_evaluation \
--project PROJECT_NAME \
--evaluators '[{"evaluator_id": "EVAL_ID", "column_mappings": {"input": "attributes.input.value", "output": "attributes.output.value"}}]' \
--is-continuous \
--sampling-rate 0.1
# Create (project — one-time / backfill)
ax tasks create \
--name "Correctness Backfill" \
--task-type template_evaluation \
--project PROJECT_NAME \
--evaluators '[{"evaluator_id": "EVAL_ID", "column_mappings": {"input": "attributes.input.value", "output": "attributes.output.value"}}]' \
--no-continuous
# Create (experiment / dataset)
ax tasks create \
--name "Experiment Scoring" \
--task-type template_evaluation \
--dataset DATASET_NAME --space SPACE \
--experiment-ids "EXP_ID_1,EXP_ID_2" \ # base64 IDs from `ax experiments list --space SPACE -o json`
--evaluators '[{"evaluator_id": "EVAL_ID", "column_mappings": {"output": "output"}}]' \
--no-continuous
# Trigger a run (project task — use data window)
ax tasks trigger-run TASK_ID \
--data-start-time "2026-03-20T00:00:00" \
--data-end-time "2026-03-21T23:59:59" \
--wait
# Trigger a run (experiment task — use experiment IDs)
ax tasks trigger-run TASK_ID \
--experiment-ids "EXP_ID_1" \ # base64 ID from `ax experiments list --space SPACE -o json`
--wait
# Monitor
ax tasks list-runs TASK_ID
ax tasks get-run RUN_ID
ax tasks wait-for-run RUN_ID --timeout 300
ax tasks cancel-run RUN_ID --force
Time format for trigger-run: 2026-03-21T09:00:00 — no trailing Z.
Additional trigger-run flags:
| Flag | Description |
|---|---|
--max-spans | Cap processed spans (default 10,000) |
--override-evaluations | Re-score spans that already have labels |
--wait / -w | Block until the run finishes |
--timeout | Seconds to wait with --wait (default 600) |
--poll-interval | Poll interval in seconds when waiting (default 5) |
Run status guide:
| Status | Meaning |
|---|---|
completed, 0 spans | The eval index lags 1–2 hours — spans ingested recently may not be indexed yet. Shift the window to data at least 2 hours old, or widen the time range to cover more historical data. |
cancelled ~1s | Integration credentials invalid |
cancelled ~3min | Found spans but LLM call failed — check model name or key |
completed, N > 0 | Success — check scores in UI |
Use this when the user says something like "create an evaluator for my Playground Traces project".
ax spans export accepts a project name directly — no ID lookup needed. If you don't know the project name, list available projects:
ax projects list --space SPACE -o json
Find the entry whose "name" matches (case-insensitive) and use that name as PROJECT in subsequent commands. If you later hit a validation error with a name, fall back to using the project's "id" (a base64 string) instead.
If the user specified the evaluator type (hallucination, correctness, relevance, etc.) → skip to Step 3.
If not, sample recent spans to base the evaluator on actual data:
ax spans export PROJECT --space SPACE -l 10 --days 30 --stdout
Inspect attributes.input, attributes.output, span kinds, and any existing annotations. Identify failure modes (e.g. hallucinated facts, off-topic answers, missing context) and propose 1–3 concrete evaluator ideas. Let the user pick.
Each suggestion must include: the evaluator name (bold), a one-sentence description of what it judges, and the binary label pair in parentheses. Format each like:
label_a / label_b)Example:
correct / incorrect)factual / hallucinated)ax ai-integrations list --space SPACE -o json
If a suitable integration exists, note its ID. If not, create one using the arize-ai-provider-integration skill. Ask the user which provider/model they want for the judge.
Use the template design best practices below. Keep the evaluator name and variables generic — the task (Step 6) handles project-specific wiring via column_mappings.
ax evaluators create \
--name "Hallucination" \
--space SPACE \
--template-name "hallucination" \
--commit-message "Initial version" \
--ai-integration-id INT_ID \
--model-name "gpt-4o" \
--include-explanations \
--use-function-calling \
--classification-choices '{"factual": 1, "hallucinated": 0}' \
--template 'You are an evaluator. Given the user question and the model response, decide if the response is factual or contains unsupported claims.
User question: {input}
Model response: {output}
Respond with exactly one of these labels: hallucinated, factual'
Recommended approach: Always start with a small backfill (~100 historical spans) to validate the evaluator before turning on continuous monitoring. This lets you catch column mapping errors, wrong span kinds, and template issues on known data before scoring all future production spans. Only enable continuous after a backfill confirms correct scoring.
Before creating the task, ask:
"Would you like to: (a) Run a backfill on historical spans (one-time)? (b) Set up continuous evaluation on new spans going forward? (c) Both — backfill first to validate, then keep scoring new spans automatically? (recommended)"
Do not guess paths. Pull a sample and inspect what fields are actually present:
ax spans export PROJECT --space SPACE -l 5 --days 7 --stdout
For each template variable ({input}, {output}, {context}), find the matching JSON path. Common starting points — always verify on your actual data before using:
| Template var | LLM span | CHAIN span |
|---|---|---|
input | attributes.input.value | attributes.input.value |
output | attributes.llm.output_messages.0.message.content | attributes.output.value |
context | attributes.retrieval.documents.contents | — |
tool_output | attributes.input.value (fallback) | attributes.output.value |
Validate span kind alignment: If the evaluator prompt assumes LLM final text but the task targets CHAIN spans (or vice versa), runs can cancel or score the wrong text. Make sure the query_filter on the task matches the span kind you mapped.
query_filter only works on indexed attributes: The query_filter in the evaluators JSON is evaluated against the eval index, not the raw span store. Attributes under attributes.metadata.* or custom keys may not be indexed and will silently match nothing. Use well-known indexed attributes like span_kind or attributes.llm.model_name for filtering. If a filter returns 0 spans despite data existing, try removing the filter as a diagnostic step.
Full example --evaluators JSON:
[
{
"evaluator_id": "EVAL_ID",
"query_filter": "span_kind = 'LLM'",
"column_mappings": {
"input": "attributes.input.value",
"output": "attributes.llm.output_messages.0.message.content",
"context": "attributes.retrieval.documents.contents"
}
}
]
Include a mapping for every variable the template references. Omitting one causes runs to produce no valid scores.
Backfill only (a):
ax tasks create \
--name "Hallucination Backfill" \
--task-type template_evaluation \
--project PROJECT \
--evaluators '[{"evaluator_id": "EVAL_ID", "column_mappings": {"input": "attributes.input.value", "output": "attributes.output.value"}}]' \
--no-continuous
Continuous only (b):
ax tasks create \
--name "Hallucination Monitor" \
--task-type template_evaluation \
--project PROJECT \
--evaluators '[{"evaluator_id": "EVAL_ID", "column_mappings": {"input": "attributes.input.value", "output": "attributes.output.value"}}]' \
--is-continuous \
--sampling-rate 0.1
Both (c): Use --is-continuous on create, then also trigger a backfill run in Step 8.
Eval index lag: The eval index is built asynchronously from the primary trace store and can lag 1–2 hours. For your first test run, use a time window ending at least 2 hours in the past. If you set
--data-end-timeto "now" on spans ingested in the last hour, the run will complete successfully but score 0 spans.
First find what time range has data:
ax spans export PROJECT --space SPACE -l 100 --days 1 --stdout # try last 24h first
ax spans export PROJECT --space SPACE -l 100 --days 7 --stdout # widen if empty
Use the start_time / end_time fields from real spans to set the window. For the first validation run, cap --max-spans at ~100 to get quick feedback:
ax tasks trigger-run TASK_ID \
--data-start-time "2026-03-20T00:00:00" \
--data-end-time "2026-03-21T23:59:59" \
--max-spans 100 \
--wait
Review scores and explanations before widening to the full backfill or enabling continuous.
Use this when the user says something like "create an evaluator for my experiment" or "evaluate my dataset runs".
If the user says "dataset" but doesn't have an experiment: A task must target an experiment (not a bare dataset). Ask:
"Evaluation tasks run against experiment runs, not datasets directly. Would you like help creating an experiment on that dataset first?"
If yes, use the arize-experiment skill to create one, then return here.
ax datasets list --space SPACE
ax experiments list --dataset DATASET_NAME --space SPACE -o json
Note the dataset name and the experiment name(s) to score. These accept names or IDs in subsequent commands — names are preferred.
If the user specified the evaluator type → skip to Step 3.
If not, inspect a recent experiment run to base the evaluator on actual data:
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | python3 -c "import sys,json; runs=json.load(sys.stdin); print(json.dumps(runs[0], indent=2))"
Look at the output, input, evaluations, and metadata fields. Identify gaps (metrics the user cares about but doesn't have yet) and propose 1–3 evaluator ideas. Each suggestion must include: the evaluator name (bold), a one-sentence description, and the binary label pair in parentheses — same format as Workflow A, Step 2.
Same as Workflow A, Step 3.
Same as Workflow A, Step 4. Keep variables generic.
Run data shape differs from span data. Inspect:
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | python3 -c "import sys,json; runs=json.load(sys.stdin); print(json.dumps(runs[0], indent=2))"
Common mapping for experiment runs:
output → "output" (top-level field on each run)input → check if it's on the run or embedded in the linked dataset examplesIf input is not on the run JSON, export dataset examples to find the path:
ax datasets export DATASET_NAME --space SPACE --stdout | python3 -c "import sys,json; ex=json.load(sys.stdin); print(json.dumps(ex[0], indent=2))"
ax tasks create \
--name "Experiment Correctness" \
--task-type template_evaluation \
--dataset DATASET_NAME --space SPACE \
--experiment-ids "EXP_ID" \ # base64 ID from `ax experiments list --space SPACE -o json`
--evaluators '[{"evaluator_id": "EVAL_ID", "column_mappings": {"output": "output"}}]' \
--no-continuous
ax tasks trigger-run TASK_ID \
--experiment-ids "EXP_ID" \ # base64 ID from `ax experiments list --space SPACE -o json`
--wait
ax tasks list-runs TASK_ID
ax tasks get-run RUN_ID
Use {input}, {output}, and {context} — not names tied to a specific project or span attribute (e.g. do not use {attributes_input_value}). The evaluator itself stays abstract; the task's column_mappings is where you wire it to the actual fields in a specific project or experiment. This lets the same evaluator run across multiple projects and experiments without modification.
Use exactly two clear string labels (e.g. hallucinated / factual, correct / incorrect, pass / fail). Binary labels are:
If the user insists on more than two choices, that's fine — but recommend binary first and explain the tradeoff (more labels → more ambiguity → lower inter-rater reliability).
The template must tell the judge model to respond with only the label string — nothing else. The label strings in the prompt must exactly match the labels in --classification-choices (same spelling, same casing).
Good:
Respond with exactly one of these labels: hallucinated, factual
Bad (too open-ended):
Is this hallucinated? Answer yes or no.
Pass --invocation-params '{"temperature": 0}' for reproducible scoring. Higher temperatures introduce noise into evaluation results.
--include-explanations for debuggingDuring initial setup, always include explanations so you can verify the judge is reasoning correctly before trusting the labels at scale.
Single quotes prevent the shell from interpolating {variable} placeholders. Double quotes will cause issues:
# Correct
--template 'Judge this: {input} → {output}'
# Wrong — shell may interpret { } or fail
--template "Judge this: {input} → {output}"
--classification-choices to match your template labelsThe labels in --classification-choices must exactly match the labels referenced in --template (same spelling, same casing). Omitting --classification-choices causes task runs to fail with "missing rails and classification choices."
| Problem | Solution |
|---|---|
ax: command not found | See references/ax-setup.md |
401 Unauthorized | API key may not have access to this space. Verify at https://app.arize.com/admin > API Keys |
Evaluator not found | ax evaluators list --space SPACE |
Integration not found | ax ai-integrations list --space SPACE |
Task not found | ax tasks list --space SPACE |
project and dataset-id are mutually exclusive | Use only one when creating a task |
experiment-ids required for dataset tasks | Add --experiment-ids to create and trigger-run |
sampling-rate only valid for project tasks | Remove --sampling-rate from dataset tasks |
Validation error on ax spans export | Project name usually works; if you still get a validation error, look up the base64 project ID via ax projects list --space SPACE -o json and use the id field instead |
| Template validation errors | Use single-quoted --template '...' in bash; single braces {var}, not double {{var}} |
Run stuck in pending | ax tasks get-run RUN_ID; then ax tasks cancel-run RUN_ID |
Run cancelled ~1s | Integration credentials invalid — check AI integration |
Run cancelled ~3min | Found spans but LLM call failed — wrong model name or bad key |
Run completed, 0 spans | Widen time window; eval index may not cover older data |
| No scores in UI | Fix column_mappings to match real paths on your spans/runs |
| Scores look wrong | Add --include-explanations and inspect judge reasoning on a few samples |
| Evaluator cancels on wrong span kind | Match query_filter and column_mappings to LLM vs CHAIN spans |
Time format error on trigger-run | Use 2026-03-21T09:00:00 — no trailing Z |
| Run failed: "missing rails and classification choices" | Add --classification-choices '{"label_a": 1, "label_b": 0}' to ax evaluators create — labels must match the template |
Run completed, all spans skipped | Query filter matched spans but column mappings are wrong or template variables don't resolve — export a sample span and verify paths |
query_filter set but 0 spans scored | The filter attribute may not be indexed in the eval index. attributes.metadata.* and custom attributes are often not indexed. Use span_kind or attributes.llm.model_name instead, or remove the filter to confirm spans exist in the window. |
When a task run is cancelled (status cancelled), follow this checklist in order:
1. Check integration credentials
ax ai-integrations list --space SPACE -o json
Verify the integration ID used by the evaluator exists and has valid credentials. If the integration was deleted or the API key expired, the run cancels within ~1 second.
2. Verify the model name
ax evaluators get EVALUATOR_NAME --space SPACE -o json
Check the model_name field. A typo or deprecated model causes the LLM call to fail and the run to cancel after ~3 minutes.
3. Export a sample span/run and compare paths to column_mappings
For project tasks:
ax spans export PROJECT --space SPACE -l 1 --days 7 --stdout | python3 -m json.tool
For experiment tasks:
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | python3 -c "import sys,json; runs=json.load(sys.stdin); print(json.dumps(runs[0], indent=2)) if runs else print('No runs')"
Compare the exported JSON paths against the task's column_mappings. For each template variable, confirm the mapped path actually exists. Common mismatches:
output to attributes.output.value on an experiment run (should be just output)input to attributes.input.value on a CHAIN span when the actual path is attributes.llm.input_messagescontext to a path that doesn't exist on the span kind being filtered4. Check that data_start_time is not epoch
If trigger-run used a start time of 0, 1970-01-01, or an empty string, the time window is invalid. Always derive from real span timestamps:
ax spans export PROJECT --space SPACE -l 5 --days 30 --stdout | python3 -c "
import sys, json
spans = json.load(sys.stdin)
for s in spans:
print(s.get('start_time', 'N/A'), s.get('end_time', 'N/A'))
"
5. Verify span kind matches evaluator scope
If the evaluator was created with --data-granularity trace but the task's query_filter is span_kind = 'LLM', the run may find no qualifying data and cancel. Ensure the granularity and filter are consistent.
6. Check that all template variables resolve
Every {variable} in the evaluator template must have a corresponding column_mappings entry that resolves to a non-null value. Test resolution against a real span:
ax spans export PROJECT --space SPACE -l 3 --days 7 --stdout | python3 -c "
import sys, json
spans = json.load(sys.stdin)
# Replace these paths with your actual column_mappings values
mappings = {'input': 'attributes.input.value', 'output': 'attributes.output.value'}
for i, span in enumerate(spans):
print(f'--- Span {i} ---')
for var, path in mappings.items():
parts = path.split('.')
val = span
for p in parts:
val = val.get(p) if isinstance(val, dict) else None
status = 'FOUND' if val else 'MISSING'
print(f' {var} ({path}): {status} — {str(val)[:80] if val else \"null\"}')
"
If any variable shows MISSING on all spans, fix the column mapping or adjust query_filter to target a different span kind.
See references/ax-profiles.md § Save Credentials for Future Use.