examining-ado-project

name	examining-ado-project
description	Builds a picture of work in an ado project: activity volume, spaces and operations created over time, experiments and operation configs used etc. Use to create a project/context overview report, summarize what the team has been doing in an ado project, report trends across spaces/operations, or to onboard onto an ado project.

Examining an ado Project

End-to-end workflow to summarize all discoveryspaces, operations, and related metadata in the ado project associated to the active context.

• Run all commands from the repository root with uv run (see using-ado-cli).
• See projects and contexts for details on what projects and contexts are
• Users may refer to an ado project using either the term "project" or "context"

Tips

• Prefer metastore listing and YAML dumps before heavy uv run ado show data pulls; see query-ado-data.
• For one space in depth: examining-discovery-spaces.
• For one operation in depth: examining-ado-operations.

Pre-requisites - Check active context is associated with expected project

Context names and project names are identical by construction.

If asked to examine a specific named project:

1. Check the active context name: uv run ado context
   • If it has the correct name, continue to next step
   • If not, run uv run ado get contexts to list all available contexts
     • If one matches, switch to it: uv run ado context $NAME
     • If none match, inform the user that a context for the specified project cannot be found

1. Overview: activity and types

Goal: volume of work, recency, and which spaces attract the most operations.

1. Spaces (tabular, with metadata)

   uv run ado get spaces --details
   

   Use age (list is age-sorted, most recent last), name, description, and labels to infer themes and activity.

2. Operations (tabular, with metadata)

   uv run ado get operations --details
   

   Relates operations to target spaces; age and labels summarize recent work.

   Heuristic — operation IDs: many ids encode the operator and a version segment, e.g. OPERATOR_NAME-VERSION-...-UID (exact shape varies). Use this together with uv run ado get operator --details to understand more about the operators used.

3. Operations (with measurement statistics)

   uv run ado get operations -o stats --output-file operations-stats.txt
   

   Adds TOTAL_RESULTS, SUCCESSFUL_RESULTS, FAILED_RESULTS, and MEASURED_ENTITIES columns. For explore operations, compare MEASURED_ENTITIES against the operator's numberEntities to check sampling completeness.

   For richer stats that also include request-level counts (TOTAL_REQUESTS, FAILED_REQUESTS, SUCCESSFUL_REQUESTS):

   uv run ado show stats operation --output-file operations-fullstats.csv -o csv
   

4. Discovery Spaces (with statistics)

   uv run ado get spaces -o stats --output-file spaces-stats.txt
   

   Adds EXPERIMENTS, OPERATIONS, EXPLORE_OPERATIONS, and MEASURED_ENTITIES columns.

   For richer stats that also include full entity-space coverage columns (SIZE_OF_ENTITY_SPACE, UNMEASURED_ENTITIES, MATCHING_ENTITIES, etc.):

   uv run ado show stats discoveryspace --output-file spaces-fullstats.csv -o csv
   

   Performance note: ado show stats discoveryspace is slower than ado get spaces -o stats as it instantiates each DiscoverySpace. Prefer ado get -o stats for a quick overview across many spaces.

5. Sample Stores (with statistics)

   uv run ado get samplestores -o stats --output-file samplestores-stats.txt
   

   Adds ENTITIES, RESULTS, and EXPERIMENTS columns.

6. Data Containers (with statistics)

   uv run ado get datacontainers -o stats --output-file datacontainers-stats.txt
   

   Adds TABLES, LOCATIONS, KEY_VALUES, and DATA_BYTES columns.

Synthesis: cluster mentally (or in notes) by creation time to see bursts of activity; count operations per space from the operations listing to see which spaces are busiest.

2. Deeper pass: full YAML and experiments

Goal: experiments/actuators in use, operation parameters, and how much was submitted for measurement (entities selected by explore-style operators—may differ from completed measurements if runs failed).

Dump full resource documents for easier scripted or batched reading. Use --output-file to ensure proper file handling:

uv run ado get spaces -o yaml --output-file spaces.yaml
uv run ado get operations -o yaml --output-file operations.yaml

On large metastores, these files can be very large—prefer uv run ado get … -q … filters, scripts, or the SQL store API (see query-ado-data) before dumping everything.

When interpreting YAML fields, confirm paths against resource schemas:

uv run ado template discoveryspace --include-schema
uv run ado template operation --include-schema

From space YAML: note experiment and actuator identifiers referenced by each space.

Gain further information on the experiments using

# Outputs description of actuators used
uv run ado get actuators --details
# Outputs description of experiments used
uv run ado get experiments --details
# Outputs detailed information on an experiment
# Use to drill down into most used experiments
uv run ado describe experiment $EXPERIMENT_ID

From operation YAML: note actuatorconfigurations used (if any), read parameters (operator-specific), target discoveryspace references, and any fields that indicate entity count / batch / sample configuration for explore operations.

Synthesize:

• What is being measured and with which experiments (domain of the project).
• Which explore (or similar) operations drove the largest submitted entity sets and on which spaces.
• Whether experiment definitions or parameters shift over time (versions, configs).

3. Space relationships and entity-space shape

From step 2, pick a handful of commonly operated on or recent spaces. For each, inspect its fragment in spaces.yaml, focusing on entity space structure (dimensions, bounds, representation).

Find Spaces that match these spaces (refinement, expansion, or parallel configurations)

uv run ado get spaces --matching-space-id SPACE_ID

Use the output to

• Group spaces that match
• Within each group identify subgroups whose spaces are identical
• If multiple subgroups identify the hierarchy between them (broadest to narrowest)
• Within subgroups identify what if measurement space is different between them
• Combine this information with the sequence of space creation to understand how researchers have been evolving the spaces

Optionally complement with one-hop links:

uv run ado show related space SPACE_ID

4. Report template

Write a concise markdown report

• Write the report to reports/<ado_context_name>/ (create the directory if needed), where ado_context_name is the active ado metastore context (uv run ado context).
• Write the report as project_<YYYY-MM-DD>_report.md.
• If a report already exists, check whether there has been meaningful activity since it was written before replacing it:
  1. Run uv run ado get spaces --details and uv run ado get operations --details and note the age of the most recent resource.
  2. If the most recent resource is younger than the date of the existing report, there has been new activity — proceed to write a new report.
  3. If not, ask the user whether they want to replace it.
  4. If finer-grained confirmation is needed, fetch the YAML of the most recent space or operation (uv run ado get space SPACE_ID -o yaml --output-file SPACE_ID.yaml, or the same pattern for operation) and read its creationTimestamp field.

Project summary

• Domains or problems implied by experiments, actuators, and space descriptions.
• Dominant operation and experiment patterns.

Latest activity

• Most recent spaces and operations (from --details listings).
• What the latest work seems focused on (labels, names, target spaces).

Spaces overview

• Which spaces are most used and most analyzed.
• How entity spaces and matching-space relationships evolve: expanding, narrowing, or shifting configuration.

Operations overview

• Operator mix and whether parameters or operator choice evolve over time.
• Which operations submitted the most entities (from operation YAML/config) and which produced the most results (from the stats).
• What analysis-style operations ran (infer from operator names and parameters).
• Make a note of operations with failed measurements and highlight ones with abnormal failure rates (from the stats).