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Ai-analyst runs

Skill: Runs

install
source · Clone the upstream repo
git clone https://github.com/ai-analyst-lab/ai-analyst
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/ai-analyst-lab/ai-analyst "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/runs" ~/.claude/skills/ai-analyst-lab-ai-analyst-runs && rm -rf "$T"
manifest: .claude/skills/runs/skill.md
source content

Skill: Runs

Purpose

Browse, inspect, compare, and clean up past pipeline runs. Each run is a self-contained directory under 

working/runs/
with its own working files, outputs, and pipeline state.

When to Use

  • User says 
    /runs
    , 
    /runs list
    , 
    /runs latest
    , 
    /runs clean
    , or 
    /runs compare
  • When the user wants to see what analyses have been executed

Invocation

  • /runs
    or 
    /runs list
    -- list all past runs
  • /runs latest
    -- show details of the most recent run
  • /runs {id}
    -- show details of a specific run (partial match supported)
  • /runs clean
    -- remove runs older than 30 days (confirmation required)
  • /runs compare {id1} {id2}
    -- compare two runs side by side

Instructions

Step 1: Scan Run Directory

Read 

working/runs/
directory. Each subdirectory is a run, named:

{YYYY-MM-DD}_{DATASET}_{SHORT_TITLE}/

For each run directory, read 

pipeline_state.json
to extract:

  • pipeline_id
    -- timestamp identifier
  • dataset
    -- dataset name
  • question
    -- the business question
  • status
    -- 
    completed
    , 
    failed
    , 
    paused
    , or 
    running
  • run_dir
    -- full path
  • started_at
    , 
    completed_at
    -- timing
  • steps
    -- agent status map (to compute agent counts)

If 

pipeline_state.json
is missing, infer status as 
unknown
and derive date/dataset from the directory name.

Step 2: Execute Command

List (

/runs
or 
/runs list
):

Display a table sorted by date descending:

Pipeline Runs (working/runs/)

| # | Date       | Dataset   | Title                    | Status    | Agents |
|---|------------|-----------|--------------------------|-----------|--------|
| 1 | 2026-02-23 | acme-analytics | why-revenue-dropped-q3   | completed | 14/14  |
| 2 | 2026-02-21 | acme-analytics | activation-funnel-deep   | failed    | 8/14   |
| 3 | 2026-02-19 | hero      | churn-by-segment         | completed | 14/14  |

3 runs found. Use `/runs {#}` or `/runs {date_dataset_title}` for details.

The 

Agents
column shows 
{completed}/{total}
from the step map.

Latest (

/runs latest
):

Read 

working/latest
symlink target. Display the detail view (same as 
/runs {id}
).

Detail (

/runs {id}
):

Match 

{id}
against run directory names (supports partial match -- e.g., 
/runs acme-analytics
matches the most recent acme-analytics run). Display:

Run: {directory_name}
Status: {status}
Dataset: {dataset}
Question: {question}
Started: {started_at}
Completed: {completed_at} ({duration})

Agent Status:
  completed: {list}
  failed: {list with errors}
  skipped: {list}
  pending: {list}

Output Files:
  - {RUN_DIR}/outputs/{file1}
  - {RUN_DIR}/outputs/{file2}
  ...

Confidence: {grade from validation if available}

If the run has a validation report, extract and show the confidence grade.

Clean (

/runs clean
):

  1. Identify runs older than 30 days (based on directory date prefix)
  2. List them and ask for confirmation: 
    Found {N} runs older than 30 days:
  - {dir1} (completed, {date})
  - {dir2} (failed, {date})

Delete these runs? This cannot be undone. [y/N]
  3. On confirmation, remove the directories
  4. If 
    working/latest
    pointed to a deleted run, remove the symlink

Compare (

/runs compare {id1} {id2}
):

Load 

pipeline_state.json
and key output files from both runs. Display:

Comparing Runs:
  A: {dir1}
  B: {dir2}

| Dimension          | Run A              | Run B              |
|--------------------|--------------------|--------------------|
| Date               | {date_a}           | {date_b}           |
| Dataset            | {dataset_a}        | {dataset_b}        |
| Status             | {status_a}         | {status_b}         |
| Agents completed   | {count_a}          | {count_b}          |
| Confidence grade   | {grade_a}          | {grade_b}          |
| Charts generated   | {chart_count_a}    | {chart_count_b}    |
| Key findings       | {finding_count_a}  | {finding_count_b}  |
| Duration           | {duration_a}       | {duration_b}       |

If both runs analyzed the same dataset, also compare:

  • Top 3 findings from each (extracted from analysis reports)
  • Any metrics that differ significantly

Edge Cases

  • No runs directory: Report "No pipeline runs found. Use 
    /run-pipeline
    to start one."
  • Empty runs directory: Same message as above
  • Corrupted pipeline_state.json: Show run with 
    status: unknown
    , note the error
  • Partial match ambiguity: If multiple runs match, list them and ask user to be more specific
  • Legacy runs (no run directory): Note: "Found legacy 
    working/pipeline_state.json
    -- not in per-run format. Use 
    /run-pipeline
    to create a tracked run."