Metric Auditor

Audit your Fullstory account — find duplicate metrics, stale segments nobody uses, misconfigured objects, and naming inconsistencies. Keeps the shared workspace clean so the team trusts the numbers.

When This Runs

Invoked automatically when general-analysis finds multiple candidates for a search and needs to determine which one is canonical. Also runs when the user explicitly asks:

• "Are there duplicate metrics in our account?"
• "Clean up our segments — which ones are stale?"
• "What metrics exist for checkout?"
• "Is anyone actually using this segment?"

Mental Model

Fullstory accounts accumulate cruft over time — metrics built for a one-off question, segments created for a meeting three months ago, copies of copies with slightly different names. This skill finds and diagnoses that cruft.

Workflow

Step 1: Discover everything

Search broadly to get the lay of the land:

fullstory:get_metric(regex="")  → list all metrics
fullstory:get_segment(regex="") → list all segments

If the account is large, narrow by keyword: fullstory:get_metric(regex="checkout").

Step 2: Check popularity

For every object found, call fullstory:get_view_count (up to 10 IDs at a time):

• High view count: Actively used — canonical, trusted.
• Low view count: Maybe stale — built once, never referenced again.
• Zero views: Almost certainly stale — safe to flag for cleanup.

Step 3: Identify issues

Duplicates: Two metrics with nearly identical names and the same output type. Example: "checkout conversion" and "checkout conversion rate" both returning single_number. Flag: pick the one with higher view count as canonical.

Stale objects: Zero views and created more than 30 days ago. Flag for deletion.

Naming problems: "Untitled metric", "Segment 1", "Copy of checkout funnel". Suggest renaming.

Misconfigured: A segment that filters by a property that no longer exists, or a trend metric built as single_number. These are harder to detect without computing — flag if the name doesn't match the output type.

Orphans: A metric that references a segment that no longer exists (the segment_id was deleted). These will error on compute — flag them.

Step 4: Present the audit

## Metric Audit

### Duplicates (2 pairs)
1. "checkout conversion" (247 views) vs "checkout conversion rate" (12 views)
   → Keep the first, delete or rename the second
2. "signup page views" (89 views) vs "Signup Page views" (3 views)
   → Keep the first (consistent casing), delete the second

### Stale (5 objects, 0 views)
- "Q1 experiment metric" (created Jan 15, never viewed)
- "Test segment for meeting" (created Mar 3, never viewed)
- ... (3 more)

### Naming issues (3 objects)
- "Untitled metric" → rename to describe what it measures
- "Segment 1" → rename to describe the cohort
- "Copy of checkout funnel" → merge with original or rename

### Total: 47 metrics, 23 segments. 5 objects flagged.

Want me to rename the canonic, delete the stale ones, or dig into any specific object?

Step 5: Clean up (if user says yes)

For duplicates: rename the canonical one (if needed), flag the duplicate for deletion. For stale objects: confirm before deleting — the user might know something you don't. For naming: suggest a new name and apply it with fullstory:update_metric or fullstory:update_segment.

Guidelines

• Never delete anything without explicit confirmation. Flagging is safe; deleting is not.
• When ranking by view count, treat anything with 5x or more views than the next candidate as canonical.
• If the account has >50 metrics or segments, don't list them all in the response. Summarize with stats (total, stale count, duplicate count) and offer to drill into specific categories.
• An object with zero views might be important — it could be brand new, or used via the API (which doesn't increment view count). Flag with a warning, don't assume it's garbage.
• Focus on actionable findings. A clean audit with nothing to fix is a good audit — report that and move on.