Distribution analysis
Official Chili Piper Skills and ChatGPT GPTs for the Chili Piper MCP — meeting diagnostics, routing audits, no-show analysis, availability checks, and user onboarding/offboarding.
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Analyzes a Chili Piper round-robin distribution for a workspace and date range — meeting counts by rep, imbalance vs. configured weights, day-of-week and source skew, and cancellations. Use when asked why a rep gets more or fewer meetings, or for a distribution breakdown.
SKILL.md
8.1 KB, as published. Nobody here has run it
Distribution Analysis
You are a RevOps analyst. Pull a Chili Piper distribution's configuration and the meetings its member reps hosted over a date range, then surface imbalance patterns and likely causes — using only the public Chili Piper MCP.
Prefer live data over training. MCP field names and tool signatures change. Load
references/api-reference.mdbefore making MCP calls — it is the canonical field-name truth for this skill.
Scope & honesty. The public MCP cannot filter meetings by
distributionIdand has no distribution config-history endpoint. This skill attributes meetings to a distribution by its member reps (host). If a rep belongs to multiple distributions, their meetings count toward each — state this caveat in the output. For exact per-distribution routing attribution, use the routing logs (/audit-routing,concierge-logs).
When to use
- Someone asks why a rep is getting more or fewer meetings than others.
- Someone wants a distribution breakdown (counts by rep, imbalance vs. weights).
- Someone wants to analyze meeting imbalance for a workspace over a period.
Inputs
| Input | Required | Default | What it controls |
|---|---|---|---|
workspace | ✅ | — | Workspace name or ID containing the distribution. |
distribution | ✅ | — | Distribution name (substring) or distributionId to analyze. |
start_date | ✅ | — | Start of range, inclusive (e.g. 2026-05-01). |
end_date | ✅ | — | End of range, exclusive (e.g. 2026-06-01 = through May 31). |
If a required input is missing, ask for it in one sentence rather than guessing.
Process
Step 1 — Resolve the workspace
If workspace is a name, call workspace-list and match on name; use its id.
tool: workspace-list
args:
pagination:
page: 0
pageSize: 100
Step 2 — Find the distribution and read its config
tool: distribution-list-put
args:
workspaceIds: [<workspace.id>]
name: <distribution> # omit if you were given a distributionId; filter the array instead
Extract name, active members, weights, handling/algorithm, capping, and per-member
period statistics from the matching item; derive the per-rep ideal number. If no
distribution matches, say so and list the available distribution names in the
workspace. Config fields, period statistics, and the idealNumber derivation →
references/api-reference.md § distribution-list-put — config fields and
§ distribution-list-put — period statistics (authoritative totals).
Step 3 — Resolve member names
Collect the active member userIds and resolve them to names/emails:
tool: user-find-by-ids
args:
userIds: [<userId>, <userId>, ...]
Build a userId → name map for display. Never show raw user IDs in the final output.
Step 4 — Pull meetings hosted by the members
meeting-list-put has a 7-day maximum window. Split [start_date, end_date) into
≤7-day chunks and call once per chunk, paginating while hasMore === "Yes". Merge all
chunks, dedupe on meetingId, then keep only meetings whose hostId is one of the
distribution's active members and classify each kept meeting. Windowing, pagination,
and the status classification → references/api-reference.md § Hard API limits and
§ meeting-list-put — classification and pattern fields.
Step 5 — Build the rep breakdown and detect imbalance
Use two complementary sources.
From statistics (distribution API, current period — authoritative totals):
use assigned as the primary volume metric, plus cancelled, noShow, and the
reassignedToThis / reassignedFromThis rebalancing context (statistic field
meanings and the effective-total formula → references/api-reference.md
§ distribution-list-put — period statistics (authoritative totals)).
Derived from statistics:
- Ideal number:
(userWeight / totalWeight) × totalAssigned— the fair-share target for each rep given their configured weight - Imbalance ratio: top rep's
assigned÷ median rep'sassigned - Vs. configured weight: compare each rep's
assigned / totalAssignedto theirweight / totalWeight. A rep with a high weight share but lowassignedshare (or vice versa) is the headline finding.
From meeting-list-put (date-range data — for patterns): day-of-week skew,
booking source, weekly trend (meetings per rep per week — did a gap open at a specific
week?), and cancellation breakdown. Which fields carry these patterns →
references/api-reference.md § meeting-list-put — classification and pattern fields.
Flag reps with 0 assigned (likely calendar/availability issue — suggest
/check-availability) and reps whose assigned / totalAssigned share diverges sharply
from their weight / totalWeight share.
Step 6 — Output
Lead with the config + rep breakdown, then patterns, then recommendations. Include the
attribution caveat whenever a member belongs to more than one distribution. Exact
layout → references/output-format.md.
Preflight audit
Verify before writing output:
- Required inputs present and resolved (
workspace→id,distribution→ matching item). - Field names taken from
references/api-reference.md, not guessed. -
meeting-list-putcalls each span ≤ 7 days and are fully paginated (hasMore === "No"). - Meetings filtered to active-member
hostIds and deduped onmeetingId. -
idealNumberderived client-side; user IDs resolved to names (no raw IDs shown). - Multi-distribution attribution caveat included when any member is in >1 distribution.
Checkpoint
This is a read-only diagnostic. Present the config, rep breakdown, patterns, and
recommendations, then stop and let the human decide: rebalance weights, fix
calendar/availability for an under-booked rep, or adjust the distribution in the router
builder. Applying any rebalancing requires the router builder, or
distribution-adjust-v3 with the human's explicit go-ahead since it publishes
immediately.
Data handling
- PII present: rep names/emails and guest data in meetings — used for analysis; display rep names, not guest details, unless asked
- Storage: ephemeral — nothing persists after the skill completes
- Writes: none — read-only. Apply rebalancing manually, or via
distribution-adjust-v3only with explicit human approval (it publishes immediately).