Adoption Leaderboard

Behavior Adoption Leaderboard

Scores a set of recent sales calls against five fixed behavior checklists (references/behavior-checklists.md) and ranks reps by adoption, lowest first — the reps most in need of coaching lead the report, not trail it.

Run this end to end in one pass. Don't stop to ask which calls to include, who's internal, or how to interpret an ambiguous call — apply the default rule in the relevant step below, decide it yourself, and note the assumption once. The user can correct any assumption after seeing the leaderboard; that's a quick re-run, not a precondition for the first one.

When to use this

• Prepping a 1:1 or team coaching session from call evidence instead of

manager impression.

• Running a team calibration: which behaviors is the team actually landing,

and which reps most need attention.

• Checking whether a specific behavior (e.g. rapport-building, surfacing

renewal risk) is landing consistently across a book of calls, not just on the calls a manager happened to listen to.

Step 1: Choose an input source

Two modes. Neither is the "real" one — use whichever the user has.

Connector mode — if this conversation has tools that can (a) list or search meetings/calls and (b) return call transcripts, use them. Match by capability, not by brand or vendor: any pair of list-calls + get-transcript tools works, whatever the source is called. If several are connected, prefer the one with organization-wide coverage and speaker emails on calls; say which one you picked and why. Verify the choice with one cheap call: list a single recent meeting before proceeding.

Local mode — if no such tools are present, or the user points at a directory instead, read transcript files directly: .txt/.vtt/.json/.md, same formats every other skill in this repo accepts. Speaker labels carry attribution when the source provides them; where they don't (generic "Speaker 1" labels), infer rep vs. external participant from context and state the inference once — see Step 3's edge handling.

If both a connector and local files are available, ask the user which to use; otherwise proceed on whichever exists without asking.

Step 2: The behaviors being scored

Five fixed checklists, defined in full in references/behavior-checklists.md: Rapport, Upsell opportunities, Renewal challenges, Customer experience, Value realization. Each carries one or more numbered checklist items (CH1.1, CH2.1, etc.) that get scored per call in Step 4. This skill scores against this fixed set — it doesn't take a custom behavior list.

Step 3: Gather calls

1. Connector mode: query workspace-wide, not just the calling user's own

calls — a personal/service-account scope often returns almost nothing. Pull newest first, using the tool's date range and pagination options. Local mode: read every transcript file in the directory the user pointed at.

1. Keep only external sales calls: at least one participant outside the

selling org. Ask the user for the org's own email domain if it isn't obvious from the data; if genuinely unavailable, infer internal vs. external from the majority participant domain across the files and state that inference once. Skip internal-only meetings, all-hands, recruiting interviews, and calls where the org is clearly the buyer being pitched by an outside vendor (see Step 4 for how to tell from the transcript).

1. Target coverage before settling: at least 10 qualifying external calls

and at least 5 distinct reps, where that many exist. If the first pull is thin, widen the window (connector mode: further back in history; local mode: check for more files) before settling for less. Never ask permission to widen — just widen.

1. Cap scoring at the 10 most recent qualifying calls. Label them C1

(newest) to C10. Label distinct reps S1, S2, … and keep a legend (name to label) for your own bookkeeping — the legend and the raw per-call grid are internal working state, never shown in the final output.

1. Fetch and score calls one at a time (Step 4) rather than accumulating raw

transcripts — keep only the scores, the legend, and one short evidence quote per satisfied item.

Step 4: Score each call

Full discipline in references/scoring.md — read it before scoring the first call. In short: every checklist item gets exactly 1 (transcript shows a rep doing it, with a quote as evidence) or 0 (absence is the evidence), no partial credit, no hedging language on an individual mark. Decide selling-vs-buying and internal-vs-external from transcript evidence, never by asking.

Step 5: Build the leaderboard

Roll per-item scores up to adoption percentages per references/scoring.md, then:

1. Table — rows are reps ordered by overall adoption, lowest first;

columns are Rep, Adoption (overall), then one column per behavior using its title (never BH1/BH2 codes). Every cell a whole number with a % sign, e.g. 43%. No calls column, no internal grid.

`` | Rep | Adoption | Rapport | Upsell opportunities | Renewal challenges | Customer experience | Value realization | |---|---|---|---|---|---|---| | S1 | 24% | 0% | 20% | 0% | 40% | 20% | | S2 | 46% | 40% | 33% | 60% | 40% | 60% | ``

1. Leaderboard narrative — lead with the team pattern: name the two or

three behaviors with the lowest adoption across the whole team, since those are the biggest, most actionable gaps. Then, lowest-adoption rep first, always show at least the bottom 5 reps (all of them if 5 or fewer): their current adoption, the behaviors dragging it down, and for each the one or two checklist items they miss most, with what to coach.

1. Summary line, filled from the actual data: "X of Y reps consistently run

these behaviors today (adoption 50% or higher); the rest do not."

Stop there. No projection table, no outreach step — the leaderboard and the coaching notes are the deliverable.

Sample data

assets/ ships 6 short synthetic transcripts across 4 reps (S1–S4), a deliberately mixed spread — some reps land most behaviors, some land almost none. Run local mode against skills/adoption-leaderboard/assets/ first:

claude "run adoption-leaderboard on skills/adoption-leaderboard/assets/"

What this does not do

No API calls beyond whatever connector the user already has open, no telemetry, no data retention beyond the current session, no outreach or email step. It reads what you point it at (or what a connector already present in the conversation returns) and nothing else.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: zime-ai
• Source: zime-ai/zime-gtm-skills
• License: MIT
• Homepage: https://zime.ai

Install and usage instructions live in the source repository linked above.