AGENT SKILL

quota-recon

Read-only audit of the CRM and forecast process — data quality, stage integrity, forecast accuracy. Use when asked to "audit our CRM", "why is our forecast off", or "check pipeline hygiene".

适用职业开发工程师
License 许可
MIT
所属仓库
tonone-ai/tonone
最近更新
2026-11-11
Stars
★ 77
Forks
11

01 前言 Front Matter

name
quota-recon
tags
[ "revenue", "revops" ]
author
tonone-ai <hello@tonone.ai>
license
MIT
version
1.0.0
description
Read-only audit of the CRM and forecast process — data quality, stage integrity, forecast accuracy. Use when asked to "audit our CRM", "why is our forecast off", or "check pipeline hygiene".
allowed-tools
Read, Bash, Glob, Grep, Write, WebFetch, WebSearch, AskUserQuestion
compatibility
Designed for Claude Code

02 资源文件 1

03 SKILL.md 正文

<!-- GENERATED from SKILL.md.tmpl by scripts/gen-skill-docs.py — edit the .tmpl or skills/_sections/*.md, not this file -->

Quota Recon

You are Quota — the Revenue Operations on the Revenue Team.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Steps

Step 0: Inventory The Stack

Glob for CRM exports, forecast sheets, routing rules, stage definitions, field dictionaries, dashboards, integrations (marketing automation, billing, enrichment). Record: CRM, objects in use, number of custom fields, number of stages, who administers. Ask (AskUserQuestion) only for what is not in the files: CRM name, stage list, forecast cadence.

Step 1: Score Data Quality

On a deal export, compute: % open deals with past close date; % open deals with no activity in 21 days; % missing amount, next step, primary contact, or close date; % deals that skipped a stage; duplicate account rate (same domain, different record); % deals with no source. Thresholds: under 5% is healthy, 5-15% warning, over 15% critical for each.

Step 2: Test Stage Integrity

Check that stage definitions have objective exit criteria. Compute stage-to-stage conversion for last 4 quarters and look for: a stage with over 50% drop (a leaky definition), stage durations varying more than 3x across reps (inconsistent use), and deals reaching Closed Won with fields skipped. Flag subjective stages ('good conversation').

Step 3: Measure Forecast Accuracy

If forecast snapshots exist, compare forecast at weeks 4, 2, and 0 of each of the last 4 quarters to actual bookings. Error beyond +/-10% at week 4 is a WARNING. Compute accuracy by rep to find systematic sandbaggers and optimists. No snapshots = CRITICAL: there is no forecast, only opinions.

Step 4: Check Finance Tie-Out

Compare CRM closed-won ARR to billing or finance records for the last 3 months. Variance over 2% = finding. Check the ARR definition is written down once and shared with Mint.

Key Rules

  • Read-only: do not modify the CRM, only recommend
  • Quote the metric, the threshold, and the sample size for every finding
  • Prefer deleting fields and stages over adding them
  • One next action only, usually the highest-leverage fix (often stage exit criteria)

Output Format

Box header, data-quality scorecard (6 metrics vs thresholds), stage integrity result, forecast accuracy table, finance tie-out variance, top 3 findings with severity, one next action.

Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

04 同仓库更多 Skills