sales_pipeline_leak_diagnosis v1.0.0

name: sales_pipeline_leak_diagnosis description: "Diagnose where a sales pipeline leaks, stalls, or goes stale from CRM data: stage-by-stage conversion, days-in-stage aging, stall detection, dollar impact of the biggest leak. Trigger when a user asks why deals are stalling, where pipeline leaks, to analyze pipeline health, forecast risk, or review stage conversion and deal aging."

Sales Pipeline Leak Diagnosis

Purpose

Read CRM pipeline data, find where deals leak or stall, benchmark each stage against the team's own baseline, and name the biggest leak with its dollar impact. Tool-agnostic: works from any CRM export (HubSpot, Salesforce, Pipedrive, spreadsheet) via a CSV the CLI reads.

Workflow

1. Get the data. Export deals with at minimum: deal id, stage, amount, stage entry date, deal created date, outcome (open/won/lost). Strongly preferred additions: last activity date, expected close date, owner, next step. See references/input_schema.md.
2. Run the diagnosis. python3 scripts/leak_diagnosis.py --csv deals.csv [--stages "Qualification,Discovery,Demo,Proposal,Negotiation"] prints a report and writes diagnosis.json. See references/.
3. Name the leak. The report ranks every stage by estimated revenue lost: entered_count x (1 - conversion_to_next) x avg_deal_value. The top row is the biggest leak. State it in dollars, not percentages: "$41K lost last quarter at Proposal-to-Negotiation" beats "38% conversion".
4. Check stalls separately. A healthy win rate can hide leakage when high-value deals sit stuck late-stage. List every open deal past 2x its stage's median days-in-stage, with severity (2.0x stuck, 3.0x+ dead weight) and risk flags (no activity 14+ days, no next step, close date past).
5. Benchmark, carefully. Compare each stage's conversion against the team's own baseline (median days-in-stage, stage conversion from last quarter). Industry reference ranges live in references/benchmarks.md but they are practice-based guides, not targets. Never present a rough industry range as a fact about the user's business.
6. Deliver findings + fixes. Follow references/output_template.md: the leak, the stalls, coverage and velocity numbers, and the highest-ROI fix (tighten qualification, add stage exit criteria, kill zombie deals, fix follow-up cadence). One recommended action, with the dollar math showing why.

Output Contract

Every diagnosis delivers, in this order:

1. Funnel table: per stage, deals entered, converted to next, conversion %, median/p90 days-in-stage.
2. The biggest leak: stage pair, dollar value lost, share of total leakage.
3. Stall list: open deals past 2x stage median, with age multiple and risk flags.
4. Coverage ratio (pipeline / quota) and pipeline velocity (($ opps x win rate x avg deal value) / avg cycle days).
5. One prioritized fix, priced in dollars recovered.

Operating Rules

• Accuracy rule: every number in the report comes from the input data. No invented benchmarks, no confident claims about data you were not given. Unverifiable = cut or frame as opinion.
• Never diagnose a stage the data cannot support. If there is no stage history, say stage distribution only, not conversion. A current-stage snapshot alone cannot produce true stage-to-stage conversion.
• Label every industry benchmark as a rough reference with its source. The team's own baseline is always the primary comparison.
• Zombie deals (3x+ median days-in-stage, no activity 14+ days) get named, not averaged into the stage stats silently. Flag and offer the qualify-out-or-revive decision.
• Do not advise fabricating CRM activity or editing deal history to make numbers look better. Diagnosis means seeing the real picture, not dressing it.
• The CLI is the source of truth for the math. Recompute, never estimate, the leak dollars.