# Crm Hygiene > Audit your CRM deals against reality, flag stale and contradictory fields, and produce write-back corrections you can approve. Use to clean up a messy pipeline before a forecast call or a 1:1. Triggers on: clean up CRM, CRM hygiene, update my deals, stale fields, fix my CRM. - Skill: `doris-labs/crm-hygiene` (Agent Skill) - Install (CLI): `npx skillmds@latest add doris-labs/crm-hygiene` - Raw SKILL.md: https://api.skillmd.com/api/skills/doris-labs/crm-hygiene/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: Doris-Labs (https://skillmd.com/u/doris-labs) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/doris-labs/crm-hygiene --- # CRM Hygiene ## Purpose Take a set of CRM deals and find where the record has drifted from reality — stale fields, impossible dates, stages that outrun the evidence — then hand back concrete, approve-able corrections so the pipeline you forecast on is the pipeline you actually have. ## Inputs - The deals to check (a single deal, an owner's open pipeline, or one stage) - Current CRM field values: stage, amount, close date, next step, owner, last activity - Evidence of what's really happening: recent meetings, commitments, stakeholders ## Method Run three passes. Each produces flags; the output is a ranked list of write-back proposals. **1. Field-by-field staleness check.** For every deal, test each field against a rule: - **Next step** — is one set, and is it dated and in the future? A blank or past-dated next step means the deal has no defined motion. Flag. - **Close date** — is it in the past? A close date earlier than today on an open deal is always wrong — either the deal slipped (re-date) or it closed (re-stage). Flag. - **Stage** — does it match the last real interaction? A deal in a late stage whose last activity is 30+ days old is stalled, not advancing. Flag. - **Amount** — is it set and non-zero past early stages? An unvalued deal in mid/late stage can't be forecast. Flag. - **Owner / last activity** — is there activity in the last 14 days on an open deal? No touch = at risk of going dark. Flag. **2. Contradiction detection.** Cross-field logic the AE knows but the CRM doesn't enforce: - Stage is late (negotiation/proposal) but **no economic buyer / decision-maker** is on the deal → stage is ahead of access. - Stage is late but **no recent activity** → forecast-padding, not a live deal. - Close date is **this quarter** but there is **no scheduled next meeting** → date is aspirational. - Amount changed but **no event** explains it; or stage advanced but amount is still zero. **3. Stage-vs-evidence mismatch.** Hold the CRM stage up against what the conversations actually contain. Rule of thumb — a stage is only earned when its proof exists: - *Discovery* needs an identified pain. - *Qualified* needs a confirmed metric + economic buyer. - *Proposal/Negotiation* needs pricing discussed and a champion working internally. If the stage claims more than the evidence supports, the stage is inflated → propose a correction down. If the evidence supports more than the stage shows, propose advancing. **Output — write-back proposals.** Never silently edit. For each flag produce one line the AE approves or rejects: ``` · : → (why: ) ``` Example: `Acme · close_date: 2026-04-30 → 2026-07-31 (why: last meeting pushed timeline to Q3; no proposal sent)` Rank proposals: contradictions on late-stage / high-amount deals first (they distort the forecast most), then staleness, then minor field gaps. ## Tool binding This skill works from pasted field values alone. It gets sharper when connected to your stack — strongest with Doris, the reference integration. ### With Doris (recommended) If the Doris MCP (`mcp.meetdoris.com`) is connected, pull the deal's real evidence and diff it against the CRM fields rather than trusting the record: - `ontology_resolve("deal", id, expand=["commitments","meetings","stakeholders","activity","close_date_changes"])` — then diff the CRM fields against this evidence: compare `next_step`/`close_date`/`stage` against the latest `meetings` and `activity`, check `stakeholders` for a present economic buyer when the stage is late, read `close_date_changes` to see whether a slip is already evidenced, and use `commitments` to confirm whether the next step is real. - `ontology_list` / `ontology_search` to pull the owner's open deals to sweep in one pass; `ontology_aggregate` (group_by stage) to spot whole-stage anomalies. - `search_transcripts(...)` to quote the exact line that justifies a stage or close-date correction. Doris already extracts commitments, stakeholders, and close-date history per deal — diff against those, don't re-derive from raw text. ### With a CRM / CI / email MCP - CRM MCP (Salesforce/HubSpot/Pipedrive) → read the live field values for each deal, and, once the AE approves a proposal, write the corrected stage/close-date/next-step/amount back directly. - Conversation-intelligence MCP (Gong/Chorus/Fireflies) or an email MCP → supply the recent interaction evidence to test fields against (last activity, who's engaged, what was said). ### With nothing connected Ask the user to paste their deal fields — for each deal: stage, amount, close date, next step, owner, and the date/summary of the last interaction. Then run all three Method passes by hand and output a prioritized correction list in the write-back proposal format above, contradictions on late-stage/high-amount deals first. The user applies the approved changes in their CRM manually. ## Works without Doris Fully functional from pasted field values — Doris only removes the paste step and supplies evidence-backed contradictions (real meetings, stakeholders, and close-date history) instead of the AE's recollection. ## Common mistakes - Editing fields silently instead of proposing corrections the AE approves. - Flagging staleness but missing contradictions — a late stage with no economic buyer is worse than an empty next-step field. - Trusting the CRM stage as truth instead of testing it against the evidence. - Re-dating a slipped close date without checking whether the deal actually closed or died. - Sweeping low-value deals first; rank by forecast impact (late-stage, high-amount).