Glossary
The terms behind RevStack’s scores, dashboard, and stack map. Every entry has its own link; use the # beside a term to copy it.
The full scoring rules are on the methodology page. The current figures are on the dashboard.
Scores
How disconnected the tool stack is that one role is asked to hold together. Higher is worse.
What raises it: more tools named, two tools doing the same job (two CRMs, two engagement platforms), no integration plan or system of record, “manage all platforms” language.
What it is not: a tool count. A model assigns it against the bands below and writes a rationale.
How it is computed · Current average · The tools it refers to
How many distinct jobs one posting asks a single hire to do. Higher means less hireable as written.
What raises it: more skill categories, admin mixed with strategy or engineering with enablement, a junior title with senior scope, “own everything” with no team.
What it is not: a tool count. Skill categories drive it, not tools.
Four fixed groups per score that the dashboard uses to chart postings. They are not the scoring bands.
- Franken-Stack: Minimal below 35, Low risk 35–49, Moderate risk 50–64, High risk 65 and up.
- Unicorn JD: Focused below 40, Reasonable 40–59, Stretched 60–74, Unicorn 75 and up.
What the parser reads
The eight kinds of work a posting can ask for. How many a posting spans drives its Unicorn JD score.
adminConfiguring and maintaining systems: fields, permissions, page layouts, data hygiene.analyticsReporting, dashboards, forecasting, and analysis of funnel or revenue data.engineeringBuilding integrations, writing code or SQL, automation that needs a developer’s hands.strategyGo-to-market planning, territory and quota design, pricing or packaging input.process_designDesigning lead routing, deal stages, handoffs, and the rules between teams.enablementTraining reps, writing playbooks, onboarding people onto tools and process.project_managementRunning implementations, migrations, and cross-team rollouts.leadershipManaging people, owning budget, reporting to the executive team.
Six fixed labels the parser attaches when a posting shows a known problem. They are labels, not extra points on either score.
The model includes every flag that applies, and each should show up in the scoring rationale. In the methodology.
The seat mixes functions that are usually separate teams (admin + strategy + engineering in one JD).
One of the six red flags. Label only; no points added.
Scope and seniority do not match stated pay, or pay is missing on a senior/lead seat.
One of the six red flags. Label only; no points added.
Many tools named, no data model, integration strategy, or system-of-record language.
One of the six red flags. Label only; no points added.
Overlapping tools in the same category (two CRMs, two engagement platforms) without a consolidation plan.
One of the six red flags. Label only; no points added.
“You will own” language with no team, budget, or reporting support.
One of the six red flags. Label only; no points added.
No reporting line, no success metric, and a scope that could mean anything.
One of the six red flags. Label only; no points added.
How many distinct tools a posting names, including generics such as “CRM (unspecified)”.
An input to Franken-Stack, not the score itself. The distribution in the record.
The written reason the model gives for both scores, citing the posting.
A score without a rationale that points at the posting should not be quoted.
Data
The Supabase table the pipeline writes one row to for each collected RevOps posting, with its parse and scores.
The dashboard never lists raw postings. It reads aggregates of this table through materialized views, filtered as described in the contract.
The set of scored postings the dashboard currently counts, and the date of the newest one.
It grows when the daily pipeline adds postings. Cite it with its sample size and last-collected date, both shown on the dashboard.
Parser tool categories
The eleven keys the parser files each named tool under. One tool, one key.
The system of record for accounts, contacts, and pipeline.
Examples in the parser prompt: Salesforce, HubSpot, Pipedrive, Dynamics, unspecified CRM. CRM on the stack map.
Outbound sequences, call tasks, and multi-channel prospecting.
Examples in the parser prompt: Outreach, Salesloft, Apollo, sequences / cadences. Sales Engagement on the stack map.
Contact and company data, enrichment workflows, and lead intelligence.
Examples in the parser prompt: ZoomInfo, Clay, Lusha, Clearbit, Cognism. Enrichment & Data on the stack map.
Email campaigns, nurture flows, lead scoring, and lifecycle stages.
Examples in the parser prompt: Marketo, Pardot, Braze, Klaviyo, Mailchimp. Marketing Automation on the stack map.
Dashboards, reporting, product analytics, and data visualization.
Examples in the parser prompt: Looker, Tableau, Power BI, Metabase, Mode. BI & Analytics on the stack map.
Warehouses, ELT, reverse ETL, CDPs, and data modeling.
Examples in the parser prompt: Snowflake, dbt, Fivetran, Segment, BigQuery, Hightouch, Census. Data Infrastructure on the stack map.
Help desk, customer success platforms, and health scoring.
Examples in the parser prompt: Zendesk, Intercom, Freshdesk, Gainsight. Support & CS on the stack map.
Configure-price-quote, subscription billing, and revenue recognition.
Examples in the parser prompt: Stripe, Chargebee, Zuora, CPQ. CPQ & Billing on the stack map.
Workflow automation, API connectors, and lead routing logic.
Examples in the parser prompt: Zapier, Make, n8n, Workato, Tray. Integration / iPaaS on the stack map.
Conversation intelligence, forecasting, and AI assistants.
Examples in the parser prompt: ChatGPT, Claude, Copilot, Gong, Clari, 6sense. AI & Intelligence on the stack map.
The catch-all for anything that does not fit the other ten keys.
Examples in the parser prompt: Excel, SQL, Python, Slack, Jira, Confluence, anything that does not fit above.
On the stack map only
ABM & Intent and Conversational exist on the stack map but are not separate parser keys. When a posting names one of their tools, the parser files it under one of the eleven keys above, and it still counts toward the posting’s tool total.
Target account selection, intent data, and account-based campaigns.
Not a separate parser key. The parser has to place each of these tools in one of its eleven keys; 6sense, for example, is listed under ai_tools in the parser prompt. They still count toward the tool total. ABM & Intent on the stack map.
Live chat, chatbots, meeting scheduling, and inbound routing.
Not a separate parser key. Intercom is listed under support in the parser prompt; the others land in whichever of the eleven keys fits, with other as the catch-all. Each still counts toward the tool total. Conversational on the stack map.