TAM Expertise
Parent: TAM Operating Reference · Published reference · snapshot 2026-09-08 · skill tam-operations/references/tam-expertise.md
↓ Facts as markdownall context files
Reference skill compiled from 120+ authoritative sources. Full context in tam-expertise-context.md.
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TAM Expertise — BI, Reporting, Writing, Risk, Churn, Sentiment & Account Management
- Reference skill compiled from 120+ authoritative sources. Full context in tam-expertise-context.md. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#tam-expertise-bi-reporting-writing-risk-churn-sentiment-account-management]
When NOT to use
- MongoDB Premium Services operating procedures → use tam-reference [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#when-not-to-use]
- Active case management and TS Tools API → use case-tracker [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#when-not-to-use]
- Atlas cluster diagnostics and troubleshooting → use atlas-diagnostics-expert [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#when-not-to-use]
- Code review, frontend design, or implementation tasks → use domain-specific skills [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#when-not-to-use]
When to use
- Writing or reviewing account deliverables (EBRs, QBRs, architecture reviews, post-mortems, runbooks, migration guides, case notes) [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#when-to-use]
- Assessing account health, churn risk, or customer sentiment [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#when-to-use]
- Building KPI frameworks, dashboards, ROI analyses, or adoption reports [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#when-to-use]
- Applying communication frameworks (BLUF, STAR, Pyramid, SCQA, Diataxis) [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#when-to-use]
- Onboarding accounts (30-60-90), managing escalations, navigating stakeholder dynamics [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#when-to-use]
- Benchmarking SaaS metrics, calculating NRR, or scoring customer health [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#when-to-use]
Skill guidance
- Treat tam-expertise-context.md in this directory as the source of truth for frameworks, templates, and benchmarks. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#skill-guidance]
- Prefer the frameworks, checklists, templates, and benchmarks in the context before improvising. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#skill-guidance]
- Cross-reference with tam-reference for MongoDB Premium Services specifics. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#skill-guidance]
- Cross-reference with case-tracker for case management specifics. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#skill-guidance]
- Apply the Diataxis framework for document structure and BLUF/Pyramid/STAR for communication. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#skill-guidance]
- SaaS benchmarks are 2025-2026 vintage; verify current figures for customer-facing deliverables. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#skill-guidance]
Common mistakes
- Mixing Diataxis types: a runbook (how-to) that drifts into explanation, or a reference doc that tries to teach via tutorial. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#common-mistakes]
- Unoperationalized RYG scores: every score state needs a mandatory action, not just a color. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#common-mistakes]
- Benchmarks without recommendations: presenting a number without a prescriptive next step. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#common-mistakes]
- Score inflation: defaulting accounts to Green without data-backed justification. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#common-mistakes]
- Single-audience framing: writing for engineers when the deliverable serves both engineers and executives. Use layered structure: exec summary → findings → technical appendix. [archived source: https://llms-explorer.com/sources/mdb-context-hub/tam-expertise/#common-mistakes]
Where this helps
- Writing or reviewing an EBR, QBR, architecture review, post-mortem, runbook, migration guide, or case note and needing the right communication framework — BLUF, STAR, Pyramid, SCQA, Diataxis — for the job. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Assessing account health, churn risk, or customer sentiment as part of a recurring account review. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Building a KPI framework, dashboard, ROI analysis, or adoption report for a customer-facing deliverable. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Deciding whether a question belongs here or should route to a sibling skill — MongoDB Premium Services procedures, active case management, or Atlas diagnostics each have their own dedicated skill. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
How to apply this
- Treat the compiled tam-expertise-context.md as the source of truth for frameworks, templates, and benchmarks, and prefer it over improvising a new structure for a deliverable. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Match the Diataxis type to the deliverable's actual job — a runbook stays how-to, a reference doc stays reference — rather than letting one document drift between types. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Operationalize every RYG (Red/Yellow/Green) health score with a mandatory action tied to each state, not just a color with no next step. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Pair every benchmark cited in a deliverable with a prescriptive recommendation, rather than presenting a number and leaving the reader to infer what to do about it. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
Antipatterns
- Mixing Diataxis types within one document — a runbook that drifts into explanation, or a reference doc that tries to teach via tutorial — confusing the reader about what the document is for. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Scoring an account Green by default without data-backed justification, inflating apparent health and delaying the intervention a true Yellow or Red would have triggered. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Using this skill for MongoDB Premium Services operating procedures, active case management, or Atlas diagnostics, when those each have a dedicated skill (tam-reference, case-tracker, atlas-diagnostics-expert) built for exactly that. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Presenting a benchmark without a recommendation attached, leaving a customer-facing number with no clear next step for the reader. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
Limitations
- This reference is compiled from 120+ sources with the full context living in a separate context file, tam-expertise-context.md; the pack itself is a pointer to that depth, not a complete substitute for reading it. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- It explicitly does not cover MongoDB Premium Services operating procedures, active case management or the TS Tools API, Atlas cluster diagnostics, or code and frontend implementation — those require the sibling skills named in its own "When NOT to use" section. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Frameworks like BLUF, STAR, Pyramid, and SCQA are communication structures, not guarantees of a good deliverable — applying the right framework to weak underlying analysis still produces a well-structured but unconvincing document. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
Where this helps
- Writing or reviewing an EBR, QBR, architecture review, post-mortem, runbook, migration guide, or case note and needing the right communication framework — BLUF, STAR, Pyramid, SCQA, Diataxis — for the job. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Assessing account health, churn risk, or customer sentiment as part of a recurring account review. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Building a KPI framework, dashboard, ROI analysis, or adoption report for a customer-facing deliverable. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Deciding whether a question belongs here or should route to a sibling skill — MongoDB Premium Services procedures, active case management, or Atlas diagnostics each have their own dedicated skill. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
How to apply this
- Treat the compiled tam-expertise-context.md as the source of truth for frameworks, templates, and benchmarks, and prefer it over improvising a new structure for a deliverable. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Match the Diataxis type to the deliverable's actual job — a runbook stays how-to, a reference doc stays reference — rather than letting one document drift between types. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Operationalize every RYG (Red/Yellow/Green) health score with a mandatory action tied to each state, not just a color with no next step. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Pair every benchmark cited in a deliverable with a prescriptive recommendation, rather than presenting a number and leaving the reader to infer what to do about it. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
Antipatterns
- Mixing Diataxis types within one document — a runbook that drifts into explanation, or a reference doc that tries to teach via tutorial — confusing the reader about what the document is for. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Scoring an account Green by default without data-backed justification, inflating apparent health and delaying the intervention a true Yellow or Red would have triggered. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Using this skill for MongoDB Premium Services operating procedures, active case management, or Atlas diagnostics, when those each have a dedicated skill (tam-reference, case-tracker, atlas-diagnostics-expert) built for exactly that. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Presenting a benchmark without a recommendation attached, leaving a customer-facing number with no clear next step for the reader. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
Limitations
- This reference is compiled from 120+ sources with the full context living in a separate context file, tam-expertise-context.md; the pack itself is a pointer to that depth, not a complete substitute for reading it. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- It explicitly does not cover MongoDB Premium Services operating procedures, active case management or the TS Tools API, Atlas cluster diagnostics, or code and frontend implementation — those require the sibling skills named in its own "When NOT to use" section. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Frameworks like BLUF, STAR, Pyramid, and SCQA are communication structures, not guarantees of a good deliverable — applying the right framework to weak underlying analysis still produces a well-structured but unconvincing document. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
Children
- Account Health Assessment (frontier)
- EBR and QBR Preparation (frontier)
- Churn Risk Analysis (frontier)
- Customer Success Frameworks (frontier)
- MEDDPICC and NRR Metrics (frontier)
Frontier under this node: Account Health Assessment, Churn Risk Analysis, Customer Success Frameworks, EBR and QBR Preparation, MEDDPICC and NRR Metrics