business-pulse

<quick_start> Trigger: "business pulse", "how are we doing", "weekly numbers", "standup brief". One call gets you 90% of it:

weekly_brief({})

Then synthesize into the Output shape below (never dump raw JSON). For the new SDR cohort or deeper cuts, use the query_dataset calls in "How to run it". Reference material: reference/ in this skill

• epiphan-ai-mcp-guide-skill (golden defaults, owner IDs, verticals pack). </quick_start>

How to run it

1. One-shot brief (start here):

   weekly_brief({})
   

   Returns revenue (week/MTD/QTD/YTD + prior-year), deals won/lost, new contacts by lifecycle + source, BDR activity, and AE pipeline by stage — already tuned to FY2026 (start 2025-11-01), $19.5M target, BDR IDs 87486452 (Tim) / 423155215 (Ron).

   Track the new SDR cohort (Edgar / Vasil / Nyasha — onboarded June 2026) explicitly:

   weekly_brief({ bdr_owner_ids: ["93367782","93782443","94135434"] })
   query_dataset({ dataset: "rep_activity", group_by: ["owner"],
                   filters: { owner_ids: ["93367782","93782443","94135434"] } })
   

   (IDs + Nooks mapping live in epiphan-ai-mcp-guide-skill golden defaults.)

2. Deeper cuts as needed:

   query_dataset({ dataset: "pipeline_open", group_by: ["owner","stage"] })
   query_dataset({ dataset: "revenue", group_by: ["period_month"], date_from: "2025-11-01", date_to: "2026-11-01" })
   query_dataset({ dataset: "deals_closed", group_by: ["outcome"], date_from: <quarter start>, date_to: <quarter end> })
   

3. Synthesize into the output shape below — never just dump JSON.

Output shape

BUSINESS PULSE — week ending <date>

PACE        YTD $<x> of $19.5M target  →  <n>% of FY pace   [On / Behind / Ahead]
REVENUE     Week $<x> · MTD $<x> · QTD $<x> · YoY <±%>
PIPELINE    $<x> open across <n> deals
            by stage: Discovery $<x> · … · Commit $<x>
WON / LOST  Won <n> ($<x>) · Lost <n> ($<x>) this period
BDR         Tim: <dials/connects/meetings> · Ron: <…>
TOP MOVERS  <2-3 notable deals advancing or slipping>

SO WHAT (coaching)
 1. <where the gap is, and the single highest-leverage action>
 2. <a stage with stalled volume → who multi-threads it>
 3. <a vertical/segment signal worth a play this week>

Vertical-awareness — and its honest limit

There is no native vertical/industry dimension in the datasets (group_by = period/owner/stage/outcome/country/pipeline/lifecycle/source). So any HigherEd / Community College / Live Events / Corporate / Broadcast split is derived — proxy via source/pipeline/country, or enrich the top open deals account-by-account (sales_brief). Always label a vertical breakdown as approximate; never present a fabricated clean split. (Vertical pack: epiphan-ai-mcp-guide-skill/reference/verticals.md.)

Coaching lens (tie back to the data we have)

• Every 2026 loss was single-threaded below Manager level → if a big deal sits in Discovery, the play is "thread up to the economic buyer," not "follow up."
• 22 of 29 HigherEd deals stalled in Discovery → Discovery-stage bulk is the leading indicator to act on.

Guardrail — internal only

Output is internal. Naming partners/competitors here is fine; anything buyer-facing must stay clean (no AV-matrix mechanism, no third-party brand names — "your CMS / LMS"). The pooled figure is "a more affordable path, a starting point, not a quote." See epiphan-ai-mcp-guide-skill.

<success_criteria>

• Pulled LIVE data via weekly_brief / query_dataset this run — no stale or fabricated numbers
• Output follows the BUSINESS PULSE shape: pace vs $19.5M target, revenue (week/MTD/QTD/YoY), pipeline by stage, won/lost, BDR activity, top movers
• Exactly three "SO WHAT" coaching takeaways, each tied to a number in the brief
• Any vertical breakdown is labeled approximate (derived via source/pipeline/country proxy — no native vertical dimension)
• Output stays internal-only; buyer-facing guardrails respected </success_criteria>