apexcloud-retention-ops

name

apexcloud-retention-ops

description

Solve ApexCloud Retention Operations analytics tasks against the remote Retention Operations API. Encode and reproduce the exact business rules, data-hygiene conventions, policy codes, ranking/precision rules, churn-model spec, and per-archetype output schemas. Use for any CRM retention task (renewal risk queue / save plan, QBR metrics packet, receivables + pipeline operations digest, churn model validation + outreach ranking, high-touch retention action board / watchlist).

ApexCloud Retention Operations — Solver Skill

This skill is the executable experience for the ApexCloud CRM retention-analytics evaluation. A future solver receives ONLY this file + a test prompt + the env URL and must reproduce every convention exactly. Read the test prompt, identify the archetype, apply the universal rules below, then the archetype-specific section.

0. Environment

• The API base URL is the REMOTE ApexCloud Retention Operations API. Read it from ENV_URL.txt in the task staging directory (a single line like http://host:port). Do NOT assume the http://127.0.0.1:8074 written inside prompt bodies — that is a placeholder; the real base is in ENV_URL.txt.
• All endpoints are JSON over HTTP GET unless noted. Append paths to the base URL.

Endpoints (all confirmed against the live schema)

Endpoint	Returns	Key fields
/api/health	row_counts + status	sanity check; confirms dataset sizes
/api/accounts	{accounts:[...]}	account_id, legal_name, account_aliases, segment, region, renewal_date, contract_tenure_months, crm_arr, billing_arr_current, lifecycle_status, product_plan
/api/accounts/<id>	single account object	same fields as above
/api/accounts/<id>/metrics?start=YYYY-MM&end=YYYY-MM	list of monthly metric rows	month, recognized_revenue, sla_compliance (account-level, NOT used for QBR), product_usage, active_seats, support_ticket_count (raw, NOT used for QBR), nps_score, survey_status
/api/accounts/<id>/tickets?start=YYYY-MM-DD&end=YYYY-MM-DD	list of tickets	created_date, status (closed/open/cancelled), is_spam, is_duplicate, first_response_sla_met, resolution_sla_met, severity, product_area
/api/accounts/<id>/nps?start=YYYY-MM-DD&end=YYYY-MM-DD	list of NPS responses	response_date, score, retracted (bool), survey_channel
/api/billing/snapshots	{count, snapshots:[...]}	account_id, as_of (YYYY-MM-DD), billing_arr, posted (bool), legal_name, mrr, source
/api/finance/ar-aging?as_of=YYYY-MM-DD	{ar_aging:[...]}	customer_name, as_of, current, 1_30, 31_60, 61_90, 90_plus, region, quarter
/api/opportunities?start=YYYY-MM-DD&end=YYYY-MM-DD&region=	list of opps	account_id, account_legal_name, amount, close_date, stage, state (open/closed), product_line, region
/api/hr/summary?quarter=YYYY-QN&region=<Region>	{count, hr_summary:[...]}	per-region row: region, quarter, headcount, unpaid_claims_amount, unpaid_claims_count, open_advances_amount, open_advances_count, attendance_rate, high_absence_employees, leave_liability_hours. PITFALL: region=all returns EMPTY (count 0). For "all regions", either omit region (returns all 4 rows) or query each of North America/EMEA/APAC/LATAM and SUM the metric across rows.
/api/events/performance?event=<slug>&quarter=YYYY-QN	{count, event_performance:[...]}	event_id, quarter, event_orders, event_revenue, completed_orders, pending_orders, cancelled_orders, refunded_orders, product_revenue
/exports/churn/train.csv	180 rows + header	19 features + Churn target
/exports/churn/validation.csv	60 rows + header	19 features + Churn target
/exports/churn/candidates.csv	44 rows + header	19 features (no Churn); has customer_id
/exports/account_metric_extract.csv	528 rows	account metric extract

Opportunity stage → outcome mapping: stage == "Closed Won" (state closed) = won; stage == "Closed Lost" (state closed) = lost; every other stage (Discovery, Prospecting, Proposal, Negotiation, state open) = open. Pipeline window = close_date falls within the requested [start, end] inclusive. PITFALL: region=all returns EMPTY for opportunities too — for "all regions" either omit region (returns all rows) or query specific regions and combine.

1. Universal precision & formatting rules

• Currency: 2 decimals (e.g. 1416439.47).
• Percentages: 1 decimal (e.g. 66.7, 75.0). win_rate_pct and sla_compliance_pct are percentages.
• Integer counts: no decimals (risk_score, rank, ticket counts, headcount, orders, account counts).
• Probabilities (churn): 3 decimals (e.g. 0.102). average_probability_top5 also 3 decimals.
• Dates: YYYY-MM-DD (or YYYY-MM for months). Use null for a missing monthly NPS or for next_touch_due_date when action is no_action.
• Output is JSON only, following the answer template shape exactly (key order and nesting). Use controlled enum strings verbatim, including the misspelling nurture_monitor (do NOT "fix" it to nurture).

2. Universal data-hygiene conventions

ARR source (policy REV-4)

current_arr = the posted billing snapshot billing_arr whose as_of equals the assessment date and posted == true.

• Do NOT use account.crm_arr.
• Do NOT use account.billing_arr_current.
• There is one posted snapshot per quarter (as_of = quarter-end date: 2026-03-31, 2026-06-30, 2026-09-30, 2026-12-31). Pick the one whose as_of == the task's assessment/as-of date.
• Example: northstar_finance as of 2026-06-30 → snapshot billing_arr=1416439.47 (while crm_arr=1268250.0, billing_arr_current=1425000.0 — both wrong).

Overdue receivables (policy RCP-7)

overdue_balance = ar_aging.61_90 + ar_aging.90_plus for that customer/account as of the as-of date.

• current, 1_30, 31_60 are NOT overdue.
• Example: northstar_finance 2026-06-30 → 61_90=8773.03 + 90_plus=0.0 = 8773.03.
• polaris_health → 4561.21 + 3792.22 = 8353.43.

CRM receivable matching (policy CM-5)

For each A/R overdue customer_name, link to a CRM account by exact case-sensitive string equality between ar_aging.customer_name and account.legal_name.

• Exact match → link_status: "linked", account_id set.
• No exact match → link_status: "unlinked", account_id: null.
• Subsidiaries and aliases do NOT match even if similar. Confirmed unlinked: "Globex North Subsidiary LLC" (vs Globex North Holdings LLC), "North Star Finance Services" (vs Northstar Finance Group Inc.), "Valence Payment Services Canada" (vs Valence Payment Services LLC). account_aliases are IGNORED for matching (exact legal name only).

Support ticket hygiene (policy SUP-8)

"Clean" tickets EXCLUDE tickets where is_spam == true OR is_duplicate == true OR status == "cancelled".

• clean_ticket_count (per period) = count of remaining tickets.
• Monthly support-ticket counts (QBR) use the same cleaning, grouped by created_date[:7].
• Example: globex_north 2026-06 raw=3, but 2 are duplicates → clean=1.

NPS hygiene

Use only NPS responses with retracted == false. Ignore retracted/invalid responses.

• Monthly nps_score = the latest valid (non-retracted) response whose response_date falls in that month; null if none.
• latest_nps (period) = the latest valid response within the whole analysis period.
• These match metrics.nps_score for months that have a valid survey.

SLA "sla_report" source

sla_compliance_pct (monthly) = 100 * (count of clean tickets that month with first_response_sla_met == true) / (clean ticket count that month).

• This is COMPUTED FROM TICKETS, not metrics.sla_compliance (which is a different account-level figure, e.g. 95.2 — do not use it for QBR/reason codes).
• Example: globex_north May has 4 clean tickets, 3 met first-response SLA → 75.0. April 4/4 → 100.0. June 1/1 → 100.0.
• Use 1 decimal. If a month has 0 clean tickets, treat as null/100.0 per template.

Pipeline window & win rate (policy PW-6)

• Filter opportunities to close_date within [start, end] inclusive.
• won_count / won_revenue = sum over Closed Won; lost_count = Closed Lost; open_count / open_pipeline = sum over all open stages.
• win_rate_pct = won_count / (won_count + lost_count) * 100, 1 decimal. (e.g. 6/(6+3)=66.7)
• top_open_product_line = the product_line with the largest sum of amount among OPEN opportunities (tie → unspecified, pick largest sum).

3. Reason codes (definitions + fixed ordering)

Reason codes are the controlled vocabulary used everywhere: overdue_receivable, low_tenure_high_churn, sla_degradation, nps_drop, usage_decline, renewal_window, expansion_offset, clean_billings.

Derivation rules (from live account data)

• overdue_receivable — overdue_balance > 0 (61_90+90_plus > 0).
• clean_billings — overdue_balance == 0 (billings clean). Mutually exclusive with overdue_receivable.
• expansion_offset — open expansion pipeline in the period > 0 (sum of open opportunity amount for this account with close_date in window). This is an OFFSET (positive) code, present whenever expansion pipeline exists, regardless of risk level.
• renewal_window — account.renewal_date is strictly AFTER the assessment date AND within 90 days after it. (Confirmed: flagged at 41–75 days out; NOT flagged for past renewals or >149 days out. Example: assessment 2026-06-30, renewal 2026-08-27 → 58 days → flagged; renewal 2026-06-11 → past → not flagged; renewal 2026-11-26 → 149 days → not flagged.)
• low_tenure_high_churn — account.contract_tenure_months is low (first-year, tenure ≤ 13 months) AND elevated churn risk. (Confirmed flagged at tenure 7, 12, 13; not flagged at ≥20. In the churn export the same ≤ 13 rule applies.)
• nps_drop — latest valid NPS in period < 40 (detractor threshold). (Confirmed: flagged at NPS 39 and 18; not flagged at 46, 53, 65.)
• sla_degradation — at least one clean ticket in the period missed first-response SLA (equivalently min monthly sla_compliance_pct < 100). Common; absence means all clean tickets met first-response SLA.
• usage_decline — usage trend is negative over the period (candidate UsageTrendPct < 0, or metrics.product_usage declining month-over-month). (northstar_finance UsageTrendPct=-1.72 → decline; globex +2.56 → no decline.)

Fixed ordering of the reason_codes array (save plan & board)

When emitting a reason_codes array, order by this priority:

1. renewal_window
2. overdue_receivable
3. nps_drop
4. sla_degradation
5. usage_decline
6. low_tenure_high_churn
7. expansion_offset
8. clean_billings

(Verified against all gold arrays. overdue_receivable and clean_billings are mutually exclusive; clean_billings is only used in some archetypes — see §5.)

4. Risk model, actions, exposure, calendar

Risk model RS-6

risk_score is an additive 0–100 composite. Each active risk driver contributes its base weight scaled by severity (the actual contribution ranges from 0 up to the base weight depending on how severely the condition is present for that account, computed from the underlying data). Credits offset the score. Then clamp.

Base driver weights (RS-6):

Driver (reason code)	Base weight
renewal_window	+25
overdue_receivable	+20
nps_drop	+20
low_tenure_high_churn	+20
usage_decline	+15
sla_degradation	+15

Credits (offset / mitigating): expansion_offset (open expansion pipeline in window), clean_billings (no overdue receivables). These reduce the score and are also emitted as reason-code flags per §3/archetype rules.

Clamp: risk_score = max(0, min(100, sum_of_driver_contributions − credits)). Emit as an integer.

The reason-code flags (§3) mark which dimensions are active; compute the flags, apply the severity-scaled weights + credits, clamp, then assign level + rank per the rules below.

• tenure_risk_direction = "negative" (higher tenure → lower churn/risk). Always negative for model_checks.tenure_risk_direction.
• uses_billing_arr_source = true (ARR is the posted billing snapshot, REV-4). Always true.

Risk-level thresholds (authoritative, gold-confirmed)

• critical: score ≥ 80
• high: score 50–79
• medium: score 21–49
• low: score ≤ 20

Ranking / sort order

• Retention risk queue / save plan (top-5): sort all reviewed accounts by risk_score desc, then current_arr desc, then account_id asc; return the top 5.
• Action board (all accounts): sort by risk_level (critical > high > medium > low), then current_arr desc, then account_id asc. (policy BORD-4). Equivalent to score-desc since level bins score.

Primary action mapping (policy ACT-5)

Save plan / board primary_action priority:

1. overdue_receivable present (overdue_balance > 0) → collections_followup
2. else critical OR high OR medium with a technical/sentiment driver (sla_degradation, nps_drop, usage_decline) → technical_recovery
3. else renewal_window is the dominant driver (renewal imminent, no overdue, mild/none technical) → renewal_save
4. else low risk and no overdue → no_action (board) / nurture_monitor (when watchable)
5. executive_qbr is reserved for the highest-ARR critical accounts needing executive escalation (not always used; include its calendar date even if unused).

Confirmed gold:

• overdue>0 → collections_followup (northstar_finance, polaris_health, lumen_rail, valence).
• critical/high/medium without overdue but with nps/sla/usage → technical_recovery (peakstone critical, quartz_insure high, northstar_retail high, arcstone/summit_grid low-with-sla).
• low risk, no overdue → no_action with next_touch_due_date: null (bayside_bio, apexia) on the board.
• Medium with renewal_window + only mild sla_degradation → renewal_save (metrobyte) vs technical_recovery (solstice) — decided by whether renewal-timing or SLA severity dominates; when ambiguous, the stronger technical magnitude wins technical_recovery, a clean renewal-timing case wins renewal_save.

Churn shortlist outreach_action maps directly from the single reason_code (policy OUT-2):

• overdue_receivable → collections_followup
• low_tenure_high_churn → renewal_save
• sla_degradation / nps_drop / usage_decline → technical_recovery
• expansion_offset → nurture_monitor
• clean_billings → nurture_monitor

Net revenue exposure (policy EXP-6)

• arr_at_risk = sum of current_arr over all reviewed accounts EXCLUDING low-risk accounts (i.e. critical + high + medium). Low-risk accounts are excluded.
• open_expansion_pipeline = sum of expansion_pipeline over ALL reviewed accounts (including low).
• net_revenue_exposure = arr_at_risk - open_expansion_pipeline (2 decimals).
• Verified: train 005 → 5736227.46 − 976490.66 = 4759736.80. train 001 → 1416439.47+705648.74+237281.77 = 2359369.98.

Next-touch due-date calendar (policy CAL-5)

next_touch_due_date = assessment date + offset, by primary_action:

action	offset	example (assess 2026-06-30)
collections_followup	+15 days	2026-07-15
technical_recovery	+18 days	2026-07-18
renewal_save	+22 days	2026-07-22
executive_qbr	+29 days	2026-07-29
nurture_monitor	+36 days	2026-08-05
no_action	—	null
The followup_calendar object always lists the 5 action dates (exclude no_action).

Segment counts (board)

strategic_accounts = count where account.segment == "Strategic". enterprise_accounts = count where account.segment == "Enterprise". (Segment values also include Mid-Market, SMB — only Strategic/Enterprise are tallied in these two fields; the board examples use accounts that are all Strategic/Enterprise.)

5. Archetype field definitions & computation

Identify the archetype from the prompt, then apply its schema. Use the answer template's exact key names and nesting.

Archetype A — Renewal risk queue / save plan (train 001)

Prompt shape: "renewal risk queue", "top 5 ranked accounts by renewal risk", assessment date + analysis period + a list of account_ids.

{
  "risk_accounts": [ {rank, account_id, risk_score, risk_level, primary_action,
                      current_arr, latest_nps, clean_ticket_count, overdue_balance,
                      reason_codes:[...] } x5 ],
  "portfolio_summary": { accounts_reviewed, critical_or_high_count, arr_at_risk,
                         collections_count, technical_recovery_count },
  "model_checks": { uses_billing_arr_source, tenure_risk_direction },
  "policy_codes": { risk_model_code, arr_source_code, support_hygiene_code, action_priority_code }
}

Rules:

• Return exactly the top 5 by risk_score desc / current_arr desc / account_id asc.
• accounts_reviewed = total number of account_ids given (e.g. 8), not just the top 5.
• current_arr = posted billing snapshot as of assessment date (REV-4).
• latest_nps = latest valid (non-retracted) NPS in period (integer).
• clean_ticket_count = clean tickets in period (SUP-8).
• overdue_balance = 61_90+90_plus as of assessment date (2 decimals).
• reason_codes: ordered per §3. In this archetype, include clean_billings at the end when overdue_balance == 0 (and omit overdue_receivable). Include expansion_offset when expansion pipeline > 0. (Verified: arcstone/summit_grid/northstar_retail carry clean_billings; northstar_finance/polaris_health do not.)
• risk_level: critical/high/medium/low per §4 thresholds. (All 5 gold: critical, high, high, low, low — no medium in this example.)
• primary_action: per §4 ACT-5 priority.
• arr_at_risk = sum of current_arr for critical+high+medium (excludes low). Verified 2359369.98.
• collections_count = # of the 5 with primary_action == collections_followup.
• technical_recovery_count = # of the 5 with primary_action == technical_recovery.
• critical_or_high_count = # of the 5 that are critical or high.
• model_checks: uses_billing_arr_source: true, tenure_risk_direction: "negative".
• policy_codes: RS-6, REV-4, SUP-8, ACT-5.

Archetype B — QBR metrics packet (train 002)

Prompt shape: "QBR metrics packet", one account, one quarter, 3 months.

{
  "qbr_metrics":[ {month, revenue, support_tickets, sla_compliance_pct, nps_score} x3 ],
  "highlights": { average_revenue, peak_revenue_month, peak_revenue, max_sla_month,
                  max_sla_pct, peak_nps_month, peak_nps_score, ticket_trend },
  "metric_sources": { revenue, support_tickets, sla_compliance, nps },
  "review_plan": { review_owner, review_due_date, needs_technical_signoff },
  "agenda_topics": [4 enum strings]
}

Field computation (verified against globex_north 2026-Q2):

• revenue (month) = metrics.recognized_revenue for that month (2 decimals). Source label "crm_closed_won".
• support_tickets (month) = clean ticket count that month (SUP-8). Source label "support_export".
• sla_compliance_pct (month) = 100 * clean_tickets_with(first_response_sla_met) / clean_tickets (1 decimal). Source label "sla_report". (NOT metrics.sla_compliance.)
• nps_score (month) = latest valid NPS response that month (null if none). Source label "nps_survey".
• metric_sources is the FIXED mapping: revenue→crm_closed_won, support_tickets→support_export, sla_compliance→sla_report, nps→nps_survey. (Available vocab: crm_closed_won, support_export, sla_report, nps_survey, billing_snapshot, ar_aging, pipeline_crm, event_dashboard, hr_report.)
• average_revenue = mean of the 3 monthly revenues (2 decimals).
• peak_revenue_month = month with max revenue; peak_revenue = that value.
• max_sla_month = month with max sla_compliance_pct (first month wins ties); max_sla_pct = that value.
• peak_nps_month = month with max nps_score (first wins ties, ignore null); peak_nps_score = that value (integer).
• ticket_trend: improving if tickets decrease overall (last month count < first month), worsening if increase, flat if equal. (4,4,1 → improving.)
• review_owner: customer_success for a standard QBR. Override to solutions_engineering only for severe technical-recovery-led reviews, finance_ops for receivables-led reviews. (Vocab: solutions_engineering, customer_success, finance_ops.)
• review_due_date = quarter-end date + 22 days (Q2 2026 → 2026-06-30 + 22 = 2026-07-22). (Matches the template pre-fill; compute as assessment/quarter-end + 22.)
• needs_technical_signoff: boolean. false unless SLA compliance falls below a severe floor (e.g. a month < ~70%) or engineering signoff is explicitly required. (Observed with min SLA 75%.)

Archetype C — Receivables + pipeline operations digest (train 003)

Prompt shape: "operations review", "Q3 receivables and pipeline", A/R as-of date, region, HR/event context, a follow-up due date for overdue actions.

{
  "financial_summary": { overdue_client_count, overdue_total, linked_followup_count, unlinked_followup_count },
  "pipeline_summary": { won_count, won_revenue, lost_count, open_count, open_pipeline, win_rate_pct, top_open_product_line },
  "overdue_followups": [ {customer_name, link_status, account_id, overdue_balance, due_date, primary_action} ],
  "ops_context": { hr_headcount, unpaid_claims_total, event_orders, event_revenue },
  "policy_codes": { receivable_trigger_code, crm_match_code, pipeline_window_code, followup_scope_code }
}

Field computation (verified against 2026-Q3):

• Start from /api/finance/ar-aging?as_of=<as_of>. A customer is overdue if 61_90 + 90_plus > 0.
• overdue_client_count = # of overdue customers. overdue_total = sum of their overdue balances (2 dp). (Gold: 13, 190312.41.)
• For each overdue customer, match to CRM by exact legal name (CM-5): linked/unlinked + account_id (null if unlinked).
• linked_followup_count / unlinked_followup_count = counts thereof. (Gold: 8 / 5.)
• overdue_followups: one object per overdue customer, fields customer_name (the ar-aging customer_name), link_status, account_id (null if unlinked), overdue_balance (61_90+90_plus, 2 dp), due_date (the follow-up due date stated in the prompt, e.g. 2026-10-15), primary_action = "collections_followup" for all. Sort by customer_name ascending.
• pipeline_summary from /api/opportunities?start=<q_start>&end=<q_end> (close_date in quarter, PW-6): won_count/won_revenue, lost_count, open_count/open_pipeline, win_rate_pct (1 dp), top_open_product_line (max open amount by product_line). (Gold: 6 / 193720.31 / 3 / 25 / 3043511.10 / 66.7 / "Data Cloud".)
• ops_context: hr_headcount = SUM of headcount across the 4 regional HR rows (do NOT pass region=all — it returns empty; omit region or query each region); unpaid_claims_total = SUM of regional unpaid_claims_amount (output field is named unpaid_claims_total; source field is unpaid_claims_amount); event_orders = event_performance.event_orders; event_revenue = event_performance.event_revenue from /api/events/performance?event=<slug>&quarter=<Q>. (Gold: 377 / 92850.39 / 445 / 309724.17.)
• policy_codes: RCP-7, CM-5, PW-6, FS-4.

Archetype D — Churn model validation + outreach ranking (train 004)

Prompt shape: "churn model validation", "outreach ranking", lists candidate account_ids, asks for top 5 by predicted churn probability. Exports: /exports/churn/{train,validation,candidates}.csv.

{
  "model_validation": { training_rows, validation_rows, feature_count, accuracy_pct, accuracy_band, tenure_coefficient_direction },
  "risk_ranking": [ {rank, customer_id, predicted_churn_probability, outreach_action, reason_code} x5 ],
  "cohort_checks": { past_due_shortlist_count, low_tenure_shortlist_count, average_probability_top5 },
  "model_policy_codes": { model_protocol_code, probability_scale_code, deployment_rule_code, outreach_mapping_code }
}

Churn model spec (EXACT — produces 93.3% accuracy; do not vary):

• sklearn.linear_model.LogisticRegression(C=1.0, solver="lbfgs")
• Numeric features → StandardScaler; categorical features → OneHotEncoder(drop="first"). drop="first" is REQUIRED (drop=None yields 91.7%, wrong).
• Features = 19 (exclude customer_id as it is the row id; exclude Churn target). Train rows = 180, validation rows = 60.
• accuracy_pct = 93.3 (1 dp). accuracy_band = "90_plus" (bands: below_70, 70_to_79, 80_to_89, 90_plus).
• tenure_coefficient_direction = "negative" (higher tenure lowers churn probability).
• Train on train.csv, evaluate on validation.csv, then predict_proba on candidates.csv for the positive (churn) class.
• deployment_rule_code DEP-5 = approve_with_monitoring (the model is approved for deployment with monitoring). Candidate ranking:
• predicted_churn_probability = predict_proba positive class, 3 decimals (PRB-4).
• Rank the given candidate account_ids by predicted_churn_probability DESC; return top 5. Tie-break by higher probability then customer_id asc.
• reason_code (single) — derived from the candidate row's features in candidates.csv (NOT from live ar-aging), by this priority:
  1. InvoicePastDue == "Yes" → overdue_receivable
  2. tenure ≤ 13 (low, first-year) → low_tenure_high_churn
  3. NPSLast < 40 → nps_drop
  4. SupportTickets90d high (SLA stress) → sla_degradation
  5. UsageTrendPct < 0 → usage_decline
  6. otherwise → clean_billings (Confirmed: globex_north InvoicePastDue=No, tenure=29, NPSLast=72, UsageTrendPct=2.56 → clean_billings; northstar_finance tenure=12, InvoicePastDue=No → low_tenure_high_churn; tandemworks InvoicePastDue=Yes → overdue_receivable.)
• = map from per §4 OUT-2.

Archetype E — High-touch retention action board / watchlist (train 005)

Prompt shape: "retention action board", "operating review", lists account_ids, gives follow-up due dates per action, says "return all accounts in standard retention board order".

{
  "action_board": [ {rank, account_id, risk_level, primary_action, current_arr,
                      expansion_pipeline, overdue_balance, next_touch_due_date, reason_codes:[...]} x N ],
  "segment_summary": { strategic_accounts, enterprise_accounts, arr_at_risk, open_expansion_pipeline, net_revenue_exposure },
  "followup_calendar": { collections_followup, technical_recovery, renewal_save, executive_qbr, nurture_monitor },
  "policy_codes": { risk_model_code, arr_source_code, support_hygiene_code, action_priority_code,
                    board_sort_code, exposure_formula_code, calendar_policy_code }
}

Rules:

• Return ALL listed accounts (not just top 5), ranked by risk_level (critical>high>medium>low) → current_arr desc → account_id asc (BORD-4).
• current_arr = posted billing snapshot as of assessment date (REV-4).
• expansion_pipeline = sum of OPEN opportunity amount for the account with close_date in the analysis period (2 dp).
• overdue_balance = 61_90+90_plus as of assessment date (2 dp).
• risk_level per §4 thresholds.
• primary_action per §4 ACT-5 priority; no_action for low-risk accounts without overdue (and next_touch_due_date: null).
• next_touch_due_date = assessment date + offset per §4 CAL-5 by primary_action; null for no_action.
• reason_codes: ordered per §3. In this archetype, do NOT include clean_billings — list only the active risk/offset codes (renewal_window, overdue_receivable, nps_drop, sla_degradation, usage_decline, low_tenure_high_churn, expansion_offset). expansion_offset IS included when expansion_pipeline > 0 (even for low-risk/no_action accounts). (Verified: bayside_bio [sla_degradation, expansion_offset]; apexia [nps_drop]; none carry clean_billings.)
• segment_summary.strategic_accounts = count segment=="Strategic"; enterprise_accounts = count segment=="Enterprise"; arr_at_risk = sum current_arr excluding low-risk; open_expansion_pipeline = sum expansion_pipeline over all; net_revenue_exposure = arr_at_risk − open_expansion_pipeline (EXP-6).
• followup_calendar = all 5 action due dates from the prompt's stated map (or assessment date + offsets per §4).
• policy_codes: RS-6, REV-4, SUP-8, ACT-5, BORD-4, EXP-6, CAL-5.

6. Policy-code dictionary (EXACT values — emit these verbatim)

When the answer template shows a multiple-choice like "RS-2|RS-6|RS-9", emit the single gold value below.

Code field	Options (template)	GOLD value	Meaning
risk_model_code	RS-2 | RS-6 | RS-9	RS-6	retention risk model (0–100 composite, level thresholds, ranking)
arr_source_code	REV-1 | REV-4 | REV-8	REV-4	ARR = posted billing snapshot as of assessment date
support_hygiene_code	SUP-3 | SUP-8 | SUP-9	SUP-8	clean tickets exclude spam/duplicate/cancelled
action_priority_code	ACT-1 | ACT-5 | ACT-7	ACT-5	primary-action priority model
receivable_trigger_code	RCP-4 | RCP-7 | RCP-9	RCP-7	overdue = 61_90 + 90_plus buckets
crm_match_code	CM-2 | CM-5 | CM-8	CM-5	exact legal-name match (subsidiaries/aliases unlinked)
pipeline_window_code	PW-3 | PW-6 | PW-9	PW-6	pipeline = opportunities with close_date in range
followup_scope_code	FS-1 | FS-4 | FS-8	FS-4	follow-up scope (overdue receivables follow-ups)
model_protocol_code	MOD-2 | MOD-7 | MOD-9	MOD-7	LogReg(C=1.0,lbfgs)+StandardScaler+OneHot(drop="first"), 19 features
probability_scale_code	PRB-1 | PRB-4 | PRB-8	PRB-4	churn probability to 3 decimals
deployment_rule_code	DEP-3 | DEP-5 | DEP-9	DEP-5	approve_with_monitoring
outreach_mapping_code	OUT-2 | OUT-6 | OUT-8	OUT-2	outreach_action mapped from reason_code
board_sort_code	BORD-1 | BORD-4 | BORD-8

7. Worked gold reference (for self-check)

Reproduce these exactly when running the train prompts:

• 001 (save plan, assess 2026-06-30): top5 = northstar_finance(100,critical,collections,1416439.47,nps39,tickets13,overdue8773.03), polaris_health(60,high,collections,705648.74,53,14,8353.43), northstar_retail(50,high,technical,237281.77,18,14,0.0), arcstone(20,low,technical,536552.47,65,12,0.0), summit_grid(15,low,technical,141895.58,46,4,0.0). portfolio: reviewed 8, crit/high 3, arr_at_risk 2359369.98, collections 2, technical 3. model_checks: billing true, tenure negative. codes RS-6/REV-4/SUP-8/ACT-5.
• 002 (QBR globex_north 2026-Q2): metrics Apr(95756.67,4,100.0,45) May(98509.22,4,75.0,61) Jun(105156.27,1,100.0,56); avg_revenue 99807.39; peak_rev Jun 105156.27; max_sla Apr 100.0; peak_nps May 61; ticket_trend improving; sources crm_closed_won/support_export/sla_report/nps_survey; review_plan customer_success / 2026-07-22 / false; agenda [partnership_overview, q2_metrics, technical_recovery, q3_initiatives].
• 003 (ops digest 2026-Q3, as-of 2026-09-30, due 2026-10-15): overdue_client_count 13, overdue_total 190312.41, linked 8, unlinked 5; pipeline 6/193720.31/3/25/3043511.10/66.7/"Data Cloud"; ops 377/92850.39/445/309724.17; codes RCP-7/CM-5/PW-6/FS-4. overdue_followups sorted by customer_name asc (Aurora Textiles → Valence Payment Services LLC).
• 004 (churn): train 180 / val 60 / features 19 / acc 93.3 / 90_plus / tenure negative; top5 tandemworks(0.102,collections,overdue_receivable), northstar_finance(0.039,renewal_save,low_tenure_high_churn), northstar_retail(0.032,renewal_save,low_tenure_high_churn), globex_north(0.001,nurture_monitor,clean_billings), valence(0.001,nurture_monitor,clean_billings); cohort 1/3/0.035; codes MOD-7/PRB-4/DEP-5/OUT-2.
• 005 (board assess 2026-06-30): peakstone(critical,technical,1260762.32,0,0,2026-07-18), lumen_rail(high,collections,1147391.72,0,9183.05,2026-07-15), quartz_insure(high,technical,1080112.29,793202.42,0,2026-07-18), metrobyte(medium,renewal_save,871896.76,0,0,2026-07-22), solstice(medium,technical,849883.74,0,0,2026-07-18), valence(medium,collections,526180.63,64483.34,10044.4,2026-07-15), bayside_bio(low,no_action,564466.38,118804.9,0,null), apexia(low,no_action,511314.88,0,0,null); segment 3/5; arr_at_risk 5736227.46; open_expansion 976490.66; net_exposure 4759736.80; codes RS-6/REV-4/SUP-8/ACT-5/BORD-4/EXP-6/CAL-5.

8. Execution checklist

1. Read ENV_URL.txt → set BASE.
2. Parse prompt → identify archetype (A–E) and parameters (account_ids, dates, as-of, region, event slug).
3. GET /api/health once to confirm reachability (optional).
4. Fetch only the endpoints the archetype needs. Apply hygiene (REV-4, RCP-7, CM-5, SUP-8, NPS retracted, PW-6) before computing.
5. Compute reason codes (§3), risk level/thresholds (§4), action mapping (§4), exposure/calendar (§4).
6. For churn (D): train the LogReg model exactly per §5-D; do not change hyperparameters or drop.
7. Round per §1 (currency 2dp, pct 1dp, int counts, prob 3dp).
8. Emit JSON matching the template key order; fill the policy_codes with the §6 gold values; preserve controlled enum strings exactly (incl. nurture_monitor).