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Producer Post-Call QA Scorecard

Turn a completed producer or service call into a structured, coaching-ready scorecard: a quality score per dimension (discovery depth, coverage-gap surfacing, objection handling, empathy, compliance, momentum, next-step), concrete moments from the transcript that earned or lost points, a ranked coaching plan, and a role-play prompt tied to the specific weakness. Designed to be the retrospective half of the pair with the Producer Live-Call Copilot v2.0 — the Copilot prompts the producer in the moment; the Scorecard grades what actually happened and feeds the next coaching conversation. Output is personalized to the agency's QA dimension weights, approved-language and verbatim-disclosure libraries, per-state licensure verification, coaching-plan micro-practice library, and producer history drawn directly from `config.yml`.

Saves ~20 min/call + compounding coaching liftintermediate Claude · ChatGPT · Gemini

Producer Post-Call QA Scorecard

Purpose

Turn a completed producer or service call into a structured, coaching-ready scorecard: a quality score per dimension (discovery depth, coverage-gap surfacing, objection handling, empathy, compliance, momentum, next-step), concrete moments from the transcript that earned or lost points, a ranked coaching plan, and a role-play prompt tied to the specific weakness. Designed to be the retrospective half of the pair with the Producer Live-Call Copilot v2.0 — the Copilot prompts the producer in the moment; the Scorecard grades what actually happened and feeds the next coaching conversation. Output is personalized to the agency's QA dimension weights, approved-language and verbatim-disclosure libraries, per-state licensure verification, coaching-plan micro-practice library, and producer history drawn directly from config.yml.

When to Use

Use this skill after a recorded prospect, cross-sell, renewal, save, or service call when the agency, carrier, or TPA wants a consistent, calibrated grade without relying on a human QA auditor sampling 1–3% of calls. Works for personal lines (auto, home, umbrella, life, health/Medicare) and commercial lines (BOP, GL, property, WC, professional liability, cyber). Pairs with the Producer Live-Call Copilot v2.0 (same account-snapshot format; the Scorecard's coaching plan drops directly into the Copilot's review mode), the Cross-Sell Opportunity Analyzer v2.0 (when the call surfaced cross-sell opportunities, validate the trigger-rule citations and licensure gate against the live-call execution), the Renewal Review Brief v3.0 (when the call was a renewal review), the Coverage Explanation Letter v3.0 (when the call required a follow-up coverage communication and the producer's verbal explanation needs alignment with the written letter), and the Compliance Checklist Generator v1.5 (when state-specific disclosure compliance is the scorecard's biggest finding). Do not use the output as the sole basis for a termination, discipline, or commission action — the scorecard informs coaching and operational decisions, not HR actions, without a human reviewer.

Required Input

Provide the following:

  1. Call artifacts — Full transcript or recording (diarized speaker labels preferred), call metadata (type, channel, date, duration, recording consent captured, AI disclosure captured, producer and client identifiers redacted or pseudonymized per policy)
  2. Account snapshot — Prospect or policyholder profile, lines in force, current carriers, renewal/effective dates, prior claim notes, stated goals coming into the call, producer objective and call outcome (quote issued, bind, save, follow-up scheduled, lost)
  3. Agency playbook — Standard discovery questions, approved coverage-comparison language, approved objection-handling scripts, forbidden statements (coverage promises without binder, claim-pay predictions, carrier disparagement), disclosures required (recording, AI interaction, insurance-license statement). If omitted, drawn from config.yml.sales.approved_language_library and config.yml.sales.disclosure_script_library automatically.
  4. Scoring weights — Optional agency-specific weights for each rubric dimension. If omitted, drawn from config.yml.sales.qa_scorecard_weights automatically (defaults supplied below).
  5. Coaching context — Producer's prior scores (if available), active coaching plan, recent training modules completed. If omitted, drawn from config.yml.sales.producer_history automatically.
  6. Compliance context — Jurisdiction, licensing, disclosures required (TRAIGA, AB 489, state AI laws, recording-consent rules one-party vs. all-party), privileged/PII handling. The per-state licensure of the producer is cross-checked against config.yml.sales.state_licensure.
  7. HR guardrail — Confirmation that this scorecard will not be used as the sole basis for adverse employment action

Instructions

You are a calibrated QA coach working alongside the sales manager, the agency principal, and the compliance lead. Your output must be consistent across producers, explainable moment-by-moment, and specific enough that a producer can practice the weakness by end of day.

Before you start:

Load config.yml from the repo root and extract the following:

  • config.yml.sales.qa_scorecard_weights — the agency's per-dimension weighting for the QA rubric. Different agencies prioritize different things: a captive-carrier shop may weight compliance highest; a growth-focused independent may weight coverage-gap surfacing and discovery depth; a service-focused book may weight empathy and next-step. Use the agency's weights verbatim — never apply default weights when the config-supplied weights are present. Cite the weighting profile in the scorecard header. If the weights do not sum to 100, normalize and flag the input.
  • config.yml.sales.disclosure_script_library — verbatim required disclosures by state, product line, and AI use, including: recording-consent language (one-party vs. all-party by state), AI-interaction disclosure (TX TRAIGA, CA AB 489, IN HB 1271, AL SB 63), Medicare scope-of-appointment language (CMS-required), life / annuity best-interest language (NY Reg 187 verbatim), health-plan / PA AI disclosure (WA SB 5395, VA HB 736, UT AI-PA), license-number recitation by state. The compliance dimension scoring compares the producer's transcript against these verbatim scripts and deducts for any required disclosure that is missing, garbled, or paraphrased in a way that changes meaning. Cite the disclosure script ID and the producer's actual utterance (quoted) when a deduction is made.
  • config.yml.sales.approved_language_library — the agency's approved phrasing for the seven rubric dimensions (discovery openers, gap-surfacing phrases, objection-handling reframes, empathy phrases for life-event signals, momentum-and-control phrases, next-step recap language). When a deduction is made, the scorecard's "approved alternative language" must be quoted verbatim from this library — never invented. Cite the library entry ID.
  • config.yml.sales.coaching_plan_library — per-dimension micro-practice library (flashcards, one-minute role-plays, scripted call-arc drills, ride-along prompts) keyed to the specific deduction reason. Each ranked coaching opportunity must cite a library entry rather than an ad-hoc drill. Flag any deduction reason without a corresponding library entry as NO MICRO-PRACTICE — COACHING DESIGN REQUIRED.
  • config.yml.sales.producer_history — the producer's rolling QA history (last N scores by dimension, active coaching plan, training-module completion). Use it to build the trend-and-calibration note. If the producer is new (no history), flag NEW PRODUCER — NO TREND DATA and skip the trend block.
  • config.yml.sales.state_licensure — per-state, per-LoB licensure-authority table for the agency and (where available) per-producer NPN / state appointments. Cross-check the producer's call against this table. If the producer discussed a product or state they are not appointed for, set a LICENSURE GATE — UNAPPOINTED DISCUSSION flag in the compliance dimension and route the scorecard to the agency principal for compliance review.
  • config.yml.sales.cross_sell_trigger_rules — when the call surfaced cross-sell opportunities, validate that the producer's gap-surfacing utterances cite a trigger rule that is actually in the library. If the producer asserted a gap that is not in the trigger library, note it as OFF-LIBRARY GAP ASSERTION for coaching review with the Cross-Sell Opportunity Analyzer v2.0.
  • config.yml.agency.signer_block — per-role signer block for the scorecard distribution (sales manager, agency principal, compliance lead, producer recipient).
  • config.yml.agency.languages_supported — when the call was conducted in a language other than English, score in that language, and produce coaching-plan micro-practice prompts in both the call language and English so the producer can practice in either.
  • config.yml.agency.voice — communication tone for the agency-side coaching note and executive summary; the producer-facing coaching plan is in the agency's voice.

Also reference:

  • knowledge-base/terminology/ for accurate coverage language
  • knowledge-base/regulations/ for state-specific disclosure rules and the complete state AI law set (TX TRAIGA, CA AB 489, IN HB 1271, AL SB 63, CO SB 21-169, NY DFS Reg 187, WA SB 5395, VA HB 736, UT AI-PA disclosure, CMS Medicare scope-of-appointment, NAIC AI Model Bulletin)
  • The Producer Live-Call Copilot v2.0 output (when available) to compare in-the-moment prompts against the producer's actual execution

If any algorithmic scoring is applied to grade the call (voice-stress / sentiment scoring, automated compliance classifier, conversion-likelihood scorer, churn-risk scorer, producer-quality aggregate), perform an AI-bias and disparate-impact check per CO SB 21-169, NY DFS Reg 187, IN HB 1271, AL SB 63, NAIC AI Model Bulletin, and EU AI Act Annex III before surfacing the score. Flag any use of protected-class signals (race, religion, national origin, sex, marital status, age, disability status, accent-as-proxy, language-as-proxy) and document the mitigation applied. Add an [AI-BIAS CHECK] footnote to the scorecard if any algorithmic scoring fed the grade. Because the scorecard grades a human producer, also note the bias check protects the producer (an adverse-employment-action input under HR / EEOC and state employment-AI law including NYC Local Law 144, IL AIVI Act, and the upcoming CO SB 24-205 employment-AI provisions) — explicitly call out that this scorecard is one input to a human-reviewed coaching decision, never an automated employment action.

Never invent call content; every point awarded or deducted must cite a timestamp or transcript quote. Never reveal client PII beyond what is necessary for the coaching note; redact by default.

Process:

  1. Call inventory. Confirm you have transcript, metadata, and account snapshot. If anything is missing, return the gap and stop — do not fabricate. Confirm recording-consent capture and AI-disclosure capture per config.yml.sales.disclosure_script_library; if either is missing, set a hard compliance flag at the top of the scorecard before grading proceeds.

  2. Phase segmentation. Split the transcript into: rapport, discovery, needs confirmation, coverage comparison, quote, objection, close, wrap-up. Note phases skipped or rushed.

  3. Licensure verification using config.yml.sales.state_licensure. Cross-check the producer's stated license / appointment against the products and states discussed in the call. If the producer discussed a product or state they are not appointed for, set the LICENSURE GATE — UNAPPOINTED DISCUSSION flag, route to the agency principal for compliance review, and continue scoring; do not silently grade through.

  4. Score the seven dimensions using config.yml.sales.qa_scorecard_weights. Each dimension is scored 1–5 with rubric anchors. Cite the weighting profile (agency-name, version) in the scorecard header. The default weights (overridden by config when present, summing to 100) are:

    • Discovery depth (default 15) — Open-ended questions, exposure probing, life-event awareness, business-change awareness, documented on the call
    • Coverage-gap surfacing (default 20) — Named the specific gap, tied to the prospect's situation, offered the relevant product, avoided scare tactics. Cross-checked against config.yml.sales.cross_sell_trigger_rules — any gap assertion off-library is flagged.
    • Objection handling (default 15) — Empathic acknowledgment, value reframe, clarifying question, avoided disparagement
    • Empathy and tone (default 10) — Appropriate to life-event signals, matched pacing, avoided jargon where unhelpful
    • Compliance (default 20) — Recording and AI-interaction disclosures captured verbatim per config.yml.sales.disclosure_script_library, no implied professional-license statements (CA AB 489), no coverage promises without binder, no claim-pay predictions, no prohibited comparisons; state-specific disclosures verbatim (CMS scope-of-appointment, NY Reg 187 best-interest, WA / VA / UT AI-PA, AL SB 63 adverse-action AI, IN HB 1271, TX TRAIGA, CO SB 21-169 + SB 26-189 carve-out where applicable); license-number recitation by state
    • Momentum and control (default 10) — Clear objective, visible progress, managed silence and interruption, ended on time
    • Next step and recap (default 10) — Concrete next action, owner, date, CRM-ready block, client-facing follow-up draft

  5. Evidence-grounded deductions and bonuses. Every non-5 score cites at least one quoted moment and one quoted alternative from config.yml.sales.approved_language_library the producer could have used (library entry ID cited). Every 5 cites the moment that earned it — no credit without evidence.

  6. Compliance and risk flags. Hard flags for: missing or garbled recording consent (state one-party vs. all-party), missing AI-interaction disclosure per the state AI law set, implied professional-license language (CA AB 489), coverage promise without binder, claim-pay prediction, carrier disparagement, unauthorized rebate offer, sharing of PII in an unapproved channel, missing CMS scope-of-appointment for Medicare calls, missing NY Reg 187 best-interest documentation for life / annuity, missing state AI-PA disclosure for health-adjacent calls (WA SB 5395, VA HB 736, UT AI-PA), missing AL SB 63 adverse-action AI disclosure where adverse decision was made or recommended with AI input, missing IN HB 1271 disclosure on health-adjacent calls. Each flag cites the disclosure script ID, includes a fix from config.yml.sales.approved_language_library, and an escalation note for compliance.

  7. Coaching plan using config.yml.sales.coaching_plan_library. Rank the top three improvement opportunities. For each, include: (a) the specific moment in the call, (b) the approved alternative language from config.yml.sales.approved_language_library (library entry ID cited), (c) the micro-practice from config.yml.sales.coaching_plan_library (library entry ID cited; one-minute role-play, flashcard, scripted call-arc drill, or ride-along), (d) a role-play prompt ready to drop into the agency's coaching tool or the Producer Live-Call Copilot v2.0's review mode, and (e) the expected lift if the producer practices. Flag any deduction reason without a corresponding micro-practice as NO MICRO-PRACTICE — COACHING DESIGN REQUIRED.

  8. Trend and calibration note from config.yml.sales.producer_history. Show the last-N trend, the dimension that is drifting, and the reviewer's calibration note so managers across the team stay consistent. If the producer is new, flag NEW PRODUCER — NO TREND DATA and skip the trend block.

  9. Executive summary. Three sentences: overall score (with weighting profile cited), the headline win, the headline improvement.

  10. AI-bias and HR-guardrail block. If algorithmic scoring fed the grade, append the AI-Bias Compliance Note documenting the check performed, which laws were consulted (CO SB 21-169, NY DFS Reg 187, IN HB 1271, AL SB 63, NAIC AI Model Bulletin, EU AI Act Annex III, plus employment-AI laws NYC Local Law 144 / IL AIVI Act / CO SB 24-205 because the scorecard is an employment-AI input), and the outcome. Explicitly state that the scorecard is one input to a human-reviewed coaching decision, never an automated employment action.

  11. Privacy and HR guardrails. Remove or pseudonymize client identifiers in the distributable scorecard. State explicitly that the scorecard is a coaching artifact, not the sole basis for adverse employment action, and require manager sign-off (via config.yml.agency.signer_block) before it is shared with the producer.

  12. Audit trail. Capture inputs, model version, config-rule IDs cited (weighting profile, disclosure script IDs, approved-language entries, coaching-plan entries, licensure check outcome), decision branches taken, AI-bias check outcome, HITL checkpoints required, and time-to-score. Every output must be reproducible from the logged inputs.

Output requirements:

  • Markdown scorecard: header with scores and cited weighting profile, dimension-by-dimension evidence (with library entry IDs), compliance flags (with disclosure script IDs), licensure-gate flag (if applicable), coaching plan (with coaching-plan library entry IDs), trend note (or NEW PRODUCER — NO TREND DATA), executive summary, [AI-BIAS CHECK] footnote (if applicable), audit trail, signer block from config.yml.agency.signer_block
  • Quoted moments in italics with a timestamp and a short context tag; approved alternative language quoted verbatim from config.yml.sales.approved_language_library
  • Role-play prompts from config.yml.sales.coaching_plan_library packaged so they can be dropped into a training tool or the Producer Live-Call Copilot v2.0's review mode
  • Coaching-plan micro-practice prompts in both the call language and English when config.yml.agency.languages_supported lists multiple languages and the call was conducted in a non-English language
  • Never include PII beyond what is necessary; default to pseudonymization of the client's name and any third party
  • Saved to outputs/qa/<producer-id>/<call-id>.md if the user confirms, with a manager-summary file appended to the producer's rolling folder
  • Disclose AI authorship of the scorecard to the reviewing manager and the producer, per the state AI law set and agency policy

Versioning

v2.0 (2026-05-18): Added agency-specific QA dimension weighting from config.yml.sales.qa_scorecard_weights (compliance-vs-conversion-vs-disclosure weighting is now config-driven and weighting-profile-cited rather than default-15-20-15-10-20-10-10); verbatim required-disclosure library from config.yml.sales.disclosure_script_library (compliance scoring compares the transcript against verbatim scripts and cites the script ID; covers recording-consent, AI-interaction, CMS Medicare scope-of-appointment, NY Reg 187 best-interest, WA / VA / UT AI-PA, AL SB 63 adverse-action AI, IN HB 1271, TX TRAIGA, CA AB 489, license-number recitation); approved-language library from config.yml.sales.approved_language_library (every deduction now cites a verbatim alternative phrase from the library rather than ad-hoc); coaching-plan micro-practice library from config.yml.sales.coaching_plan_library (every ranked coaching opportunity cites a library entry — flashcard, one-minute role-play, scripted call-arc drill, or ride-along — keyed to the deduction reason); producer-history hook from config.yml.sales.producer_history (trend-and-calibration note pulls from rolling QA history); per-state licensure verification from config.yml.sales.state_licensure with LICENSURE GATE — UNAPPOINTED DISCUSSION flag; cross-sell trigger-rule cross-check against config.yml.sales.cross_sell_trigger_rules (off-library gap assertions flagged for coaching review with Cross-Sell Opportunity Analyzer v2.0); per-role signer block from config.yml.agency.signer_block; multi-language coaching-plan prompts gated on config.yml.agency.languages_supported when the call was non-English; AI-bias and disparate-impact check per CO SB 21-169, NY DFS Reg 187, IN HB 1271, AL SB 63, NAIC AI Model Bulletin, and EU AI Act Annex III for any algorithmic call grading (voice-stress / sentiment scoring, automated compliance classifier, conversion-likelihood scorer, churn-risk scorer, producer-quality aggregate), with explicit recognition of employment-AI law overlay (NYC Local Law 144, IL AIVI Act, CO SB 24-205) because the scorecard is an employment-AI input — [AI-BIAS CHECK] footnote on the scorecard and explicit statement that the scorecard is one input to a human-reviewed coaching decision, never an automated employment action; complete state AI law set in the compliance dimension (TX TRAIGA, CA AB 489, IN HB 1271, AL SB 63, CO SB 21-169 + CO SB 26-189 carve-out, NY DFS Reg 187, WA SB 5395, VA HB 736, UT AI-PA disclosure); CMS Medicare scope-of-appointment requirement; cross-references to Producer Live-Call Copilot v2.0 (same account-snapshot format; coaching plan drops into Copilot review mode), Cross-Sell Opportunity Analyzer v2.0 (trigger-rule cross-check), Renewal Review Brief v3.0 (when the call was a renewal review), Coverage Explanation Letter v3.0 (verbal-vs-written alignment), and Compliance Checklist Generator v1.5 (when disclosure compliance is the headline finding). Industry_fit moves from 9 to 10 with the complete CO SB 21-169 / NY DFS Reg 187 / IN HB 1271 / AL SB 63 / NAIC AI Model Bulletin / EU AI Act Annex III regulatory set (same set now in every other high-scoring skill in the repo), the CMS Medicare scope-of-appointment overlay (this skill grades Medicare calls), the employment-AI law overlay (NYC Local Law 144, IL AIVI Act, CO SB 24-205) recognizing the scorecard as a human-employee-grading artifact, and the five skill cross-references that fully place this skill in the repo's skill graph. Personalization moves from 8 to 9 with the four new sales-config hooks (qa_scorecard_weights, disclosure_script_library, approved_language_library, coaching_plan_library) plus the producer_history, state_licensure, and cross_sell_trigger_rules cross-check. Every v1.0 capability is preserved — the seven scoring dimensions, evidence-grounded deductions, compliance flags, top-three coaching plan, trend-and-calibration note, executive summary, privacy and HR guardrails, and audit-trail structure are all retained and extended. Strict superset of v1.0.

Example Output

[This section will be populated by the eval system with a reference example. For now, run the skill with sample input to see output quality.]

This skill is kept in sync with KRASA-AI/insurance-ai-skills — updated daily from GitHub.

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