mindtickle-core-workflow-b

v2026.09.24

Design and govern a Mindtickle readiness and coaching measurement cycle tied to business outcomes. Use when defining competencies, assessments, coaching, or Readiness Index reviews. Trigger with "measure Mindtickle readiness".

GitHub
安装命令
npx skhub add jeremylongshore/mindtickle-core-workflow-b
Markdown
SKILL.md

Mindtickle Readiness and Coaching Measurement Cycle

Overview

Create an explainable measurement plan that connects competencies, learning, coaching, and field outcomes without turning a readiness score into an unsupported employment decision.

Prerequisites

  • A role owner, approved competency model, review population, and business outcome
  • Defined lawful uses, access controls, retention, and employee communication
  • Confirmed entitlements for Readiness Index, assessments, coaching, analytics, and any CRM integration

Tool Discipline

Use Read, Glob, and Grep for profiles and metric definitions, WebFetch for current official contracts, and Write or Edit for a governed measurement specification and redacted review receipt.

Current Contract

Mindtickle describes Ideal Rep Profiles, competency benchmarks, assessments, coaching, module-level insights, team and regional views, and correlation with CRM outcomes. The customer owns the validity, fairness, interpretation, and permitted decisions built on those measurements.

Authentication

Restrict learner-level data to approved roles. Use aggregate or de-identified data where possible, and keep CRM, HR, assessment, and coaching access independently authorized.

Instructions

  1. Define the decision the measurement may inform and decisions it must never make automatically.
  2. Version the role profile, competencies, weights, evidence sources, exclusions, and review cadence.
  3. Establish baselines and minimum sample sizes before setting targets or claiming correlation.
  4. Validate assessment accessibility, scoring reproducibility, manager calibration, and missing-data treatment.
  5. Map learning and coaching interventions to named gaps; retain a human review and appeal route.
  6. Present any tenant configuration change with population impact, effective date, and rollback.
  7. After approval, run the cycle and reconcile source evidence, computed views, and authorized exports.
  8. Report trends with uncertainty and confounders; do not infer causation from a dashboard correlation.

Approval Boundaries

Do not change competency weights, assign remediation, export learner-level results, or feed scores into compensation or employment actions without explicit policy and owner approval.

Output

Return the versioned model, purpose and prohibited uses, access matrix, validation evidence, approved interventions, aggregate results, limitations, and next review date.

Error Handling

ConditionResponse
A metric cannot be reproducedQuarantine it from decisions and reconcile its source and transformation.
Group size risks re-identificationSuppress or aggregate the result according to policy.
Outcome correlation is unstableReport uncertainty and collect more evidence; do not tune weights to force a result.

Example

profile=enterprise-ae-v3; population=approved; calibration=pass; learner-export=none; outcome-link=correlation-only; review=quarterly

Resources

Next Steps

Review the model with affected stakeholders and compare interventions against the frozen baseline.

发现
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版本
最新版本元数据

版本

v2026.09.24

发布时间

Sep 24, 2026

分类

未分类

许可证

MIT

源路径

skills/.curated/mindtickle-core-workflow-b

默认分支

main

最新提交

e5a6c3b

Tree SHA

c2dc8e8