Talent Review Calibrator

SkillDev tools

Facilitate talent review and people decisions with a structured view of performance, potential, readiness, risks, and development actions using talent calibration and 9-box logic. Use when preparing talent review, succession planning, promotion readiness, or performance calibration sessions. Do not use for payroll administration, HRIS updates, or generic praise notes.

Use Talent Review Calibrator in Claude, ChatGPT or Ahel Desktop

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Also: Claude Code · Cursor · Codex

Then ask your AI: use the Talent Review Calibrator skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Talent Review CalibratorStart free

What this skill tells your AI

The instructions your AI receives, as published by hoavdc/codexkit in skills/codexkit-talent-review-calibrator/SKILL.md and read by Ahel’s review.

Purpose

Support fairer and more actionable talent discussions by structuring evidence, risk, and development actions.

When to use

  • Leaders are preparing a talent review or succession discussion.
  • Managers need calibration before promotion or performance decisions.
  • HR wants consistent talent cards and action plans.

When not to use

  • The task is a transactional HR operation.
  • There is no evidence beyond vague opinion or hearsay.

Inputs

  • employee or talent pool list
  • recent performance evidence and outcomes
  • potential indicators, readiness signals, and role criticality
  • retention risk, succession context, or capability gaps

Procedure

  1. Define the evaluation lens and remove criteria drift before rating people.
  2. Separate evidence, interpretation, and bias risks.
  3. Place talent using performance, potential, and readiness signals.
  4. Identify critical-role coverage, succession gaps, and flight risk.
  5. Recommend development, retention, promotion, or watch actions.
  6. Flag where calibration evidence is weak or inconsistent.

Output

  • talent calibration summary
  • per-person talent cards
  • 9-box or equivalent placement rationale
  • succession and retention risk view
  • action plan with owners and timing

Definition of done

  • Each recommendation ties back to evidence.
  • Critical talent risks and succession gaps are visible.
  • The output is usable in an actual calibration session.

Examples

  • "Prepare a talent review pack for our engineering and operations managers."
  • "Calibrate promotion readiness for these six employees and identify development actions."

Quality Criteria

  • Feedback is specific and references exact locations in the reviewed material
  • Each critique includes a concrete improvement suggestion
  • Severity is categorized (critical / important / nice-to-have)
  • Positive aspects are acknowledged alongside areas for improvement

Verification (4C)

CheckQuestion
CorrectnessIs the feedback technically accurate and properly contextualized?
CompletenessWere all major sections of the reviewed material addressed?
Context-fitIs the review granularity appropriate for the material's maturity level?
ConsequenceIf the author implemented all feedback literally, what could go wrong?

Edge Cases

  • Material is too early-stage for detailed review — Provide structural feedback only. Note that content review is deferred until it matures.
  • Reviewer lacks domain expertise — Focus on structure, clarity, and consistency. Flag domain-specific claims as 'Needs SME verification'.
  • Author is defensive or resistant to feedback — Lead with what works well. Frame changes as questions rather than mandates.

Changelog

  • v1.0.0 — Initial release

Signals

GitHub stars
25
Forks
13
Last commit
Oct 2026
Advanced
Item type
skill
Key
codexkit-talent-review-calibrator
Source
github.com/hoavdc/codexkit