Skip to content
cccskills
FreePublished on GitHub

codexkit-talent-review-calibrator

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.

The English summary is being prepared. Showing the original description.

See how to install

Files included (2)

  • SKILL.md3.2 KB
  • agents/openai.yaml202 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

Talent Review Calibrator

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

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Design rigorous A/B test plans with hypothesis, sample size calculation, Minimum Detectable Effect (MDE), randomization strategy, and decision rules. Includes guardrail metrics and rollout playbook. Use when planning product experiments, conversion optimization, or data-driven feature decisions.

The English summary is being prepared. Showing the original description.

hoavdc/CodexKit25Updated Oct 11, 2026

Review REST and GraphQL API designs for consistency, usability, and best practices. Covers naming conventions, versioning strategy, error format, pagination, authentication patterns, and breaking change detection. Use when reviewing API specs, designing new APIs, or auditing existing endpoints.

The English summary is being prepared. Showing the original description.

hoavdc/CodexKit25Updated Oct 11, 2026

Write Architecture Decision Records (ADRs) following the Michael Nygard format. Captures context, options considered, decision rationale, and consequences. Use when making technology choices, framework selections, or any architectural decision that future developers need to understand.

The English summary is being prepared. Showing the original description.

hoavdc/CodexKit25Updated Oct 11, 2026

Assess organizational readiness for financial audits (internal or external). Map assertions to account balances, check evidence completeness, score readiness using a Red/Amber/Green framework, and generate a remediation timeline. Aligned with SOX, IFRS, and GAAP audit standards. Use before scheduled audits or when preparing for first-time compliance.

The English summary is being prepared. Showing the original description.

hoavdc/CodexKit25Updated Oct 11, 2026

Design safe recurring Codex automations with clear prompts, outputs, schedules, and gating rules.

The English summary is being prepared. Showing the original description.

hoavdc/CodexKit25Updated Oct 11, 2026

Refine Product Backlog Items to meet INVEST criteria. Write User Stories with Acceptance Criteria in Given/When/Then format, estimate with Story Points, and flag dependencies. Use before sprint planning when backlog items need grooming. Do not use to prioritize the backlog — that is the Product Owner's decision.

The English summary is being prepared. Showing the original description.

hoavdc/CodexKit25Updated Oct 11, 2026

See all skills by hoavdc

Report a problem with this skill