Reduction in Force Review

Reduction in Force Review is a skill for Claude Code, Codex from zgbrenner/agentcounsel. It costs 53 tokens per session (3,716 once invoked), scanned A, original, MIT.

A legal-review checklist for planning a group layoff, also called a reduction in force (RIF). It organizes the business reason, selection process, severance, notices, releases, and employee communications for an attorney.

In plain words
What is it for?
Preparing a layoff or restructuring for legal review, including selection criteria, affected employees, severance plans, notice duties, release documents, and communications.
Why use it?
It helps employers and HR teams gather the facts counsel needs before decisions are final. It also highlights questions about unequal effects on groups of employees and required paperwork without deciding the legal outcome.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Preparing a layoff or restructuring for legal review, including selection criteria, affected employees, severance plans, notice duties, release documents, and communications.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zgbrenner/agentcounsel/reduction-in-force-review
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add zgbrenner/agentcounsel --skill reduction-in-force-review
Clone the repo
git clone --depth 1 https://github.com/zgbrenner/agentcounsel

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for Reduction in Force Review

README.md
[![agentmods](https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/reduction-in-force-review/github.svg)](https://agentmods.dev/skills/zgbrenner/agentcounsel/reduction-in-force-review)
Your own site
<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/reduction-in-force-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/reduction-in-force-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for Reduction in Force Review

Your own site · 80×15
<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/reduction-in-force-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/reduction-in-force-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,716 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00053 $0.03716
Opus 5 $0.00026 $0.01858
Sonnet 5 $0.00011 $0.00743
Haiku 4.5 $0.00005 $0.00372

Measured 12d ago against content hash 8c09436b1d56, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

Reduction in Force Review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/employment/reduction-in-force-review/SKILL.md · 155 lines

How it starts

The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reduction in Force Review

Purpose

Produce a structured, attorney-ready organization of a proposed group termination or reduction in force (RIF) before it proceeds. The skill documents the business rationale, the decisional unit and selection process as described, and the proposed severance and communication plan; frames adverse-impact and comparator questions for counsel; and flags group-notice and group-release formalities as verification items. It never computes or concludes on disparate impact, never asserts a notice threshold, headcount trigger, or notice period, and never computes a date. This is draft legal work product for attorney review — not legal advice.

Use When

  • An employer, HR team, or in-house counsel is planning a layoff, restructuring, site closure, or other group termination and wants the legal review points organized before decisions are finalized.
  • Counsel needs a structured intake of a proposed RIF — rationale, decisional unit, criteria, selection list status, severance plan, and timeline — to begin their own risk assessment.
  • A user says "we're planning a RIF — what should legal be looking at?" or "help me organize this layoff for outside counsel."
  • The selection process or criteria are still being designed and the team wants the adverse-impact and consistency questions surfaced early.
  • A group severance program is being prepared and the group-release documentation items need to be inventoried.

Required Inputs

  • Business rationale: the employer's stated reason for the reduction (cost, restructuring, site closure, product discontinuation, or other), recorded precisely as stated.
  • Decisional unit(s) and selection process as described: the organizational scope within which selections are or will be made, who decides, who reviews, and the sequence of the process.
  • Selection criteria as described: the stated criteria (role elimination, skills, performance, tenure, or other) and whether they are objective, subjective, or mixed.
  • Selection list status: whether a proposed list exists; if provided, the affected roles, levels, and work locations as stated. Use anonymized identifiers — do not place employee names into reusable work product.
  • Proposed severance, benefits, and release terms, if any: the proposed structure, tiers, and any draft release documents referenced.

Read the full file on GitHub · 155 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 155 lines · 53 tokens per session scan A 8c09436b1d56

Subscribe to this mod's changes

Reduction in Force Review is a skill published in the GitHub repository zgbrenner/agentcounsel (19 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 3,716 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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